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p_top="65"][vc_column][wbc_heading tag="h3" heading_style="heading-3" align="center" wbc_animation="slideInDown" title="|GEN AI APPS| ON RISE?" font_size="35" m_bottom="0"][wbc_heading tag="h3" heading_style="heading-3" align="center" wbc_animation="slideInDown" title="AI AGENTS, AI WORKFLOWS IN |BUSINESS|?" font_size="35" m_bottom="0"][wbc_heading tag="h3" heading_style="heading-3" align="center" title="|WORRIED| ABOUT SECURITY? " font_size="35" m_bottom="0"][wbc_hr height="5" width="85" bg_color="#ff6632"][vc_column_text] Left Unguarded, security risks around GenAI would lead to serious breaches, Enterprise fallouts. Organizations ride momentum to GenAI, for what is yet largest security risk. Adversarial attacks, LLM & Model Vulnerabilities Data Privacy violations, Copyright legal exposures, Sensitive information disclosures are only few among them. [/vc_column_text][vc_row_inner][vc_column_inner width="1/4"][wbc_icon_box display_type="img" box_style="center" wbc_animation="zoomInUp" heading="ADVERSARIAL ATTACKS" icon_size="65" icon_bg_color="#ffffff" icon_bg_color_hover="#ffffff" icon_img="1949" icon_color_hover="#ffffff" icon_border_color_hover="#ffffff" icon_outline_color="#ffffff" icon_outline_color_hover="#fd9528" icon_outline_spacing="8" icon_color="#ffffff" heading_size="25"]Attacks on AI Integrity, Data Manipulation, Poisoning, Evasion, Feature corruption attacks [/wbc_icon_box][/vc_column_inner][vc_column_inner width="1/4"][wbc_icon_box display_type="img" box_style="center" wbc_animation="zoomInUp" heading="PRIVACY, SENSITIVE INFORMATION, TRUST" icon_size="65" icon_bg_color="#fd9528" icon_color_hover="#ffffff" icon_color="#ffffff" icon_bg_color_hover="#ffffff" icon_img="1947" icon_border_color_hover="#ffffff" icon_outline_color_hover="#fd9528" icon_outline_spacing="8" icon_outline_color="#ffffff" heading_size="25"]Data privacy, Sensitive content, Copyright & Legal, InSecure LLM Tokenizers, Rogue manipulations, Prompt attacks.[/wbc_icon_box][/vc_column_inner][vc_column_inner width="1/4"][wbc_icon_box display_type="img" box_style="center" wbc_animation="zoomInUp" heading="LLM & MODEL VULNERABILITIES" icon_size="65" icon_bg_color="#ffffff" icon_color_hover="#ffffff" icon_color="#ffffff" icon_bg_color_hover="#ffffff" icon_img="1945" icon_outline_color_hover="#fd9528" icon_outline_spacing="8" icon_outline_color="#ffffff" heading_size="25"]Automated Vulnerabilities Scan, GenAI & AI Attack surface management, Recommendations, AI Security posture [/wbc_icon_box][/vc_column_inner][vc_column_inner width="1/4"][wbc_icon_box display_type="img" box_style="center" wbc_animation="zoomInUp" heading="INTEGRITY, GOVERNANCE, COMPLAINCE" icon_size="65" icon_bg_color="#fd9528" icon_color_hover="#ffffff" icon_color="#ffffff" icon_bg_color_hover="#ffffff" icon_img="1948" icon_border_color_hover="#ffffff" icon_outline_color_hover="#fd9528" icon_outline_spacing="8" icon_outline_color="#ffffff" heading_size="25"]Spills, Leaks, Contaminations, Inference, Training time attacks, Environmental, Transboundary pollutions.  [/wbc_icon_box][/vc_column_inner][/vc_row_inner][vc_empty_space height="20px"][vc_empty_space][vc_empty_space height="50px"][/vc_column][/vc_row][vc_row row_type="full_width" type="container" bg_select="bg_color_section" p_top="60" p_bottom="50" border_color="#ebebeb" anchor="About" bg_color="#ffffff"][vc_column][wbc_heading tag="h3" heading_style="heading-3" align="center" title="WE HAVE A |SOLUTION|" font_size="35" m_bottom="0"][wbc_heading tag="h1" heading_style="heading-3" align="center" title="|SECURITY| FOR GEN AI APPS, AI AGENTS, WORKFLOWS" font_size="35" m_bottom="0"][wbc_heading tag="h3" heading_style="heading-3" align="center" title="END-TO-END, |INTEROPERABLE|" font_size="35" m_bottom="0"][wbc_hr height="5" width="85" bg_color="#ff6632"][wbc_heading tag="div" align="center" title="Designed for the enterprise. |Enhance, Optimize, Manage| security of Generative AI applications and workflows" font_size="25" m_bottom="37" max_width="750" m_left="auto" m_right="auto"][/vc_column][vc_column width="1/2"][vc_empty_space height="160px"][vc_tta_pageable no_fill_content_area="true" autoplay="20" active_section="1" pagination_style="outline-square" css_animation="none" css=".vc_custom_1726183319861{background-color: #f9f9f9 !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;border-radius: 5px !important;}"][vc_tta_section title="Section 1" tab_id="1722657998050-4040a871-ec3d"][video_player_for_wpbakery video="1784" controls="" autoplay="autoplay" muted="muted" loop="loop"][/vc_tta_section][vc_tta_section title="Section 3" tab_id="1722707112966-0a941959-ec34"][video_player_for_wpbakery video="1788" controls="" autoplay="autoplay" muted="muted" loop="loop"][/vc_tta_section][vc_tta_section title="Section 3" tab_id="1725135056120-87e686b5-47d7"][video_player_for_wpbakery video="1776" controls="" autoplay="autoplay" muted="muted" loop="loop"][/vc_tta_section][/vc_tta_pageable][/vc_column][vc_column width="1/2"][wbc_heading tag="h4" heading_style="heading-3" title="UNCOVER SECURITY BLIND SPOTS" font_size="25" m_bottom="0"][vc_empty_space height="20px"][wbc_heading tag="h3" heading_style="heading-1" title="AI ENVIRONMENTS ARE COMPLEX, VULNERABLE, MULTI-PRONGED" font_size="35" m_bottom="0"][wbc_hr height="5" width="85" bg_color="#ff6632" m_left="0"][wbc_heading tag="div" title="Generative AI is the new IT Perimeter. Data science is new Security Realm." font_size="25" m_bottom="15" max_width="750" m_left="auto" m_right="auto" m_top="auto"][wbc_button button_text="TURN COMPLEXITY INTO CLARITY" align_button="left" font_size="25" hover_bg_color="#fbfbfb" hover_border_color="#fbfbfb" hover_color="#0058f2" color="#000000" bg_color="#ffffff" padding_left="0" el_class="scroll-button" link="url:%231722707112966-0a941959-ec34" padding_top="0" padding_bottom="0" margin_top="0" margin_bottom="0"][vc_column_text]Discover, track, alert on insecure access, unusual usage of AI assets. Trace back to single point-of-origin with AI lineage. 360 view command, control, reconnaissance, lateral movements.x[/vc_column_text][wbc_button button_text="ADVERSARIAL LLM & ML THREAT DETECTION" align_button="left" font_size="25" hover_bg_color="#ffffff" hover_border_color="#ffffff" hover_color="#0058f2" color="#000000" bg_color="#ffffff" padding_left="0" link="url:%231725135056120-87e686b5-47d7" el_class="scroll-button" padding_top="0" padding_bottom="0" padding_right="auto" margin_top="0" margin_bottom="0" margin_left="0" margin_right="0"][vc_column_text]Detect Adversarial threats on LLMs, Models, poison, evasion, exfiltration, infiltration, feature corruption attacks using IOC, IOA's, threat intelligence. Detect malicious injected exploitable deltas.[/vc_column_text][wbc_button button_text="LLM & MODEL VULNERABILITIES MANAGEMENT" align_button="left" font_size="25" hover_bg_color="#fbfbfb" hover_border_color="#fbfbfb" hover_color="#0058f2" color="#000000" bg_color="#ffffff" padding_left="0" el_class="scroll-button" link="url:%231722657998050-4040a871-ec3d" padding_top="0" padding_bottom="0" margin_top="0" margin_bottom="0"][vc_column_text]Automated LLM and model Vulnerability scan. Domain-specific integration. Recommendations, Reviews,Issues, Model, LLM, Prompt, RAG Vulnerability database.[/vc_column_text][/vc_column][/vc_row][vc_row row_type="full_width" type="container" bg_select="bg_color_section" p_top="60" p_bottom="50" border_color="#ebebeb" anchor="mobile-app"][vc_column width="1/2"][wbc_heading tag="h4" heading_style="heading-3" title="SECURE WAY TO USE AI FOR BUSINESS" font_size="25" m_bottom="0"][wbc_heading tag="h3" heading_style="heading-1" title="STOP RISKS THAT STEAL INTELLIGENCE AND DERAIL OPERATIONS" font_size="35" m_bottom="0"][wbc_hr height="5" width="85" bg_color="#ff6632" m_left="0"][wbc_heading tag="div" title="Generative AI is New Attack Vector endangering Enterprises. Elevate Security for high-value use cases. Ensure the reliability and trustworthiness of LLMs." font_size="22" m_bottom="5" max_width="750" m_left="auto" m_right="auto" p_top="0" p_bottom="0"][wbc_button button_text="DETECT ROGUE MODELS, RISKY PIPELINES," align_button="left" font_size="25" hover_bg_color="#fbfbfb" hover_border_color="#fbfbfb" hover_color="#0058f2" color="#000000" bg_color="#ffffff" padding_left="0" link="url:%231725134549782-2a2097b8-eb5c" el_class="scroll-button" padding_top="10" padding_bottom="0"][wbc_button button_text="HARMFUL PROMPTS" align_button="left" font_size="25" hover_bg_color="#fbfbfb" hover_border_color="#fbfbfb" hover_color="#0058f2" color="#000000" bg_color="#ffffff" padding_left="0" link="url:%231725134549782-2a2097b8-eb5c" el_class="scroll-button" padding_top="10" padding_bottom="0"][vc_column_text]Training, Evaluation, Inference analytics, Log anomaly detection, Metric anomaly detection, Model behavior analytics, Prompt usage analytics, detect corrupt outputs. Severity, Explainability, Compliance scores. Recommendations, Reviews.[/vc_column_text][wbc_button button_text="ZERO-TRUST LLMs, ENSURE INTEGRITY," align_button="left" font_size="25" hover_bg_color="#ffffff" hover_border_color="#ffffff" hover_color="#0058f2" color="#000000" bg_color="#ffffff" padding_left="0" link="url:%231725134421148-24abbde7-41d3" el_class="scroll-button" padding_top="0" padding_bottom="0" margin_top="0" margin_bottom="0"][wbc_button button_text="RELIABILITY OF LLM's" align_button="left" font_size="25" hover_bg_color="#ffffff" hover_border_color="#ffffff" hover_color="#0058f2" color="#000000" bg_color="#ffffff" padding_left="0" link="url:%231725134421148-24abbde7-41d3" el_class="scroll-button" padding_top="0" padding_bottom="0" margin_top="0" margin_bottom="0"][vc_column_text]Use domain-specific guardrails. Audit upstream dependency pipelines. Integrity verifications at runtime. Detect tokenizer manipulations in LLMs. Monitor Tokenizer for files any supply chain attacks.[/vc_column_text][wbc_button button_text="SECURE ACCESS TO AI RESOURCES IN AI ENVIRONMENTS" align_button="left" font_size="25" hover_bg_color="#fbfbfb" hover_border_color="#fbfbfb" hover_color="#0058f2" color="#000000" bg_color="#ffffff" padding_left="0" link="url:%231724502197959-c60efa6e-15ac" el_class="scroll-button" padding_top="0" padding_bottom="0" margin_top="0" margin_bottom="0"][vc_column_text]Ensure security controls to LLM’s ready for enterprise infrastructure. Assign the AI service roles on the AI resource's to Managed identities. SPOT and STOP Attacks your AI compute, gpu, ext,int traffic, denial attacks.[/vc_column_text][/vc_column][vc_column width="1/2"][vc_empty_space height="256px"][vc_tta_pageable no_fill_content_area="true" autoplay="20" active_section="1" pagination_style="outline-square" css_animation="none"][vc_tta_section title="Section 1" tab_id="1724502197959-c60efa6e-15ac"][video_player_for_wpbakery video="1783" controls="" autoplay="autoplay" muted="muted" loop="loop"][/vc_tta_section][vc_tta_section title="Section 1" tab_id="1725134421148-24abbde7-41d3"][video_player_for_wpbakery video="1779" controls="" autoplay="autoplay" muted="muted" loop="loop"][/vc_tta_section][vc_tta_section title="Section 1" tab_id="1725134549782-2a2097b8-eb5c"][video_player_for_wpbakery video="1780" controls="" autoplay="autoplay" muted="muted" loop="loop"][/vc_tta_section][/vc_tta_pageable][/vc_column][/vc_row][vc_row row_type="full_width" type="container" bg_select="bg_color_section" p_top="55" p_bottom="0" border_color="#ebebeb" anchor="mobile-app" bg_color="#ffffff" m_top="0" m_bottom="0"][vc_column width="1/2" p_bottom="0" m_bottom="0"][vc_empty_space height="256px"][vc_tta_pageable no_fill_content_area="true" autoplay="20" active_section="1" pagination_style="outline-square" css_animation="none" css=".vc_custom_1726183045693{background-color: #f9f9f9 !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;border-radius: 5px !important;}"][vc_tta_section title="Section 3" tab_id="1724550207880-ed0d0701-3ed0"][video_player_for_wpbakery video="1782" controls="" autoplay="autoplay" muted="muted" loop="loop"][/vc_tta_section][vc_tta_section title="Section 3" tab_id="1725133869370-879a4bc8-e560"][video_player_for_wpbakery video="1781" controls="" autoplay="autoplay" muted="muted" loop="loop"][/vc_tta_section][vc_tta_section title="Section 3" tab_id="1725133925576-7a18b8c2-4bdb"][video_player_for_wpbakery video="1787" controls="" autoplay="autoplay" muted="muted" loop="loop"][/vc_tta_section][/vc_tta_pageable][/vc_column][vc_column width="1/2"][wbc_heading tag="h4" heading_style="heading-3" title="SENSITIVE, COPYRIGHT LEGAL, PRIVACY" font_size="25" m_bottom="0"][wbc_heading tag="h3" heading_style="heading-1" title="ENHANCE PRIVACY WITH DOMAIN SPECIFIC GUARDRAILS" font_size="35" m_bottom="0"][wbc_hr height="5" width="85" bg_color="#ff6632" m_left="0"][wbc_heading tag="div" title="Generative AI opens up all kinds of opportunities to obtain sensitive data. Generative AI pose the greatest risk yet with a variety of concerns around." font_size="25" m_bottom="37" max_width="750" m_left="auto" m_right="auto"][wbc_button button_text="IDENTIFY AND OBFUSCATE SENSITIVE INFORMATION" align_button="left" font_size="25" hover_bg_color="#fbfbfb" hover_border_color="#fbfbfb" hover_color="#0058f2" color="#000000" bg_color="#ffffff" padding_left="0" link="url:%231725133869370-879a4bc8-e560" el_class="scroll-button"][vc_column_text]Detect, Redact, Alert Sensitive information disclosures, Data privacy violations, PII, PHI, Copyright Legal exposures in all Generative AI applications in environment.[/vc_column_text][wbc_button button_text="INTEGRATION WITH TOP GENERATIVE AI PLATFORMS" align_button="left" font_size="25" hover_bg_color="#ffffff" hover_border_color="#ffffff" hover_color="#0058f2" color="#000000" bg_color="#ffffff" padding_left="0" link="url:%231724550207880-ed0d0701-3ed0" el_class="scroll-button"][vc_column_text]Interoperable with your GenAI stack integrations with top providers, platforms, tools.[/vc_column_text][wbc_button button_text="AI FORENSICS, GOVERNANCE, COMPLIANCE" align_button="left" font_size="25" hover_bg_color="#fbfbfb" hover_border_color="#fbfbfb" hover_color="#0058f2" color="#000000" bg_color="#ffffff" padding_left="0" link="url:%231724550207880-ed0d0701-3ed0" el_class="scroll-button"][vc_column_text]Enriched ADR (AI Detection & Response) events with Alert data and forward to SIEM.[/vc_column_text][/vc_column][/vc_row][vc_row row_type="full_width" type="container" bg_select="bg_color_section" p_top="60" p_bottom="35" border_color="#ebebeb" anchor="services"][vc_column][wbc_heading tag="h4" heading_style="heading-3" align="center" wbc_animation="slideInDown" title="DESIGNED FOR ENTERPRISE" font_size="30" m_bottom="0"][wbc_heading tag="h3" heading_style="heading-3" align="center" title="ALERT AI |#1 GEN AI SECURITY PLATFORM OF CHOICE|" font_size="40" m_bottom="0"][wbc_hr height="5" width="85" bg_color="#ff6632"][wbc_heading tag="div" align="center" title="With over 100+ integrations and 1000+ detections, domain-specific security guardrails, easy-to-deploy and manage security platform seamlessly integrates AI workflows and applications." font_size="25" m_bottom="43" max_width="750" m_left="auto" m_right="auto"][/vc_column][vc_column width="1/3"][wbc_icon_box display_type="img" box_style="center" icon_style="square" icon_extra="outline" wbc_animation="zoomIn" heading="DISCOVERY" icon_size="70" box_link="url:https%3A%2F%2Falertai.com%2Fgenerative-ai-security-llm-security-services" icon_img="1948" icon_outline_color_hover="#ffffff" icon_outline_spacing="8" icon_color_hover="#ffffff" icon_border_color_hover="#ffffff" icon_bg_color="#ffffff" icon_bg_color_hover="#ffffff" heading_size="25"] Discovery Alerts AI assets, AI Inventory, Catalog, Models, LLM's, Training, Inference Pipelines, Prompts, Cluster resources, Compute, Networks    [/wbc_icon_box][wbc_icon_box display_type="img" box_style="center" wbc_animation="zoomIn" heading="LLM & ML PIPELINE ANALYTICS" icon_size="70" icon_img="1952" icon_outline_spacing="8" box_link="url:https%3A%2F%2Falertai.com%2Fgenerative-ai-security-llm-security-services%23ThreatDetection" heading_size="25"]Pipeline Alerts LLM and ML Pipelines Training, Evaluation, Inference Metrics, Recommendations, Data skew detection, Spills, leaks, Rogue pipelines, Run, Usage Alerts.        [/wbc_icon_box][wbc_icon_box display_type="img" box_style="center" wbc_animation="zoomIn" heading="PRIVACY, SENSITIVE INFORMATION" icon_size="70" icon_img="1962" icon_outline_spacing="8" box_link="url:https%3A%2F%2Falertai.com%2Fgenerative-ai-security-llm-security-services%23PrivacySensitiveContent" icon_color_hover="#ee7125" heading_size="25"] Data Privacy Alerts Detection, Redaction and PII, PHI Obfuscation, Data privacy in Prompt response queries, embeddings, Copyright and Legal exposures, Removal requests, Suppression list entries, Sensitive content filters    [/wbc_icon_box][/vc_column][vc_column width="1/3" content_align="text-center"][wbc_icon_box display_type="img" box_style="center" wbc_animation="zoomIn" heading="TRACKING ANALYTICS" icon_size="70" box_link="url:https%3A%2F%2Falertai.com%2Fgenerative-ai-security-llm-security-services%23DiscoveryTrackingLineage|title:Alert%20AI%20security%20integration" icon_img="1950" icon_outline_spacing="8" heading_size="25"] Tracking Alerts Experiments, Jobs, Runs, Datasets, Models, Versions, Artifacts, Parameters,Metrics, Predictions, LLM's Interactions, Prompts, Tokenizers  [/wbc_icon_box][wbc_icon_box display_type="img" box_style="center" wbc_animation="zoomIn" heading="LLM & MODEL VULNERABILITIES" icon_size="60" box_link="url:https%3A%2F%2Falertai.com%2Fgenerative-ai-security-llm-security-services%23Vulnerabilities|title:Pipeline%20Detection" icon_img="1956" icon_outline_spacing="8" heading_size="25"]Vulnerability scan Alerts LLM and Model vulnerabilities, Prompt Injection, Perturbations, Information Exposures, Hallucination, Misinformation, categorization, recommendations.      [/wbc_icon_box][wbc_icon_box display_type="img" box_style="center" wbc_animation="zoomIn" heading="ADVERSARIAL THREAT DETECTION" icon_size="70" icon_img="1978" icon_outline_spacing="8" box_link="url:https%3A%2F%2Falertai.com%2Fgenerative-ai-security-llm-security-services%23ThreatDetection" heading_size="25"] Indicators, Threat Data, Alerts Security models for Adversarial ML & LLM attacks, Indicators of Attack, Indicators of Compromise, Threat modelling, Feature extraction,Metrics, Events, Logs, Trace data, Anomaly detection Alerts.  [/wbc_icon_box][/vc_column][vc_column width="1/3" content_align="text-center"][wbc_icon_box display_type="img" box_style="center" wbc_animation="zoomIn" heading="AI LINEAGE" icon_size="70" icon_img="1955" icon_outline_spacing="8" box_link="url:https%3A%2F%2Falertai.com%2Fgenerative-ai-security-llm-security-services%23DiscoveryTrackingLineage" heading_size="25"]Data Lineage Alerts Identify Data sources, Data types, Versions, Map, Topology of Data origin and their Lineage, Detect data contamination attacks, environmental risks in LLM & ML, training copyright, classified data[/wbc_icon_box][wbc_icon_box display_type="img" box_style="center" wbc_animation="zoomIn" heading="PROMPT SECURITY & INTEGRITY" icon_size="70" icon_img="1980" icon_outline_spacing="8" box_link="url:https%3A%2F%2Falertai.com%2Fgenerative-ai-security-llm-security-services%23PrivacySensitiveContent" heading_size="25"] Prompt usage Alerts Prompt injections, Embedding operations, Response Alerts, Token utilization, Model Utilization, Token transaction Alerts, Secure LLM Tokenizer, Application Integrity, Insecure prompts, RAG, fine-tuning Alerts [/wbc_icon_box][wbc_icon_box display_type="img" box_style="center" wbc_animation="zoomIn" heading="AI FORENSICS" icon_size="70" icon_img="1961" icon_outline_spacing="8" 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A lot of the trends in the world are accelerating the movement to what we do. Customers are in AI. Now they are exploring Generative AI in Business. They want enhance, optimize, manage security and integrity of their AI applications. They want to protect models, intelligence, privacy — all of the stuff we are doing. I feel like we’re in the bullseye of where the world’s going``. | - Srini Mommileti CEO, ALERT AI, Ex Palo Alto Networks, Ex Gigamon|" font_size="25" color="#000000" p_top="40" wbc_color="#919191"][vc_btn title="JOIN OUR DEMO" color="danger" align="center" css_animation="slideInDown" css=".vc_custom_1725137639560{margin-top: 20px !important;margin-bottom: 80px !important;margin-left: -100px !important;}" link="url:%23contact"][/vc_column][/vc_row][vc_row row_type="full_width" type="full_screen" p_top="70" p_bottom="130" bg_color="#ffffff"][vc_column wbc_animation="slideInDown"][wbc_heading tag="h4" heading_style="heading-3" align="center" wbc_animation="slideInDown" title="ABOVE AND BEYOND" font_size="14" m_bottom="0" color="#ffffff"][wbc_heading tag="h4" heading_style="heading-3" align="center" title="ABOVE AND BEYOND" font_size="25" m_bottom="0" color="#000000" wbc_color="#ff6632"][wbc_heading tag="h3" heading_style="heading-3" align="center" title="|OUR| MILESTONES" font_size="40" m_bottom="0" color="#000000" wbc_color="#ff6632"][wbc_hr height="5" width="85" bg_color="#ff6632"][wbc_heading tag="div" align="center" title="We are at intersection of AI and Cyber Warfare. 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class="attachment-large size-large wp-post-image" alt="LLM vulnerabilities Model vulnerabilities" decoding="async" srcset="https://alertai.com/wp-content/uploads/2024/08/iStock-bright-orange-1024x576.jpg 1024w, https://alertai.com/wp-content/uploads/2024/08/iStock-bright-orange-300x169.jpg 300w, https://alertai.com/wp-content/uploads/2024/08/iStock-bright-orange-768x432.jpg 768w, https://alertai.com/wp-content/uploads/2024/08/iStock-bright-orange-1536x864.jpg 1536w, https://alertai.com/wp-content/uploads/2024/08/iStock-bright-orange-2048x1152.jpg 2048w, https://alertai.com/wp-content/uploads/2024/08/iStock-bright-orange-1140x641.jpg 1140w, https://alertai.com/wp-content/uploads/2024/08/iStock-bright-orange-848x477.jpg 848w, https://alertai.com/wp-content/uploads/2024/08/iStock-bright-orange-320x180.jpg 320w, https://alertai.com/wp-content/uploads/2024/08/iStock-bright-orange-480x270.jpg 480w, https://alertai.com/wp-content/uploads/2024/08/iStock-bright-orange-800x450.jpg 800w" sizes="(max-width: 1024px) 100vw, 1024px" /> <div class="item-link-overlay"></div> <div class="wbc-extra-links"> <a data-fancybox title="LLM vulnerabilities Model vulnerabilities" href="https://alertai.com/wp-content/uploads/2024/08/iStock-bright-orange-scaled.jpg" class="wbc-photo-up"><i class="fa fa-search"></i></a> </div> </div></div> <div class="post-contents"> <header class="post-header"> <h1 class="entry-title">GenAI Security Integration Platform as Service</h1> <div class="entry-meta"> <span class="date"><i class="far fa-calendar-alt"></i> August 28, 2024</span> <span class="user"><i class="fas fa-user"></i> By <a href="https://alertai.com/author/srinitagsecurity-ai/" title="Posts by Security Research, Alert AI" rel="author">Security Research, Alert AI</a></span> <span class="post-in"><i class="fas fa-pencil-alt"></i> In <a href="https://alertai.com/llm-security-generative-ai-security-vulnerabilities-privacy-model-risks/" rel="category tag">Resources</a>, <a href="https://alertai.com/llm-security-generative-ai-security-model-vulnerabilities-privacy-trust-threats/" rel="category tag">Services</a></span> <span class="comments"><i class="fas fa-comments"></i> No Comments</span> </div> </header> <div class="entry-content clearfix"> <div class="wpb-content-wrapper"><div class="lnkdn_buttons"><div class="lnkdn-share-button"> <script type="IN/Share" data-url="https://alertai.com/gen-ai-llm-security-insights-metrics-leading-generative-ai-platforms/" data-counter=""></script> </div><div class="lnkdn-follow-button"> <script type="IN/FollowCompany" data-id="104405749" data-counter="right"></script> </div></div><h2><span style="font-size: 24pt;"><b>Effective Enterprise Architecture (EA) for Generative AI Applications</b></span></h2> <p> </p> <p><span style="font-size: 18pt;">Effective enterprise architecture practices deliver remarkable IT and business benefits.</span></p> <p><span style="font-size: 18pt;">Today’s Enterprise Architectures are driving Organizations AI Transformation.</span></p> <p><span style="font-size: 18pt;">A remarkable pattern of Enterprise Architecture is Architectural layers and Separation of Concerns.</span></p> <p><span style="font-size: 18pt;">When it comes to Generative AI in Business, Enterprise architecture layers are Business, Data, Technology, and Application.</span></p> <p><span style="font-size: 18pt;">And, concerns are of Scalability, Availability, and Security.</span></p> <p> </p> <h2><span style="font-size: 24pt;"><b>Generative AI for Business</b></span></h2> <p><span style="font-size: 18pt;">In the world of Enterprise application architecture, It’s all about connecting any combination cloud-based processes, Cloud services, Applications and Data</span></p> <p><span style="font-size: 18pt;">Enabling Generative AI in Business Applications is enabling development, execution and governance of integration between GenAI applications are Enterprise Applications and Data.</span></p> <p><span style="font-size: 18pt;">Architecture layers looks like:</span></p> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Data and Apps</b></span></li> </ul> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Generative AI Platform Service Providers</b></span></li> </ul> <p><span style="font-size: 18pt;">Amazon Bedrock, Azure OpenAI, Google Vertex AI, Nvidia DGX ..</span></p> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Cloud platform</b></span></li> </ul> <p><span style="font-size: 18pt;"><b> </b> AWS , Azure, GCP</span></p> <p><span style="font-size: 18pt;">The Final layer is Security,</span></p> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Security service for Generative AI applications</b></span></li> </ul> <p><span style="font-size: 18pt;"><b> </b> <b>Alert AI </b>(hosted on choice of their cloud in customer’s account) integrates</span></p> <p><span style="font-size: 18pt;">seamlessly with all three layers plus Domain-specific guardrails).</span></p> <h3><span style="font-size: 24pt;"><b>A match made in Heaven</b></span></h3> <p><span style="font-size: 18pt;">Considering the synergy between <b>Generative AI platforms </b>and<b> Alert AI </b> in the layers of <b>Enterprise Gen AI Stack ..</b></span></p> <p><span style="font-size: 18pt;">Alert AI – “Generative AI Security platform as a Service for Business”, fits into the Remarkable Enterprise Architecture and Effective Enterprise Generative AI Architecture.</span></p> <p><img decoding="async" class="alignnone size-full wp-image-1923" src="https://alertai.com/wp-content/uploads/2024/08/alertai-gen-ai-platfroms.jpg" alt="" width="640" height="857" srcset="https://alertai.com/wp-content/uploads/2024/08/alertai-gen-ai-platfroms.jpg 640w, https://alertai.com/wp-content/uploads/2024/08/alertai-gen-ai-platfroms-224x300.jpg 224w, https://alertai.com/wp-content/uploads/2024/08/alertai-gen-ai-platfroms-320x429.jpg 320w, https://alertai.com/wp-content/uploads/2024/08/alertai-gen-ai-platfroms-480x643.jpg 480w" sizes="(max-width: 640px) 100vw, 640px" /></p> <p><img decoding="async" class="alignnone size-full wp-image-1922" src="https://alertai.com/wp-content/uploads/2024/08/alertai-genai-platforms2.jpg" alt="" width="309" height="486" srcset="https://alertai.com/wp-content/uploads/2024/08/alertai-genai-platforms2.jpg 309w, https://alertai.com/wp-content/uploads/2024/08/alertai-genai-platforms2-191x300.jpg 191w" sizes="(max-width: 309px) 100vw, 309px" /></p> <p> </p> <h2><span style="font-size: 24pt;">Strategies for new Risks</span></h2> <p> </p> <p><span style="font-size: 18pt;">Alert AI, at its core, is a breed of new generation security platforms, in the era of AI, catering Enterprise needs, augmenting Defenses.</span></p> <p><span style="font-size: 18pt;">Modern Enterprises Architectures use best of breed models and platforms that best suit the problem. Indeed, they use multiple platforms for Generative AI development.</span></p> <p><span style="font-size: 18pt;">They want a Security for their AI applications whatever the underlying Gen AI cloud platform, they choose to use multiple Gen AI platforms in most cases.</span></p> <p><span style="font-size: 18pt;"><b>Alert AI security services – integrates seamlessly with Gen AI platforms: AWS Bedrock, Azure OpenAI, Google Vertex AI, Nvidia DGX.</b></span></p> <p><span style="font-size: 18pt;">Alert AI domain-specific security guardrails enhance, optimize security for Generative AI applications and workflows in Business</span>.</p> <h2><span style="font-size: 24pt;"><b>Security insights from Application Metrics in Generative AI Landscape</b></span></h2> <p> </p> <p><span style="font-size: 18pt;">Applications typically provide several key metrics to help developers monitor and optimize the performance of generative AI models. These metrics are crucial for understanding efficiency, accuracy, and overall behavior of the models in production. Many of these Metrics can be used for Security features extraction, aggregation, sessionization, feature anomaly detection, security analytics.</span></p> <p><span style="font-size: 18pt;"><strong>Breaking down some of metrics provided by applications built on various platforms.</strong></span></p> <p> </p> <h2><span style="font-size: 24pt;"><b>AWS Bedrock</b></span></h2> <p> </p> <p><span style="font-size: 18pt;">Amazon Web Services introduced AWS Bedrock, a fully managed service that makes it simple for developers to create and scale generative AI applications. It provides access to foundation models from leading AI startups and AWS, enabling the creation of applications for various use cases like text generation, chatbots, image creation, and more. AWS Bedrock eliminates the complexities of managing the infrastructure, allowing developers to focus on fine-tuning models and integrating them into their applications. By offering flexible APIs and integration with other AWS services, Bedrock empowers organizations to harness the power of generative AI at scale, without needing deep expertise in machine learning.</span></p> <p> </p> <p><span style="font-size: 18pt;"><b>Metrics Provided by AWS Bedrock</b></span></p> <p><span style="font-size: 18pt;"><b>Model Drift</b></span></p> <p><span style="font-size: 18pt;">Description</span></p> <p><span style="font-size: 18pt;">Model drift measures the change in model performance over time as the data distribution changes. This metric is vital for the long-term maintenance of the model to ensure it continues to perform well in production.</span></p> <p><span style="font-size: 18pt;">Range</span></p> <p><span style="font-size: 18pt;">Model drift is typically measured as a percentage change in key performance metrics like accuracy or error rate over time. A higher percentage indicates more significant drift, which may require model retraining or updates.</span></p> <p> </p> <p><span style="font-size: 18pt;"><b>Inference Latency</b></span></p> <p><span style="font-size: 18pt;">Description</span></p> <p><span style="font-size: 18pt;">Inference latency measures the time taken for the model to process an input and generate a response. This metric is crucial for applications where response time is critical such as chatbots or real-time image generation.</span></p> <p><span style="font-size: 18pt;">Range</span></p> <p><span style="font-size: 18pt;">Latency is usually measured in milliseconds (ms). Ideal ranges vary depending on the application, but lower latency (e.g) under 100ms) is often preferred for real-time applications.</span></p> <p> </p> <p><span style="font-size: 18pt;"><b>Model Throughput</b></span></p> <p><span style="font-size: 18pt;">Description</span></p> <p><span style="font-size: 18pt;">Throughput refers to the number of inferences a model can handle per second. It’s an essential metric for understanding the scalability of your application and ensuring that it can handle the required load.</span></p> <p><span style="font-size: 18pt;">Range</span></p> <p><span style="font-size: 18pt;">Throughput refers to the number of inferences per second (IPS). Higher values indicate better scalability. Depending on the model complexity, throughput can range from tens to thousands of IPS.</span></p> <p> </p> <p><span style="font-size: 18pt;"><b>Accuracy</b></span></p> <p><span style="font-size: 18pt;">Description</span></p> <p><span style="font-size: 18pt;">Accuracy measures the correctness of the model’s predictions or output. This metric is vital for applications where the quality of the generated content directly impacts the user experience such as text generation or recommendation systems.</span></p> <p> </p> <p><span style="font-size: 18pt;">Range</span></p> <p><span style="font-size: 18pt;">Accuracy is typically expressed as a percentage or a score between 0 and 1. Higher accuracy (closer to 1 or 100%) indicates better performance, but this metric can vary based on the specific task and dataset.</span></p> <p> </p> <p><span style="font-size: 18pt;"><b>Resource Utilization</b></span></p> <p><span style="font-size: 18pt;">Description</span></p> <p><span style="font-size: 18pt;">This metric tracks the computational resources ( CPU, GPU memory) used by the model during the inference process. Monitoring resource utilization helps in optimizing costs and ensuring that the application runs efficiently.</span></p> <p><span style="font-size: 18pt;">Range</span></p> <p><span style="font-size: 18pt;">Resource utilization is measured in percentages, indicating the proportion of available resources being used. Ideally, resource utilization should be optimized to balance performance and cost.</span></p> <p> </p> <p><span style="font-size: 18pt;"><b>Error rate</b></span></p> <p><span style="font-size: 18pt;">Description</span></p> <p><span style="font-size: 18pt;">Error rate indicates the frequency of errors encountered during model inference. This metric is critical for maintaining reliability and identifying potential issues with model performance.</span></p> <p><span style="font-size: 18pt;">Range</span></p> <p><span style="font-size: 18pt;">The error rate is usually expressed as a percentage. Lower error rates (e.g., below 1%) are preferred, indicating more reliable model performance.</span></p> <p> </p> <p><span style="font-size: 18pt;"><b>Cost per Inference</b></span></p> <p><span style="font-size: 18pt;">Description</span></p> <p><span style="font-size: 18pt;">This metric measures the cost associated with each inference performed by the model. It’s essential for budgeting and understanding the financial implications of running AI models at scale.</span></p> <p><span style="font-size: 18pt;">Range</span></p> <p><span style="font-size: 18pt;">The cost per inference is measured in monetary units (e.g., USD). The range varies widely depending on the complexity of the model, the resources used, and the pricing model of AWS services.</span></p> <p><span style="font-size: 18pt;"><b>Response quality</b></span></p> <p><span style="font-size: 18pt;">Description</span></p> <p><span style="font-size: 18pt;">For generative models, response quality metrics evaluate the coherence, relevance, and overall quality of the output generated by the model. This metric is particularly important in applications involving text or image generation.</span></p> <p><span style="font-size: 18pt;">Range</span></p> <p><span style="font-size: 18pt;">Response quality can be measured through various scoring mechanisms, such as BLEU scores for text or FID scores for images, often ranging from 0 to 1 or as absolute scores where higher values indicate better quality.</span></p> <p> </p> <h2><span style="font-size: 18pt;"><b>Google Vertex AI</b></span></h2> <p> </p> <p><span style="font-size: 18pt;">Google Vertex AI is a unified artificial intelligence (AI) platform that enables data scientists and machine learning engineers to build, deploy, and scale machine learning models seamlessly. Vertex AI brings together all the necessary tools for the entire machine learning workflow, including data preparation, model training, tuning, and deployment, under one platform. By integrating with Google Cloud’s ecosystem, Vertex AI simplifies the management of models in production and offers powerful features like AutoML, custom training, hyperparameter tuning, and advanced MLOps capabilities. This platform is designed to reduce complexity and accelerate the development of AI applications, making it accessible for both beginners and experts in machine learning.</span></p> <p> </p> <p><span style="font-size: 18pt;"><b>Metrics Provided by Google Vertex AI</b></span></p> <p><span style="font-size: 18pt;">Google Vertex AI provides a variety of metrics that are essential for monitoring and optimizing machine learning models throughout their lifecycle. Below are some of the key metrics, along with descriptions and typical ranges:</span></p> <h3><span style="font-size: 18pt;"><b>1. Training Time</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Training time measures the total time taken to train a machine learning model. It is a critical metric for understanding the efficiency of the training process and for planning resource allocation.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Training time is typically measured in seconds, minutes, or hours, depending on the size of the dataset and the complexity of the model. It can range from a few minutes for simple models to several hours or days for more complex models.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>2. Training Accuracy</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Training accuracy represents the percentage of correct predictions made by the model on the training dataset. It is a key metric for evaluating how well the model has learned from the training data.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Training accuracy is expressed as a percentage, ranging from 0% to 100%. Higher accuracy indicates that the model is performing well on the training data.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>3. Validation Accuracy</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Validation accuracy measures the model’s performance on a validation dataset, which is separate from the training data. It is used to assess the model’s ability to generalize to new, unseen data.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Like training accuracy, validation accuracy is expressed as a percentage, ranging from 0% to 100%. A high validation accuracy suggests good generalization, while a significant gap between training and validation accuracy may indicate overfitting.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>4. Loss</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Loss is a measure of how far the model’s predictions are from the actual outcomes. It serves as the optimization objective during training, with the goal of minimizing the loss.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Loss is usually measured as a non-negative number, with lower values indicating better model performance. The specific range depends on the loss function used, but it typically ranges from 0 to a small positive value.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>5. Model Inference Latency</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Inference latency measures the time taken for a deployed model to produce predictions on new input data. This metric is crucial for applications requiring quick responses, such as real-time decision-making systems.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Latency is typically measured in milliseconds (ms). Lower latency (e.g., under 100ms) is preferred for real-time applications, but the acceptable range depends on the specific use case.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>6. Model Throughput</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Throughput refers to the number of predictions a model can generate per second. It is an essential metric for understanding the model’s efficiency and scalability in production environments.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Throughput is measured in predictions per second (PPS), and the range can vary from tens to thousands, depending on the model’s complexity and the infrastructure.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>7. CPU/GPU Utilization</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: This metric tracks the percentage of CPU or GPU resources utilized during training or inference. Monitoring resource utilization helps in optimizing costs and ensuring that resources are efficiently used.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Utilization is measured as a percentage, typically ranging from 0% to 100%. Efficient use of resources is essential to balance performance and cost, and high utilization may indicate the need to scale resources.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>8. Memory Utilization</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Memory utilization measures the amount of memory consumed during training or inference. This metric is important to prevent memory bottlenecks that can cause slowdowns or crashes.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Memory utilization is measured in bytes or as a percentage of total available memory. It can range from a few megabytes (MB) to several gigabytes (GB), depending on the model and data.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>9. Hyperparameter Tuning Metrics</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: During hyperparameter tuning, Vertex AI provides metrics such as the best objective metric value, which reflects the optimal performance achieved during the tuning process. These metrics are essential for selecting the best model configuration.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: The range depends on the specific objective metric being optimized, such as accuracy or loss. Accuracy might range from 0% to 100%, while loss values typically range from 0 to a small positive number.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>10. Error Rate</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Error rate measures the frequency of incorrect predictions or failures during inference. This metric is vital for assessing the reliability of a deployed model.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Error rate is expressed as a percentage, with lower values (e.g., below 1%) indicating more reliable model performance.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>11. Cost per Inference</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: This metric measures the cost associated with each inference made by the model. It is important for budgeting and understanding the financial impact of deploying machine learning models at scale.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Cost per inference is measured in monetary units (e.g., USD) and varies depending on the complexity of the model, the resources used, and the pricing structure of Google Cloud services.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>12. Model Drift</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Model drift refers to the change in model performance over time as the data distribution evolves. It’s a key metric for ensuring that the model remains accurate and relevant in production.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Model drift is often measured as a percentage change in key performance metrics such as accuracy or loss over time. Significant drift may indicate the need for model retraining.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>13. AutoML Model Performance</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: For models trained using AutoML in Vertex AI, this metric provides insights into how well the automatically generated models are performing. It includes metrics like accuracy, precision, recall, and F1-score.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: These performance metrics are typically expressed as percentages or scores ranging from 0 to 1. Higher values indicate better model performance, with ranges depending on the specific task (e.g., classification vs. regression).</span></li> </ul> <h3><span style="font-size: 18pt;"><b>14. Pipeline Execution Time</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: For models deployed through Vertex AI Pipelines, this metric tracks the total time taken for a pipeline to execute, from data ingestion to model deployment. It helps in optimizing pipeline efficiency.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Pipeline execution time is measured in minutes or hours, depending on the complexity of the pipeline and the tasks involved.</span></li> </ul> <p> </p> <h2><span style="font-size: 18pt;"><b>Azure OpenAI</b></span></h2> <p> </p> <p><span style="font-size: 18pt;">Azure OpenAI Service is a powerful platform provided by Microsoft Azure that allows developers and data scientists to access and integrate advanced AI models from OpenAI, such as GPT, DALL-E, and Codex, into their applications. The service provides the infrastructure to deploy and scale these models while leveraging Azure’s robust ecosystem for security, compliance, and management. Azure OpenAI enables users to build intelligent applications with capabilities like natural language understanding, content generation, code automation, and more, making it easier to implement AI-driven solutions across various industries.</span></p> <h3><span style="font-size: 18pt;"><b>Metrics Provided by Azure OpenAI Service</b></span></h3> <p><span style="font-size: 18pt;">Azure OpenAI Service provides several key metrics to help users monitor and optimize the performance of AI models during development, deployment, and inference. Below are some of the most important metrics, along with their descriptions and typical ranges:</span></p> <h3><span style="font-size: 18pt;">1. Inference Latency</span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Inference latency measures the time taken by the AI model to process an input and generate an output. This metric is crucial for applications where quick responses are needed, such as chatbots or real-time content generation.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Latency is typically measured in milliseconds (ms). Lower latency (e.g., under 100ms) is ideal for real-time applications, though acceptable ranges vary based on the specific use case.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>2. Request Throughput</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Request throughput refers to the number of requests the AI model can handle per second. This metric is essential for understanding the scalability of the model and ensuring it can handle the expected load in production environments.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Throughput is measured in requests per second (RPS) and can range from tens to thousands depending on the model’s complexity and the infrastructure used.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>3. Token Utilization</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Token utilization tracks the number of tokens processed during inference, including both input and output tokens. This metric is important for cost management, as many AI models, like GPT, are priced based on token usage.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Token count varies depending on the length of the input and the output generated. It can range from a few tokens for short inputs to several hundred tokens for longer interactions.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>4. Success Rate</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: The success rate measures the percentage of successful inferences or API calls made to the model without errors. This metric is crucial for assessing the reliability of the service.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Success rate is expressed as a percentage, typically ranging from 0% to 100%. A higher success rate (closer to 100%) indicates more reliable performance.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>5. Model Accuracy (Task-Specific)</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: For certain applications, such as classification or content generation, model accuracy indicates how well the model’s outputs align with the expected results. This metric varies depending on the task and model configuration.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Accuracy is usually expressed as a percentage, with ranges from 0% to 100%. Higher accuracy indicates better model performance on the given task.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>6. Compute Utilization</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Compute utilization tracks the percentage of CPU or GPU resources used by the model during inference. This metric helps in optimizing resource allocation and managing costs effectively.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Utilization is measured as a percentage, typically ranging from 0% to 100%. High utilization indicates that resources are being fully leveraged, but excessive utilization might necessitate scaling resources.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>7. Memory Utilization</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Memory utilization measures the amount of memory used during model inference. Monitoring this metric is crucial to prevent memory-related bottlenecks and ensure smooth operation.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Memory utilization is measured in bytes or as a percentage of total available memory. Depending on the model and data size, this can range from a few megabytes (MB) to several gigabytes (GB).</span></li> </ul> <h3><span style="font-size: 18pt;"><b>8. Error Rate</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Error rate measures the frequency of errors encountered during API calls or model inferences. This metric is important for identifying issues with model performance or infrastructure.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Error rate is expressed as a percentage, with lower values (e.g., below 1%) indicating more reliable model performance.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>9. Cost per Request</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: This metric tracks the cost associated with each request made to the AI model. It is vital for budgeting and understanding the financial implications of deploying AI models at scale.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Cost per request is measured in monetary units (e.g., USD) and varies depending on the model complexity, the resources used, and the pricing structure of Azure services.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>10. Response Quality</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: For generative models, response quality metrics evaluate the relevance, coherence, and overall quality of the output generated by the model. This is particularly important in applications like content creation or conversational AI.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Response quality can be assessed using various scoring methods, often ranging from 0 to 1 or as qualitative assessments. Higher scores indicate better quality.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>11. Rate Limits and Throttling</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: This metric monitors the frequency of hitting rate limits or experiencing throttling during API calls. Understanding this helps in managing API usage and scaling appropriately.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: This is typically a count of incidents, with a higher count indicating that the current rate limits are being exceeded, requiring adjustments to usage patterns or plan levels.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>12. User Engagement Metrics</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: These metrics measure how end-users interact with the AI models, such as click-through rates, engagement duration, or satisfaction scores. They are essential for understanding the effectiveness of AI-driven features in user-facing applications.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Engagement metrics vary widely based on the application but are often expressed as percentages or absolute counts (e.g., clicks, time spent).</span></li> </ul> <h3><span style="font-size: 18pt;"><b>13. Model Drift</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Model drift tracks changes in model performance over time as the underlying data distribution changes. It’s crucial for maintaining the relevance and accuracy of AI models in production.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Model drift is usually measured as a percentage change in key performance metrics like accuracy or error rate over time. Significant drift may require retraining or updating the model.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>14. Response Time Variability</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: This metric measures the variability in response times for model inferences, helping to identify inconsistencies or potential issues with the service.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Response time variability is often measured as a standard deviation or range of response times in milliseconds (ms). Lower variability indicates more consistent performance.</span></li> </ul> <p> </p> <h3><span style="font-size: 18pt;"><b>NVIDIA DGX</b></span></h3> <p><span style="font-size: 18pt;">NVIDIA DGX Cloud is a high-performance, scalable cloud-based platform designed to accelerate AI and machine learning workloads. Built on NVIDIA’s advanced infrastructure, DGX Cloud provides enterprises with the power of NVIDIA DGX systems combined with the flexibility of the cloud. It is optimized for training large-scale AI models, running complex simulations, and deploying AI applications in production. DGX Cloud integrates seamlessly with other cloud services, allowing organizations to leverage NVIDIA’s cutting-edge GPUs, software stack, and AI frameworks to build, train, and deploy AI models faster and more efficiently.</span></p> <h3><span style="font-size: 18pt;"><strong>Metrics Provided by NVIDIA DGX Cloud</strong></span></h3> <p><span style="font-size: 18pt;">NVIDIA DGX Cloud provides a range of metrics to monitor and optimize the performance of AI and machine learning workloads. These metrics are essential for managing resources, ensuring optimal performance, and controlling costs in large-scale AI projects. Below are some key metrics, along with their descriptions and typical ranges:</span></p> <h3><span style="font-size: 18pt;"><b>1. GPU Utilization</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: GPU utilization measures the percentage of GPU resources used during training or inference. This metric is crucial for understanding how effectively the GPUs are being utilized and whether there’s a need to optimize workloads or scale resources.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: GPU utilization is typically expressed as a percentage, ranging from 0% to 100%. Higher utilization indicates that the GPUs are being fully leveraged, while lower utilization might suggest underutilization or inefficiencies in the workload.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>2. GPU Memory Utilization</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: This metric tracks the amount of GPU memory being used during model training or inference. Monitoring GPU memory utilization helps prevent memory bottlenecks that could slow down or halt processing.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: GPU memory utilization is measured in bytes (e.g., GB) or as a percentage of total available memory. The range can vary significantly depending on the model size and data batch sizes, typically from a few gigabytes to several tens of gigabytes.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>3. Training Time</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Training time measures the total duration required to train a model on DGX Cloud. This metric is essential for evaluating the efficiency of the training process and optimizing resource usage.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Training time is measured in minutes, hours, or even days, depending on the complexity of the model and the size of the dataset. It can range from a few minutes for smaller models to several hours or more for large-scale models.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>4. Inference Latency</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Inference latency refers to the time taken for a model to produce a prediction or result after receiving input data. This metric is critical for applications requiring real-time or low-latency predictions, such as autonomous vehicles or real-time analytics.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Inference latency is typically measured in milliseconds (ms). Lower latency is generally preferred, with acceptable ranges varying depending on the specific application’s requirements.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>5. Model Throughput</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Throughput measures the number of inferences or training iterations that can be processed per second. This metric is important for understanding the scalability and efficiency of AI workloads on DGX Cloud.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Throughput is measured in inferences per second (IPS) or training iterations per second, depending on the workload. The range can vary from tens to thousands, depending on the model and the available resources.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>6. Disk I/O Utilization</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Disk I/O utilization tracks the read and write operations on the storage disks during training or inference. This metric helps in identifying potential bottlenecks in data loading or storage management.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Disk I/O is measured in input/output operations per second (IOPS) or as a percentage of total available bandwidth. Higher values indicate more intensive disk usage, which might require optimization or scaling of storage resources.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>7. Network Utilization</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Network utilization measures the bandwidth used for data transfer between GPUs, storage, and other nodes in a distributed training setup. This metric is vital for optimizing data transfer rates and minimizing bottlenecks in distributed AI workflows.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Network utilization is measured in bits per second (bps) or as a percentage of available network bandwidth. It can range from a few megabits per second (Mbps) to several gigabits per second (Gbps), depending on the workload.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>8. Job Queue Time</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Job queue time measures the time a training or inference job spends waiting in the queue before execution begins. This metric is important for understanding system load and optimizing job scheduling.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Queue time is typically measured in seconds or minutes. Lower queue times indicate more efficient resource allocation, while higher queue times may suggest the need for additional resources or better scheduling practices.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>9. Energy Consumption</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Energy consumption measures the amount of power used by the GPUs and other infrastructure during training or inference. This metric is increasingly important for managing the sustainability and cost-efficiency of AI workloads.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Energy consumption is typically measured in kilowatt-hours (kWh). The range can vary depending on the scale of the workload, with larger models and longer training times consuming more energy.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>10. Cost per Training/Inference</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: This metric tracks the cost associated with each training or inference operation on DGX Cloud. It is essential for budgeting and managing the financial impact of running large-scale AI workloads.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Cost is typically measured in monetary units (e.g., USD). The range varies depending on the model complexity, duration of training, and the resources used, and can range from a few dollars to thousands of dollars for extensive training jobs.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>11. Temperature Monitoring</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Temperature monitoring tracks the operating temperature of GPUs and other hardware components. This metric is crucial for ensuring that the hardware operates within safe temperature ranges, preventing overheating and potential damage.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Temperature is typically measured in degrees Celsius (°C). Optimal operating temperatures usually range from 30°C to 80°C, depending on the specific hardware components and workload intensity.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>12. Model Convergence</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Model convergence measures how quickly a model’s loss function reaches a minimum during training. It is an important metric for assessing the effectiveness of the training process and whether the model is learning as expected.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Convergence is usually assessed by plotting the loss over time or epochs, with the goal being a steady decrease in loss values. The specific range varies depending on the model and training parameters.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>13. Error Rate</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Error rate measures the frequency of errors or failed operations during training or inference. This metric helps in identifying issues with model performance or infrastructure.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Error rate is expressed as a percentage, with lower values indicating more reliable performance. Ideally, the error rate should be as close to 0% as possible.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>14. Job Completion Time</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;">Description: Job completion time tracks the total time taken for a training or inference job to complete from start to finish. This metric is essential for assessing the efficiency and productivity of AI workflows.</span></li> <li aria-level="1"><span style="font-size: 18pt;">Range: Completion time is measured in minutes or hours, depending on the complexity of the job. It can range from a few minutes for small tasks to several hours or more for large-scale jobs.</span></li> </ul> <p> </p> <h3><span style="font-size: 18pt;"><b>AWS SageMaker</b></span></h3> <p> </p> <p><span style="font-size: 18pt;">AWS SageMaker is a fully managed service that provides developers and data scientists with the tools to build, train, and deploy machine learning (ML) models at scale. SageMaker simplifies the entire ML workflow, from data preparation and model training to deployment and monitoring, allowing users to quickly iterate on models and bring them to production. With SageMaker, you can build models using popular ML frameworks, automatically tune hyperparameters, deploy models with a few clicks, and monitor model performance with integrated tools. SageMaker is designed to reduce the complexity and cost of building ML models, making it accessible for both beginners and experts in machine learning.</span></p> <p> </p> <p><span style="font-size: 18pt;"><b>Metrics Provided by AWS SageMaker</b></span></p> <p><span style="font-size: 18pt;">AWS SageMaker offers a wide range of metrics to help users monitor and optimize the performance of their ML models throughout the entire machine learning lifecycle. Below are some of the key metrics, along with their descriptions and typical ranges:</span></p> <p> </p> <h3><span style="font-size: 18pt;"><b>1. Training Time</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Training time measures the total duration taken to train a machine learning model. This metric is crucial for understanding the efficiency of the training process and can be used to optimize resource usage.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Training time is usually measured in seconds or minutes. The range varies depending on the complexity of the model and the size of the dataset, from a few minutes to several hours.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>2. Training Accuracy</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Training accuracy indicates the proportion of correct predictions made by the model on the training dataset. This metric is essential for evaluating how well the model has learned from the training data.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Training accuracy is expressed as a percentage, typically ranging from 0% to 100%. Higher accuracy indicates better performance on the training data.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>3. Validation Accuracy</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Validation accuracy measures the model’s performance on a separate validation dataset that was not used during training. This metric helps assess the model’s ability to generalize to new data.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Like training accuracy, validation accuracy is expressed as a percentage, ranging from 0% to 100%. A significant difference between training and validation accuracy may indicate overfitting.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>4. Loss</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Loss is a measure of how well the model’s predictions match the actual outcomes. It is used as an optimization objective during training, with the goal of minimizing the loss.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Loss is usually measured as a non-negative number, with lower values indicating better model performance. The range depends on the loss function used, but it typically ranges from 0 to a small positive value.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>5. Hyperparameter Tuning Metrics</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: During hyperparameter tuning, SageMaker provides metrics such as the best objective metric value, which reflects the best performance achieved during the tuning process. These metrics are crucial for selecting the optimal model configuration.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: The range depends on the specific objective metric (e.g., accuracy, loss) being optimized. It typically ranges from 0% to 100% for accuracy or from 0 to a small positive value for loss.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>6. Model Inference Latency</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Inference latency measures the time taken for a deployed model to produce predictions on new data. This metric is critical for real-time applications where response time is crucial.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Latency is measured in milliseconds (ms). Lower latency (e.g., under 100ms) is preferred for real-time applications, though the acceptable range depends on the use case.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>7. Model Throughput</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Throughput indicates the number of predictions a deployed model can generate per second. This metric helps assess the model’s scalability and efficiency in production environments.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Throughput is measured in predictions per second (PPS) and can range from tens to thousands depending on the model and infrastructure.</span></li> </ul> <p> </p> <h3><span style="font-size: 18pt;"><b>8. CPU/GPU Utilization</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: These metrics track the percentage of CPU or GPU resources used during training or inference. Monitoring resource utilization helps in optimizing costs and ensuring that resources are efficiently used.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Utilization is measured as a percentage, typically ranging from 0% to 100%. High utilization indicates that resources are being fully leveraged, but excessive utilization may require scaling up resources.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>9. Memory Utilization</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Memory utilization measures the amount of memory used during training or inference. This metric is important for preventing memory bottlenecks that can lead to slowdowns or failures.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Memory utilization is measured in bytes or as a percentage of total available memory. It can range from a few megabytes (MB) to several gigabytes (GB), depending on the model and data.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>10. Error Rate</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Error rate measures the frequency of errors during inference, such as incorrect predictions or failures in processing input data. This metric is vital for maintaining the reliability of deployed models.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Error rate is expressed as a percentage, with lower values (e.g., below 1%) indicating more reliable model performance.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>11. Cost per Inference</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: This metric tracks the cost associated with each inference made by the model. It is essential for budgeting and understanding the financial impact of deploying ML models at scale.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Cost per inference is measured in monetary units (e.g., USD) and varies depending on the model complexity, the resources used, and the pricing model of AWS services.</span></li> </ul> <h3><span style="font-size: 18pt;"><b>12. Model Drift</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt;"><b>Description</b>: Model drift measures changes in model performance over time as the data distribution evolves. It’s an important metric for maintaining model accuracy and ensuring that the model continues to perform well in production.</span></li> <li aria-level="1"><span style="font-size: 18pt;"><b>Range</b>: Model drift is often measured as a percentage change in key performance metrics like accuracy or loss over time. Higher percentages indicate more significant drift, which may require model retraining.</span></li> </ul> <p> </p> <p>[/vc_column_text]<div class="wbc-heading clearfix"><h3 class="special-heading-3" style="font-size:20px;">AWS Bedrock</h3></div> <div class="wpb_raw_code wpb_content_element wpb_raw_html" > <div class="wpb_wrapper"> <table><thead> <tr> <th>Feature</th> <th>Description</th> <th>Range</th> <th>Severity</th> <th>Security feature</th> <th>Aggregation</th> </tr></thead> <tbody> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Model Drift</td> <td>Model drift measures the change in model performance over time as the data distribution changes. This metric is vital for the long-term maintenance of the model to ensure it continues to perform well in production.</td> <td>Model drift is typically measured as a percentage change in key performance metrics like accuracy or error rate over time. A higher percentage indicates more significant drift, which may require model retraining or updates.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Inference Latency</td> <td>Inference latency measures the time taken for the model to process an input and generate a response. This metric is crucial for applications where response time is critical such as chatbots or real-time image generation.</td> <td>Latency is usually measured in milliseconds (ms). Ideal ranges vary depending on the application, but lower latency (e.g) under 100ms) is often preferred for real-time applications.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Model Throughput</td> <td>Throughput refers to the number of inferences a model can handle per second. It's an essential metric for understanding the scalability of your application and ensuring that it can handle the required load.</td> <td>Throughput refers to the number of inferences per second (IPS). Higher values indicate better scalability. Depending on the model complexity, throughput can range from tens to thousands of IPS.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Accuracy</td> <td>Accuracy measures the correctness of the model’s predictions or output. This metric is vital for applications where the quality of the generated content directly impacts the user experience such as text generation or recommendation systems.</td> <td>Accuracy is typically expressed as a percentage or a score between 0 and 1. Higher accuracy (closer to 1 or 100%) indicates better performance, but this metric can vary based on the specific task and dataset.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Resource Utilization</td> <td>This metric tracks the computational resources ( CPU, GPU memory) used by the model during the inference process. Monitoring resource utilization helps in optimizing costs and ensuring that the application runs efficiently.</td> <td>Resource utilization is measured in percentages, indicating the proportion of available resources being used. Ideally, resource utilization should be optimized to balance performance and cost.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Error rate</td> <td>Error rate indicates the frequency of errors encountered during model inference. This metric is critical for maintaining reliability and identifying potential issues with model performance.</td> <td>The error rate is usually expressed as a percentage. Lower error rates (e.g., below 1%) are preferred, indicating more reliable model performance.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Cost per Inference</td> <td>This metric measures the cost associated with each inference performed by the model. It’s essential for budgeting and understanding the financial implications of running AI models at scale.</td> <td>The cost per inference is measured in monetary units (e.g., USD). The range varies widely depending on the complexity of the model, the resources used, and the pricing model of AWS services.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Response quality</td> <td>For generative models, response quality metrics evaluate the coherence, relevance, and overall quality of the output generated by the model. This metric is particularly important in applications involving text or image generation.</td> <td>Response quality can be measured through various scoring mechanisms, such as BLEU scores for text or FID scores for images, often ranging from 0 to 1 or as absolute scores where higher values indicate better quality.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> </tbody></table> </div> </div> <div class="wbc-heading clearfix"><h3 class="special-heading-3" style="font-size:20px;">AWS Sagemaker</h3></div>[/vc_column][/vc_row]<div class="vc_row wpb_row "> <div class="wpb_column vc_column_container vc_col-sm-12 "><div class="vc_column-inner " > <div class="wpb_wrapper"> <div class="wpb_raw_code wpb_content_element wpb_raw_html" > <div class="wpb_wrapper"> <table><thead> <tr> <th>Feature</th> <th>Description</th> <th>Range</th> <th>Severity</th> <th>Security feature</th> <th>Aggregation</th> </tr></thead> <tbody> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Training Time</td> <td>Training time measures the total duration taken to train a machine learning model. This metric is crucial for understanding the efficiency of the training process and can be used to optimize resource usage.</td> <td>Training time is usually measured in seconds or minutes. The range varies depending on the complexity of the model and the size of the dataset, from a few minutes to several hours.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Training Accuracy</td> <td>Training accuracy indicates the proportion of correct predictions made by the model on the training dataset. This metric is essential for evaluating how well the model has learned from the training data.</td> <td>Training accuracy is expressed as a percentage, typically ranging from 0% to 100%. Higher accuracy indicates better performance on the training data.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Validation Accuracy</td> <td>Validation accuracy measures the model's performance on a separate validation dataset that was not used during training. This metric helps assess the model’s ability to generalize to new data.</td> <td>validation accuracy is expressed as a percentage, ranging from 0% to 100%. A significant difference between training and validation accuracy may indicate overfitting.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Loss</td> <td>Loss is a measure of how well the model’s predictions match the actual outcomes. It is used as an optimization objective during training, with the goal of minimizing the loss.</td> <td>Loss is usually measured as a non-negative number, with lower values indicating better model performance. The range depends on the loss function used, but it typically ranges from 0 to a small positive value.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Hyperparameter Tuning Metrics</td> <td>During hyperparameter tuning, SageMaker provides metrics such as the best objective metric value, which reflects the best performance achieved during the tuning process. These metrics are crucial for selecting the optimal model configuration.</td> <td>The range depends on the specific objective metric (e.g., accuracy, loss) being optimized. It typically ranges from 0% to 100% for accuracy or from 0 to a small positive value for loss.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Model Inference Latency</td> <td>During hyperparameter tuning, SageMaker provides metrics such as the best objective metric value, which reflects the best performance achieved during the tuning process. These metrics are crucial for selecting the optimal model configuration.</td> <td>Latency is measured in milliseconds (ms). Lower latency (e.g., under 100ms) is preferred for real-time applications, though the acceptable range depends on the use case.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Model Throughput</td> <td>Throughput indicates the number of predictions a deployed model can generate per second. This metric helps assess the model’s scalability and efficiency in production environments.</td> <td>Throughput is measured in predictions per second (PPS) and can range from tens to thousands depending on the model and infrastructure.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>CPU/GPU Utilization</td> <td>These metrics track the percentage of CPU or GPU resources used during training or inference. Monitoring resource utilization helps in optimizing costs and ensuring that resources are efficiently used.</td> <td>Utilization is measured as a percentage, typically ranging from 0% to 100%. High utilization indicates that resources are being fully leveraged, but excessive utilization may require scaling up resources.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Memory Utilization</td> <td>Memory utilization measures the amount of memory used during training or inference. This metric is important for preventing memory bottlenecks that can lead to slowdowns or failures.</td> <td>Memory utilization is measured in bytes or as a percentage of total available memory. It can range from a few megabytes (MB) to several gigabytes (GB), depending on the model and data.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Error Rate</td> <td>Error rate measures the frequency of errors during inference, such as incorrect predictions or failures in processing input data. This metric is vital for maintaining the reliability of deployed models.</td> <td>Error rate is expressed as a percentage, with lower values (e.g., below 1%) indicating more reliable model performance.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Cost per Inference</td> <td>This metric tracks the cost associated with each inference made by the model. It is essential for budgeting and understanding the financial impact of deploying ML models at scale.</td> <td>Cost per inference is measured in monetary units (e.g., USD) and varies depending on the model complexity, the resources used, and the pricing model of AWS services.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Model Drift</td> <td>Model drift measures changes in model performance over time as the data distribution evolves. It’s an important metric for maintaining model accuracy and ensuring that the model continues to perform well in production.</td> <td>Model drift is often measured as a percentage change in key performance metrics like accuracy or loss over time. Higher percentages indicate more significant drift, which may require model retraining.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> </tbody></table> </div> </div> </div> </div> </div> </div><div class="vc_row wpb_row "> <div class="wpb_column vc_column_container vc_col-sm-12 "><div class="vc_column-inner " > <div class="wpb_wrapper"> <div class="wbc-heading clearfix"><h3 class="special-heading-3" style="font-size:25px;">Google Vertex AI</h3></div> <div class="wpb_raw_code wpb_content_element wpb_raw_html" > <div class="wpb_wrapper"> <table><thead> <tr> <th>Feature</th> <th>Description</th> <th>Range</th> <th>Severity</th> <th>Security feature</th> <th></th> </tr></thead> <tbody> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Training Time</td> <td>Training time measures the total time taken to train a machine learning model. It is a critical metric for understanding the efficiency of the training process and for planning resource allocation.</td> <td>Training time is typically measured in seconds, minutes, or hours, depending on the size of the dataset and the complexity of the model. It can range from a few minutes for simple models to several hours or days for more complex models.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Training Accuracy</td> <td>Training accuracy represents the percentage of correct predictions made by the model on the training dataset. It is a key metric for evaluating how well the model has learned from the training data.</td> <td>Training accuracy is expressed as a percentage, ranging from 0% to 100%. Higher accuracy indicates that the model is performing well on the training data.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Validation Accuracy</td> <td>Validation accuracy measures the model’s performance on a validation dataset, which is separate from the training data. It is used to assess the model’s ability to generalize to new, unseen data.</td> <td>Validation accuracy is expressed as a percentage, ranging from 0% to 100%. A high validation accuracy suggests good generalization, while a significant gap between training and validation accuracy may indicate overfitting.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Loss</td> <td>Loss is a measure of how far the model’s predictions are from the actual outcomes. It serves as the optimization objective during training, with the goal of minimizing the loss.</td> <td>Loss is usually measured as a non-negative number, with lower values indicating better model performance. The specific range depends on the loss function used, but it typically ranges from 0 to a small positive value.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Model Inference Latency</td> <td>Inference latency measures the time taken for a deployed model to produce predictions on new input data. This metric is crucial for applications requiring quick responses, such as real-time decision-making systems.</td> <td>Latency is typically measured in milliseconds (ms). Lower latency (e.g., under 100ms) is preferred for real-time applications, but the acceptable range depends on the specific use case.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Model Throughput</td> <td>Throughput refers to the number of predictions a model can generate per second. It is an essential metric for understanding the model’s efficiency and scalability in production environments.</td> <td>Throughput is measured in predictions per second (PPS), and the range can vary from tens to thousands, depending on the model’s complexity and the infrastructure.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>CPU/GPU Utilization</td> <td>This metric tracks the percentage of CPU or GPU resources utilized during training or inference. Monitoring resource utilization helps in optimizing costs and ensuring that resources are efficiently used.</td> <td>Utilization is measured as a percentage, typically ranging from 0% to 100%. Efficient use of resources is essential to balance performance and cost, and high utilization may indicate the need to scale resources.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Memory Utilization</td> <td>Memory utilization measures the amount of memory consumed during training or inference. This metric is important to prevent memory bottlenecks that can cause slowdowns or crashes.</td> <td>Memory utilization is measured in bytes or as a percentage of total available memory. It can range from a few megabytes (MB) to several gigabytes (GB), depending on the model and data.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Hyperparameter Tuning Metrics</td> <td>During hyperparameter tuning, Vertex AI provides metrics such as the best objective metric value, which reflects the optimal performance achieved during the tuning process. These metrics are essential for selecting the best model configuration.</td> <td>The range depends on the specific objective metric being optimized, such as accuracy or loss. Accuracy might range from 0% to 100%, while loss values typically range from 0 to a small positive number.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Error Rate</td> <td>Error rate measures the frequency of incorrect predictions or failures during inference. This metric is vital for assessing the reliability of a deployed model.</td> <td>Error rate is expressed as a percentage, with lower values (e.g., below 1%) indicating more reliable model performance.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Cost per Inference</td> <td>This metric measures the cost associated with each inference made by the model. It is important for budgeting and understanding the financial impact of deploying machine learning models at scale.</td> <td>Cost per inference is measured in monetary units (e.g., USD) and varies depending on the complexity of the model, the resources used, and the pricing structure of Google Cloud services.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Model Drift</td> <td>Model drift refers to the change in model performance over time as the data distribution evolves. It’s a key metric for ensuring that the model remains accurate and relevant in production.</td> <td>Model drift is often measured as a percentage change in key performance metrics such as accuracy or loss over time. Significant drift may indicate the need for model retraining.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>AutoML Model Performance</td> <td>For models trained using AutoML in Vertex AI, this metric provides insights into how well the automatically generated models are performing. It includes metrics like accuracy, precision, recall, and F1-score.</td> <td>These performance metrics are typically expressed as percentages or scores ranging from 0 to 1. Higher values indicate better model performance, with ranges depending on the specific task (e.g., classification vs. regression).</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Pipeline Execution Time</td> <td>For models deployed through Vertex AI Pipelines, this metric tracks the total time taken for a pipeline to execute, from data ingestion to model deployment. It helps in optimizing pipeline efficiency.</td> <td>Pipeline execution time is measured in minutes or hours, depending on the complexity of the pipeline and the tasks involved.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> </tbody></table> </div> </div> <div class="wbc-heading clearfix"><h3 class="special-heading-3" style="font-size:25px;">Azure OpenAI</h3></div> </div> </div> </div> </div><div class="vc_row wpb_row "> <div class="wpb_column vc_column_container vc_col-sm-12 "><div class="vc_column-inner " > <div class="wpb_wrapper"> <div class="wpb_raw_code wpb_content_element wpb_raw_html" > <div class="wpb_wrapper"> <table><thead> <tr> <th>Feature</th> <th>Description</th> <th>Range</th> <th>Severity</th> <th>Security feature</th> <th>Aggregation</th> </tr></thead> <tbody> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Inference Latency</td> <td>Inference latency measures the time taken by the AI model to process an input and generate an output. This metric is crucial for applications where quick responses are needed, such as chatbots or real-time content generation.</td> <td>Latency is typically measured in milliseconds (ms). Lower latency (e.g., under 100ms) is ideal for real-time applications, though acceptable ranges vary based on the specific use case.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Request Throughput</td> <td>Request throughput refers to the number of requests the AI model can handle per second. This metric is essential for understanding the scalability of the model and ensuring it can handle the expected load in production environments.</td> <td>Throughput is measured in requests per second (RPS) and can range from tens to thousands depending on the model's complexity and the infrastructure used.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Token Utilization</td> <td>Token utilization tracks the number of tokens processed during inference, including both input and output tokens. This metric is important for cost management, as many AI models, like GPT, are priced based on token usage.</td> <td>Token count varies depending on the length of the input and the output generated. It can range from a few tokens for short inputs to several hundred tokens for longer interactions.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Success Rate</td> <td>The success rate measures the percentage of successful inferences or API calls made to the model without errors. This metric is crucial for assessing the reliability of the service.</td> <td>Success rate is expressed as a percentage, typically ranging from 0% to 100%. A higher success rate (closer to 100%) indicates more reliable performance.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Model Accuracy (Task-Specific)</td> <td>For certain applications, such as classification or content generation, model accuracy indicates how well the model's outputs align with the expected results. This metric varies depending on the task and model configuration.</td> <td>Accuracy is usually expressed as a percentage, with ranges from 0% to 100%. Higher accuracy indicates better model performance on the given task.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Compute Utilization</td> <td>Compute utilization tracks the percentage of CPU or GPU resources used by the model during inference. This metric helps in optimizing resource allocation and managing costs effectively.</td> <td>Utilization is measured as a percentage, typically ranging from 0% to 100%. High utilization indicates that resources are being fully leveraged, but excessive utilization might necessitate scaling resources.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Memory Utilization</td> <td>Memory utilization measures the amount of memory used during model inference. Monitoring this metric is crucial to prevent memory-related bottlenecks and ensure smooth operation.</td> <td>Memory utilization is measured in bytes or as a percentage of total available memory. Depending on the model and data size, this can range from a few megabytes (MB) to several gigabytes (GB).</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Error Rate</td> <td>Error rate measures the frequency of errors encountered during API calls or model inferences. This metric is important for identifying issues with model performance or infrastructure.</td> <td>Error rate is expressed as a percentage, with lower values (e.g., below 1%) indicating more reliable model performance.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Cost per Request</td> <td>This metric tracks the cost associated with each request made to the AI model. It is vital for budgeting and understanding the financial implications of deploying AI models at scale.</td> <td>Cost per request is measured in monetary units (e.g., USD) and varies depending on the model complexity, the resources used, and the pricing structure of Azure services.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Response Quality</td> <td>For generative models, response quality metrics evaluate the relevance, coherence, and overall quality of the output generated by the model. This is particularly important in applications like content creation or conversational AI.</td> <td>Response quality can be assessed using various scoring methods, often ranging from 0 to 1 or as qualitative assessments. Higher scores indicate better quality.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Rate Limits and Throttling</td> <td>This metric monitors the frequency of hitting rate limits or experiencing throttling during API calls. Understanding this helps in managing API usage and scaling appropriately.</td> <td>This is typically a count of incidents, with a higher count indicating that the current rate limits are being exceeded, requiring adjustments to usage patterns or plan levels.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>User Engagement Metrics</td> <td>These metrics measure how end-users interact with the AI models, such as click-through rates, engagement duration, or satisfaction scores. They are essential for understanding the effectiveness of AI-driven features in user-facing applications.</td> <td>Engagement metrics vary widely based on the application but are often expressed as percentages or absolute counts (e.g., clicks, time spent).</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Model Drift</td> <td>Model drift tracks changes in model performance over time as the underlying data distribution changes. It’s crucial for maintaining the relevance and accuracy of AI models in production.</td> <td>Model drift is usually measured as a percentage change in key performance metrics like accuracy or error rate over time. Significant drift may require retraining or updating the model.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Response Time Variability</td> <td>This metric measures the variability in response times for model inferences, helping to identify inconsistencies or potential issues with the service.</td> <td>Response time variability is often measured as a standard deviation or range of response times in milliseconds (ms). Lower variability indicates more consistent performance.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> </tbody></table> </div> </div> <div class="wbc-heading clearfix"><h3 class="special-heading-3" style="font-size:25px;">NVIDIA DGX</h3></div> </div> </div> </div> </div><div class="vc_row wpb_row "> <div class="wpb_column vc_column_container vc_col-sm-12 "><div class="vc_column-inner " > <div class="wpb_wrapper"> <div class="wpb_raw_code wpb_content_element wpb_raw_html" > <div class="wpb_wrapper"> <table><thead> <tr> <th>Feature</th> <th>Description</th> <th>Range</th> <th>Severity</th> <th>Security feature</th> <th>Aggregation</th> </tr></thead> <tbody> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>GPU Utilization</td> <td>GPU utilization measures the percentage of GPU resources used during training or inference. This metric is crucial for understanding how effectively the GPUs are being utilized and whether there’s a need to optimize workloads or scale resources.</td> <td>GPU utilization is typically expressed as a percentage, ranging from 0% to 100%. Higher utilization indicates that the GPUs are being fully leveraged, while lower utilization might suggest underutilization or inefficiencies in the workload.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>GPU Memory Utilization</td> <td>This metric tracks the amount of GPU memory being used during model training or inference. Monitoring GPU memory utilization helps prevent memory bottlenecks that could slow down or halt processing.</td> <td>GPU memory utilization is measured in bytes (e.g., GB) or as a percentage of total available memory. The range can vary significantly depending on the model size and data batch sizes, typically from a few gigabytes to several tens of gigabytes.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Training Time</td> <td>Training time measures the total duration required to train a model on DGX Cloud. This metric is essential for evaluating the efficiency of the training process and optimizing resource usage.</td> <td>Training time is measured in minutes, hours, or even days, depending on the complexity of the model and the size of the dataset. It can range from a few minutes for smaller models to several hours or more for large-scale models.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Inference Latency</td> <td>Inference latency refers to the time taken for a model to produce a prediction or result after receiving input data. This metric is critical for applications requiring real-time or low-latency predictions, such as autonomous vehicles or real-time analytics.</td> <td>Inference latency is typically measured in milliseconds (ms). Lower latency is generally preferred, with acceptable ranges varying depending on the specific application’s requirements.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Model Throughput</td> <td>Throughput measures the number of inferences or training iterations that can be processed per second. This metric is important for understanding the scalability and efficiency of AI workloads on DGX Cloud.</td> <td>Throughput is measured in inferences per second (IPS) or training iterations per second, depending on the workload. The range can vary from tens to thousands, depending on the model and the available resources.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Disk I/O Utilization</td> <td>Disk I/O utilization tracks the read and write operations on the storage disks during training or inference. This metric helps in identifying potential bottlenecks in data loading or storage management.</td> <td>Disk I/O is measured in input/output operations per second (IOPS) or as a percentage of total available bandwidth. Higher values indicate more intensive disk usage, which might require optimization or scaling of storage resources.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Network Utilization</td> <td>Network utilization measures the bandwidth used for data transfer between GPUs, storage, and other nodes in a distributed training setup. This metric is vital for optimizing data transfer rates and minimizing bottlenecks in distributed AI workflows.</td> <td>Network utilization is measured in bits per second (bps) or as a percentage of available network bandwidth. It can range from a few megabits per second (Mbps) to several gigabits per second (Gbps), depending on the workload.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Job Queue Time</td> <td>Job queue time measures the time a training or inference job spends waiting in the queue before execution begins. This metric is important for understanding system load and optimizing job scheduling.</td> <td>Queue time is typically measured in seconds or minutes. Lower queue times indicate more efficient resource allocation, while higher queue times may suggest the need for additional resources or better scheduling practices.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Energy Consumption</td> <td>Energy consumption measures the amount of power used by the GPUs and other infrastructure during training or inference. This metric is increasingly important for managing the sustainability and cost-efficiency of AI workloads.</td> <td>Energy consumption is typically measured in kilowatt-hours (kWh). The range can vary depending on the scale of the workload, with larger models and longer training times consuming more energy.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Cost per Training/Inference</td> <td>This metric tracks the cost associated with each training or inference operation on DGX Cloud. It is essential for budgeting and managing the financial impact of running large-scale AI workloads.</td> <td>Cost is typically measured in monetary units (e.g., USD). The range varies depending on the model complexity, duration of training, and the resources used, and can range from a few dollars to thousands of dollars for extensive training jobs.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Temperature Monitoring</td> <td>Temperature monitoring tracks the operating temperature of GPUs and other hardware components. This metric is crucial for ensuring that the hardware operates within safe temperature ranges, preventing overheating and potential damage.</td> <td>Temperature is typically measured in degrees Celsius (°C). Optimal operating temperatures usually range from 30°C to 80°C, depending on the specific hardware components and workload intensity.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Model Convergence</td> <td>Model convergence measures how quickly a model's loss function reaches a minimum during training. It is an important metric for assessing the effectiveness of the training process and whether the model is learning as expected.</td> <td>Convergence is usually assessed by plotting the loss over time or epochs, with the goal being a steady decrease in loss values. The specific range varies depending on the model and training parameters.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Error Rate</td> <td>Error rate measures the frequency of errors or failed operations during training or inference. This metric helps in identifying issues with model performance or infrastructure.</td> <td>Error rate is expressed as a percentage, with lower values indicating more reliable performance. Ideally, the error rate should be as close to 0% as possible.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td>Job Completion Time</td> <td>Job completion time tracks the total time taken for a training or inference job to complete from start to finish. This metric is essential for assessing the efficiency and productivity of AI workflows.</td> <td>Completion time is measured in minutes or hours, depending on the complexity of the job. It can range from a few minutes for small tasks to several hours or more for large-scale jobs.</td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td></td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> </tbody></table> </div> </div> </div> </div> </div> </div> </div> <div class="clearfix"></div> </div> </div> </article>    <div class="comment-block"> <div class="heading no-comments" id="comments"> <h4>No Comments</h4> </div> <div id="respond" class="comment-form"> <div class="heading"> <h4>Leave a Reply</h4> </div> <small><a rel="nofollow" id="cancel-comment-reply-link" href="/gen-ai-llm-security-insights-metrics-leading-generative-ai-platforms/#respond" style="display:none;">Cancel reply</a></small> <form action="https://alertai.com/wp-comments-post.php?wpe-comment-post=alertai" method="post" id="reply" class="form-horizontal"> <div class="row"><p class="comment-form-author col-sm-6 col-md-4"><label for="author">Name <span class="required">*</span></label> <input id="author" name="author" type="text" value="" size="30" aria-required='true' /></p> <p class="comment-form-email col-sm-6 col-md-4"><label for="email">Email <span class="required">*</span></label> <input id="email" name="email" type="text" value="" size="30" aria-required='true' /></p> <p class="comment-form-url col-sm-6 col-md-4"><label for="url">Website</label><input id="url" name="url" type="text" value="" size="30" /></p></div> <label for="message">Comment*</label><textarea id="message" name="comment" cols="90" rows="10"></textarea> <p class="form-submit"> <input name="submit" type="submit" id="submit" value="Post Comment" /> <input type='hidden' name='comment_post_ID' value='1882' id='comment_post_ID' /> <input type='hidden' name='comment_parent' id='comment_parent' value='0' /> </p> <p style="display: none !important;" class="akismet-fields-container" data-prefix="ak_"><label>Δ<textarea name="ak_hp_textarea" cols="45" rows="8" maxlength="100"></textarea></label><input type="hidden" id="ak_js_1" name="ak_js" value="104"/><script>document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() );</script></p> </form> </div> </div> </div>  </div>  <div class="col-md-3"> <div class="side-bar"> <div class="widget widget_search"> <div class="widget search-widget"> <form method="get" id="searchform" action="https://alertai.com/" role="search" class="search-form"> <input type="search" name="s" value="" id="s" placeholder="Search Site.." /> </form> </div></div><div class="widget wbc-recent-post-widget"><h4 class="widget-title">Industries | Success stories</h4><ul class="wbc-recent-post-list"><li><div class="wbc-recent-post-img"> <div class="wbc-image-wrap"> <a href="https://alertai.com/generative-ai-security-llm-security-models-risks/security-context-and-impact-of-generative-ai-in-retail-industry/"><img width="150" height="150" src="https://alertai.com/wp-content/uploads/2024/08/retail-pic-0827-150x150.jpg" class="attachment-thumbnail size-thumbnail wp-post-image" alt="Gen AI security, Generative AI security,Security for Gen AI LLM security,Model security,Prompt security,RAG security,AI vulnerabilities, vulnerabilities in AI AI risks, GenAI risks, risks in GenAI,AI privacy, Privacy in AI,AI pipeline security GEN AI in industries,GEN AI solutions,LLM Testing, GenAI testing" /> </a> <a class="item-link-overlay" href="https://alertai.com/generative-ai-security-llm-security-models-risks/security-context-and-impact-of-generative-ai-in-retail-industry/"></a> <div class="wbc-extra-links"> <a href="https://alertai.com/generative-ai-security-llm-security-models-risks/security-context-and-impact-of-generative-ai-in-retail-industry/" class="wbc-go-link"><i class="fa fa-link"></i></a> </div> </div></div><div class="widget-content"><h6><a href="https://alertai.com/generative-ai-security-llm-security-models-risks/security-context-and-impact-of-generative-ai-in-retail-industry/">Retail Industry – Generative AI security</a></h6><p>Generative AI in Retail. 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<div class="textwidget"><p>Culture of 360 : Embracing Change</p> <p>In the shifting Paradigm of Business heralded by rise of Generative AI ..<br /> 360 is culture that emphasizes security in the time of great transformation.<br /> Our commitment to Our customers is represented by Our culture of 360.</p> </div> </div><div class="widget widget_block"><script>(function() { window.mc4wp = window.mc4wp || { listeners: [], forms: { on: function(evt, cb) { window.mc4wp.listeners.push( { event : evt, callback: cb } ); } } } })(); </script><form id="mc4wp-form-1" class="mc4wp-form mc4wp-form-1998" method="post" data-id="1998" data-name="AlertAI-MC4WP-Form" ><div class="mc4wp-form-fields"><p> <label>Sign up our Newsletter: <input type="email" name="EMAIL" placeholder="Your email address" required /> </label> </p> <p> <input type="submit" value="Sign up " /> </p></div><label style="display: none !important;">Leave this field empty if you're human: <input type="text" name="_mc4wp_honeypot" 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class="wbc-nav-content"><span class="wbc-nav-title">OWASP Top 10 LLM Security Measures</span><span class="wbc-nav-image"><img width="150" height="150" src="https://alertai.com/wp-content/uploads/2024/08/shutterstock_security_shield-150x150.jpg" class="attachment-thumbnail size-thumbnail wp-post-image" alt="Gen AI security, Generative AI security,Security for Gen AI LLM security,Model security,Prompt security,RAG security,AI vulnerabilities, vulnerabilities in AI AI risks, GenAI risks, risks in GenAI,AI privacy, Privacy in AI,AI pipeline security GEN AI in industries,GEN AI solutions,LLM Testing, GenAI testing, Adversarial attacks,owasp risks" decoding="async" srcset="https://alertai.com/wp-content/uploads/2024/08/shutterstock_security_shield-150x150.jpg 150w, https://alertai.com/wp-content/uploads/2024/08/shutterstock_security_shield-500x500.jpg 500w, https://alertai.com/wp-content/uploads/2024/08/shutterstock_security_shield-1000x1000.jpg 1000w" sizes="(max-width: 150px) 100vw, 150px" /></span></span></span></a><div class="wbc-nav-row-1"><div class="container"><div class="row"><div class="col-12"><div class="wbc-page-nav wbc-prev-link"><span>PREVIOUS</span><h4 class="entry-title wbc-nav-title"><a href="https://alertai.com/owasp-top10-llm-risks-genai-model-prompt-security/">OWASP Top 10 LLM Security Measures</a></h4></div></div></div></div></div><div class="wpb-content-wrapper"><div class="lnkdn_buttons"><div class="lnkdn-share-button"> <script type="IN/Share" data-url="https://alertai.com/wbc-reuseables/about-alert-ai/" data-counter=""></script> </div><div class="lnkdn-follow-button"> <script type="IN/FollowCompany" data-id="104405749" data-counter="right"></script> </div></div><div class="vc_row wpb_row "> <div class="wpb_column vc_column_container vc_col-sm-12 "><div class="vc_column-inner " > <div class="wpb_wrapper"> </div> </div> </div> </div><div id="wbc-6746633acd41c" class="vc_row wpb_row full-width-section"><div class="container"><div class="row row-inner"> <div class="wpb_column vc_column_container vc_col-sm-12 "><div class="vc_column-inner " > <div class="wpb_wrapper"> <div class="wpb_text_column wpb_content_element " > <div class="wpb_wrapper"> <h3><span style="font-size: 18pt; color: #333333;"><b>Alert AI</b></span></h3> <p><span style="font-size: 18pt; color: #333333;">Alert AI is end-to-end, Interoperable Generative AI security platform to help enhance security of Generative AI applications and workflows against potential adversaries, model vulnerabilities, privacy, copyright and legal exposures, sensitive information leaks, Intelligence and data exfiltration, infiltration at training and inference, integrity attacks in AI applications, anomalies detection and enhanced visibility in AI pipelines. forensics, audit,AI governance in AI footprint.</span></p> <h2><span style="font-size: 18pt; color: #333333;"><b>Alert AI</b> Generative AI security platform</span></h2> <p><span style="font-size: 18pt; color: #333333;">What is at stake AI & Gen AI in Business? We are addressing exactly that.</span></p> <p><span style="font-size: 18pt; color: #333333;">Generative AI security solution for Healthcare, Insurance, Retail, Banking, Finance, Life Sciences, Manufacturing.</span></p> <p><span style="font-size: 18pt; color: #333333;">Despite the Security challenges, the promise of Generative AI is enormous.</span></p> <p><span style="font-size: 18pt; color: #333333;">We are committed to enhance the security of Generative AI applications and workflows in industries and enterprises to reap the benefits .</span></p> <h3><span style="font-size: 18pt; color: #333333;"><strong>Alert AI Generative AI Security Services</strong></span></h3> <p> </p> <p> </p> <p> </p> <p><span style="font-size: 18pt; color: #333333;"><img loading="lazy" decoding="async" class="alignnone wp-image-1812 size-full" src="https://alertai.com/wp-content/uploads/2024/08/genai-risks-alertai.jpg" alt="ALERT AI Generative AI Security platform, AI Privacy, LLM Vulnerabilities, Adversarial Risks, GenAI security, ALERT AI " width="708" height="1277" srcset="https://alertai.com/wp-content/uploads/2024/08/genai-risks-alertai.jpg 708w, https://alertai.com/wp-content/uploads/2024/08/genai-risks-alertai-166x300.jpg 166w, https://alertai.com/wp-content/uploads/2024/08/genai-risks-alertai-568x1024.jpg 568w, https://alertai.com/wp-content/uploads/2024/08/genai-risks-alertai-320x577.jpg 320w, https://alertai.com/wp-content/uploads/2024/08/genai-risks-alertai-480x866.jpg 480w" sizes="(max-width: 708px) 100vw, 708px" /></span></p> <p> </p> <h3><span style="font-size: 18pt; color: #333333;"><b>Alert AI 360 view and Detections</b></span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Alerts and Threat detection in AI footprint</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">LLM & Model Vulnerabilities Alerts</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Adversarial ML Alerts</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Prompt, response security and Usage Alerts</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Sensitive content detection Alerts</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Privacy, Copyright and Legal Alerts</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">AI application Integrity Threats Detection</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Training, Evaluation, Inference Alerts</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">AI visibility, Tracking & Lineage Analysis Alerts</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Pipeline analytics Alerts</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Feedback loop</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">AI Forensics</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Compliance Reports</span></li> </ul> <p> </p> <h3><span style="font-size: 18pt; color: #333333;">End-to-End GenAI Security</span></h3> <ul> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Data alerts</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Model alerts</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Pipeline alerts</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Evaluation alerts</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Training alerts</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Inference alerts</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Model Vulnerabilities</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Llm vulnerabilities</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Privacy</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Threats</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Resources</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Environments</span></li> <li aria-level="1"><span style="font-size: 18pt; color: #333333;">Governance and compliance</span></li> </ul> <p> </p> <h3><span style="font-size: 18pt; color: #333333;"><strong>Enhace, Optimize, Manage Generative AI security of Business applications</strong></span></h3> <ul> <li><span style="font-size: 18pt; color: #333333;">Manage LLM, Model, Pipeline, Prompt Vulnerabilities</span></li> <li><span style="font-size: 18pt; color: #333333;">Enhance Privacy</span></li> <li><span style="font-size: 18pt; color: #333333;">Ensure integrity</span></li> <li><span style="font-size: 18pt; color: #333333;">Optimize domain-specific security guardrails</span></li> <li><span style="font-size: 18pt; color: #333333;">Discover Rogue pipelines, models, Rogue prompts</span></li> <li><span style="font-size: 18pt; color: #333333;">Block Hallucination and Misinformation attack</span></li> <li><span style="font-size: 18pt; color: #333333;">Block prompts harmful Content Generation</span></li> <li><span style="font-size: 18pt; color: #333333;">Block Prompt Injection</span></li> <li><span style="font-size: 18pt; color: #333333;">Detect robustness risks, perturbation attacks</span></li> <li><span style="font-size: 18pt; color: #333333;">Detect output re-formatting attacks</span></li> <li><span style="font-size: 18pt; color: #333333;">Stop information disclosure attacks</span></li> <li><span style="font-size: 18pt; color: #333333;">Track to source of origin training Data</span></li> <li><span style="font-size: 18pt; color: #333333;">Detect Anomalous behaviors</span></li> <li><span style="font-size: 18pt; color: #333333;">Zero-trust LLM’s</span></li> <li><span style="font-size: 18pt; color: #333333;">Data protect GenAI applications</span></li> <li><span style="font-size: 18pt; color: #333333;">Secure access to tokenizers</span></li> <li><span style="font-size: 18pt; color: #333333;">Prompt Intelligence Loss prevention</span></li> <li><span style="font-size: 18pt; color: #333333;">Enable domain-specific policies, guardrails</span></li> <li><span style="font-size: 18pt; color: #333333;">Get Recommendations</span></li> <li><span style="font-size: 18pt; color: #333333;">Review issues</span></li> <li><span style="font-size: 18pt; color: #333333;">Forward AI incidents to SIEM</span></li> <li><span style="font-size: 18pt; color: #333333;">Audit reports — AI Forensics</span></li> <li><span style="font-size: 18pt; color: #333333;">Findings, Sources, Posture Management.</span></li> <li><span style="font-size: 18pt; color: #333333;">Detect and Block Data leakage breaches</span></li> <li><span style="font-size: 18pt; color: #333333;">Secure access with Managed identities</span></li> </ul> <p> </p> <h3><span style="font-size: 18pt; color: #333333;">Security Culture of 360 | Embracing Change.</span></h3> <h3></h3> <p><span style="font-size: 18pt; color: #333333;">In the shifting paradigm of Business heralded by rise of Generative AI ..</span></p> <p><span style="font-size: 18pt; color: #333333;">360 is culture that emphasizes security in the time of great transformation.</span></p> <p><span style="font-size: 18pt; color: #333333;">Our commitment to our customers is represented by our culture of 360.</span></p> <p><span style="font-size: 18pt; color: #333333;">Organizations need to responsibly assess and enhance the security of their AI environments development, staging, production for Generative AI applications and Workflows in Business.</span></p> <p><span style="font-size: 18pt; color: #333333;">Despite the Security challenges, the promise of Generative AI is enormous.</span></p> <p><span style="font-size: 18pt; color: #333333;">We are committed to enhance the security of Generative AI applications and workflows in industries and enterprises to reap the benefits.</span></p> <p><span style="font-size: 18pt; color: #333333;"><a style="color: #333333;" href="https://alertai.com/llm-generative-ai-security">Home</a> <a style="color: #333333;" href="https://alertai.com/llm-security-generative-ai-security-model-vulnerabilities-privacy-trust-threats/">Services</a> <a style="color: #333333;" href="https://alertai.com/llm-security-generative-ai-security-vulnerabilities-privacy-model-risks">Resources</a> <a style="color: #333333;" href="https://alertai.com/#industries">Industries</a></span></p> </div> </div> </div> </div> </div> </div></div></div> </div><div class="wpb-content-wrapper"><div class="lnkdn_buttons"><div class="lnkdn-share-button"> <script type="IN/Share" data-url="https://alertai.com/wbc-reuseables/customer-testimonials/" data-counter=""></script> </div><div class="lnkdn-follow-button"> <script type="IN/FollowCompany" data-id="104405749" data-counter="right"></script> </div></div><div id="wbc-6746633acec77" class="vc_row wpb_row full-width-section" style="background-color:#ffffff;padding-top: 100px;padding-bottom: 100px;"> <div class="wpb_column vc_column_container vc_col-sm-12 "><div class="vc_column-inner " style="padding-top: 30px;"> <div class="wpb_wrapper"> <div class="wbc-heading clearfix"><h4 class="special-heading-3" style="font-size:25px;color:#000000;text-align:center;margin-bottom:0px;">READ FROM INDUSTRY</h4></div><div class="wbc-heading clearfix"><h3 class="special-heading-3" style="font-size:40px;color:#000000;text-align:center;margin-bottom:0px;">OUR <span class="wbc-color" style="color:#ff6632;">TESTIMONIALS</span></h3></div><hr class="wbc-hr" style="background-color:#ff6632;width:85px;height:5px;" /><div class="wbc-heading clearfix"><div class="default-heading" style="font-size:20px;text-align:center;margin-bottom:45px;margin-right:auto;margin-left:auto;max-width:750px;">According our Customers, <span class="wbc-color" >We make difference</span></div></div><div class="vc_row wpb_row vc_inner vc_row-fluid"><div class="wpb_column vc_column_container vc_col-sm-8 vc_col-sm-offset-2"><div class="vc_column-inner"><div class="wpb_wrapper"><div class="wbc-color-box clearfix" style="background-color:rgba(255,255,255,0.03);color:#000000;padding-bottom:40px;padding-right:60px;padding-top:60px;padding-left:60px;"><div class="wbc-color-box-content"><div class="wbc-testimonial-wrap"><div class="wbc-testimonail-carousel" data-item-height="variable" data-item-speed="7000" ><div><div class="wbc-testimonial"><span class="testimonial-message">``Alert AI is <span class="wbc-color" >end to end</span> Gen AI security solution. Our clients want a consolidate platform for security of all AI applications in all environments. Easy on-boarding even into a private region of cloud. Easy integration.``</span><img loading="lazy" decoding="async" width="150" height="150" src="https://alertai.com/wp-content/uploads/2024/08/Nat-Profile-Pic-150x150.jpg" class="attachment-thumbnail size-thumbnail" alt="Nat-Profile-Pic" srcset="https://alertai.com/wp-content/uploads/2024/08/Nat-Profile-Pic-150x150.jpg 150w, https://alertai.com/wp-content/uploads/2024/08/Nat-Profile-Pic-500x500.jpg 500w" sizes="(max-width: 150px) 100vw, 150px" /><div class="testimonial-info"><div class="testimonial-name">Natarajan Ramanathan</div><small>Enterprise Gen AI security solutions architect | Retail, Pharma, Insurance Industries</small></div></div></div><div><div class="wbc-testimonial"><span class="testimonial-message">``Working with Alert AI has been an absolute pleasure. Their team of skilled professionals is not only knowledgeable in AI and <span class="wbc-color" >LLM security</span> but also dedicated to providing <span class="wbc-color" >top-notch POC and solution architecture</span>. They took the time to <span class="wbc-color" >integrations with our stack</span> and security solution exceeded our expectations.”</span><img loading="lazy" decoding="async" width="128" height="128" src="https://alertai.com/wp-content/uploads/2024/08/nothondo.jpg" class="attachment-thumbnail size-thumbnail" alt="nothondo" /><div class="testimonial-info"><div class="testimonial-name">Nothando Ndlovu</div><small>Cloud Solutions Enginer, |Dev Sec Ops|</small></div></div></div><div><div class="wbc-testimonial"><span class="testimonial-message">``Alert AI has been a game-changer in securing GenAI workflows and large language models. Their expertise in AI security and detections <span class="wbc-color" >ensures our LLMs are protected against emerging threats</span>, providing us with <span class="wbc-color" >peace of mind</span>. The innovative solutions and proactive approach from Alert AI have significantly strengthened our AI infrastructure, making them an invaluable partner in our journey ahead safe and <span class="wbc-color" >secure AI deployment</span>.``</span><img loading="lazy" decoding="async" width="150" height="150" src="https://alertai.com/wp-content/uploads/2024/08/Anjali-150x150.png" class="attachment-thumbnail size-thumbnail" alt="Anjali" /><div class="testimonial-info"><div class="testimonial-name">Anjali Krishna Gopi</div><small>Senior Enterprise AI architect, Genpact</small></div></div></div><div><div class="wbc-testimonial"><span class="testimonial-message">``AI threats are the threats of a multi-fronts.``</span><img loading="lazy" decoding="async" width="150" height="150" src="https://alertai.com/wp-content/uploads/2024/08/cropped-orange-black-removebg-preview-150x150.png" class="attachment-thumbnail size-thumbnail" alt="cropped-orange-black-removebg-preview.png" srcset="https://alertai.com/wp-content/uploads/2024/08/cropped-orange-black-removebg-preview-150x150.png 150w, https://alertai.com/wp-content/uploads/2024/08/cropped-orange-black-removebg-preview-300x300.png 300w, https://alertai.com/wp-content/uploads/2024/08/cropped-orange-black-removebg-preview-500x500.png 500w, https://alertai.com/wp-content/uploads/2024/08/cropped-orange-black-removebg-preview-320x320.png 320w, https://alertai.com/wp-content/uploads/2024/08/cropped-orange-black-removebg-preview-480x480.png 480w, https://alertai.com/wp-content/uploads/2024/08/cropped-orange-black-removebg-preview-270x270.png 270w, https://alertai.com/wp-content/uploads/2024/08/cropped-orange-black-removebg-preview-192x192.png 192w, https://alertai.com/wp-content/uploads/2024/08/cropped-orange-black-removebg-preview-180x180.png 180w, https://alertai.com/wp-content/uploads/2024/08/cropped-orange-black-removebg-preview-32x32.png 32w, https://alertai.com/wp-content/uploads/2024/08/cropped-orange-black-removebg-preview.png 512w" sizes="(max-width: 150px) 100vw, 150px" /><div class="testimonial-info"><div class="testimonial-name">Srini Mommileti <span class="wbc-color" >CEO, Alert AI</span></div><small>Ex Palo Altow Networks, Ex Gigamon</small></div></div></div><div><div class="wbc-testimonial"><span class="testimonial-message">“Security is our top concern and is our top priority. We are looking for tools for our AI workloads. Alert AI has everything <span class="wbc-color" >Risk analysis, Threats, Vulnerabilities, Compliance, Assets and Data Protection</span>. Having managed service with support that runs in our cloud is wonderful.``</span><img loading="lazy" decoding="async" width="150" height="150" src="https://alertai.com/wp-content/uploads/2015/04/taxi-portfolio-six-1-e1723141055245-150x150.jpg" class="attachment-thumbnail size-thumbnail" alt="taxi-portfolio-six" srcset="https://alertai.com/wp-content/uploads/2015/04/taxi-portfolio-six-1-e1723141055245-150x150.jpg 150w, https://alertai.com/wp-content/uploads/2015/04/taxi-portfolio-six-1-e1723141055245-300x300.jpg 300w, https://alertai.com/wp-content/uploads/2015/04/taxi-portfolio-six-1-e1723141055245-320x320.jpg 320w, https://alertai.com/wp-content/uploads/2015/04/taxi-portfolio-six-1-e1723141055245-480x480.jpg 480w, https://alertai.com/wp-content/uploads/2015/04/taxi-portfolio-six-1-e1723141055245.jpg 500w" sizes="(max-width: 150px) 100vw, 150px" /><div class="testimonial-info"><div class="testimonial-name">Senior Director <span class="wbc-color" >Security Operations</span></div><small>Leading Pharma client</small></div></div></div><div><div class="wbc-testimonial"><span class="testimonial-message">``Our team consists of security engineers, AI researchers. The moment we saw our hospital systems attacked by bad actors and forced to close, we quit our jobs to start Alert AI. We seek to work with exceptional people who make impact protect customers``</span><img loading="lazy" decoding="async" width="150" height="150" src="https://alertai.com/wp-content/uploads/2024/08/orange-black-removebg-preview-150x150.png" class="attachment-thumbnail size-thumbnail" alt="orange-black-removebg-preview" srcset="https://alertai.com/wp-content/uploads/2024/08/orange-black-removebg-preview-150x150.png 150w, https://alertai.com/wp-content/uploads/2024/08/orange-black-removebg-preview-300x300.png 300w, https://alertai.com/wp-content/uploads/2024/08/orange-black-removebg-preview-320x320.png 320w, https://alertai.com/wp-content/uploads/2024/08/orange-black-removebg-preview-480x480.png 480w, https://alertai.com/wp-content/uploads/2024/08/orange-black-removebg-preview.png 500w" sizes="(max-width: 150px) 100vw, 150px" /><div class="testimonial-info"><div class="testimonial-name">Srini Mommileti <span class="wbc-color" >CEO, Alert AI</span></div><small>Ex Palo Alto Networks, Ex Gigamon</small></div></div></div><div><div class="wbc-testimonial"><span class="testimonial-message">``Game Changers...``</span><img loading="lazy" decoding="async" width="150" height="150" src="https://alertai.com/wp-content/uploads/2015/12/taxi-blog-slide-two-1-150x150.jpg" class="attachment-thumbnail size-thumbnail" alt="taxi-blog-slide-two" srcset="https://alertai.com/wp-content/uploads/2015/12/taxi-blog-slide-two-1-150x150.jpg 150w, https://alertai.com/wp-content/uploads/2015/12/taxi-blog-slide-two-1-500x500.jpg 500w, https://alertai.com/wp-content/uploads/2015/12/taxi-blog-slide-two-1-1000x1000.jpg 1000w" sizes="(max-width: 150px) 100vw, 150px" /><div class="testimonial-info"><div class="testimonial-name">Security Engineer</div><small>Retail Industry</small></div></div></div><div><div class="wbc-testimonial"><span class="testimonial-message">``AI attacks would lead to major Enterprise fallout if you are complacent and don't act``</span><img loading="lazy" decoding="async" width="150" height="150" src="https://alertai.com/wp-content/uploads/2024/06/GOLD-TEXT-1.2-150x150.png" class="attachment-thumbnail size-thumbnail" alt="GOLD TEXT 1.2" srcset="https://alertai.com/wp-content/uploads/2024/06/GOLD-TEXT-1.2-150x150.png 150w, https://alertai.com/wp-content/uploads/2024/06/GOLD-TEXT-1.2-300x300.png 300w, https://alertai.com/wp-content/uploads/2024/06/GOLD-TEXT-1.2-1024x1024.png 1024w, https://alertai.com/wp-content/uploads/2024/06/GOLD-TEXT-1.2-768x768.png 768w, https://alertai.com/wp-content/uploads/2024/06/GOLD-TEXT-1.2-1536x1536.png 1536w, https://alertai.com/wp-content/uploads/2024/06/GOLD-TEXT-1.2-500x500.png 500w, https://alertai.com/wp-content/uploads/2024/06/GOLD-TEXT-1.2-1000x1000.png 1000w, https://alertai.com/wp-content/uploads/2024/06/GOLD-TEXT-1.2-1140x1139.png 1140w, https://alertai.com/wp-content/uploads/2024/06/GOLD-TEXT-1.2-848x848.png 848w, https://alertai.com/wp-content/uploads/2024/06/GOLD-TEXT-1.2-320x320.png 320w, https://alertai.com/wp-content/uploads/2024/06/GOLD-TEXT-1.2-480x480.png 480w, https://alertai.com/wp-content/uploads/2024/06/GOLD-TEXT-1.2-800x800.png 800w, https://alertai.com/wp-content/uploads/2024/06/GOLD-TEXT-1.2.png 2001w" sizes="(max-width: 150px) 100vw, 150px" /><div class="testimonial-info"><div class="testimonial-name">Srini Mommileti <span class="wbc-color" >CEO ,Alert AI</span></div><small>Ex Palo Alto Networks,Ex Gigamon</small></div></div></div><div><div class="wbc-testimonial"><span class="testimonial-message">“Bad actors wouldn't tell how or when they might strike.”</span><img loading="lazy" decoding="async" width="150" height="150" src="https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-2-WHITE-BACKGROUND-150x150.png" class="attachment-thumbnail size-thumbnail" alt="ALERT AI 2 WHITE BACKGROUND" srcset="https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-2-WHITE-BACKGROUND-150x150.png 150w, 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