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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" box_link="url:https%3A%2F%2Falertai.com%2Fgenerative-ai-security-llm-security-services%23ModelAnalyticsGovernance" heading_size="25"]Audits and Reports Audit trails, Feedback Loop, Recommendations, Model and Datasets versions, Model performance data, accountability and traceability Reports, Create events for security operations center (SOC) analysts, Log Forwarding Tagged AI risk events to SIEM.[/wbc_icon_box][/vc_column][/vc_row][vc_row bg_select="bg_color_section" p_top="150" p_bottom="35" border_color="#ebebeb" bg_color="#ffffff"][vc_column][wbc_heading tag="h4" heading_style="heading-3" align="center" wbc_animation="slideInDown" title="INTEGRATIONS WITH POPULAR PROVIDERS, PLATFORMS" font_size="25" m_bottom="0"][wbc_heading tag="h2" heading_style="heading-3" align="center" title="OVER 100+ |INTEGRATIONS ACROSS AI STACK|" font_size="40" m_bottom="0"][wbc_hr height="5" width="85" bg_color="#ff6632"][wbc_heading tag="div" align="center" title="Ensure domain-specific AI applications are guarded securely, across organization." font_size="25" m_bottom="43" max_width="750" m_left="auto" m_right="auto"][vc_media_grid style="load-more" items_per_page="4" element_width="3" item="masonryMedia_BorderedScale" btn_color="danger" btn_size="lg" initial_loading_animation="bounceIn" grid_id="vc_gid:1731889655302-074deb41efe60b84be69855d5370b367-7" include="1505,1501,1531,1556,1530,1518,1553,1499,1513,1503,1514,1554,1517,1506,1528,1519,1512,1524,1508,1532,1521,1498,1507,1522,1515,1516,1509,1526,1520,1510,1523,1527" css=".vc_custom_1723362115300{margin-top: 0px !important;margin-right: 40px !important;margin-bottom: 0px !important;margin-left: 40px !important;border-top-width: 10px !important;border-right-width: 40px !important;border-bottom-width: 10px !important;border-left-width: 10px !important;padding-top: 20px !important;padding-right: 10px !important;padding-bottom: 20px !important;padding-left: 20px !important;background-color: #ffffff !important;background-position: center 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The first security platform to secure GenAI applications." font_size="25" m_bottom="43" max_width="750" m_left="auto" m_right="auto"][vc_images_carousel images="1531,1505,1501" img_size="400*300" onclick="link_no" mode="vertical" speed="4000" autoplay="yes" hide_pagination_control="yes" hide_prev_next_buttons="yes" wrap="yes" css_animation="none" css=".vc_custom_1723707826831{margin-right: 500px !important;margin-left: 500px !important;border-radius: 2px !important;}"][/vc_column][vc_column][/vc_column][/vc_row][vc_row row_type="full_width" type="container" bg_select="bg_color_section" anchor="industries" p_top="65"][vc_column wbc_animation="slideInDown" content_align="text-left"][wbc_heading tag="h3" heading_style="heading-3" align="center" title="ELEVATE YOUR GENERATIVE AI SECURITY" font_size="25" m_bottom="0"][wbc_heading tag="h3" heading_style="heading-3" align="center" title="|ENHANCING SECURITY IN| GENERATIVE AI SOLUTIONS" font_size="40" m_bottom="0"][wbc_hr height="5" width="85" bg_color="#ff6632"][wbc_heading tag="div" align="center" title="Industry solutions leverage Alert AI to |Enhance, Optimize, Manage| security of Generative AI applications and workflows with domain specific security guardrails." font_size="25" m_bottom="37" max_width="750" m_left="auto" m_right="auto"][wbc_portfolio layout_type="masonry" img_size="post-500x400-image" order_by="ID" order_dir="ASC" group_lightbox="yes" gap="5" show_filter="yes" filter_align="center" cols_s="2" cols_xl="5" cols_l="5" overlay_color="#ee7125" all_word="All"][vc_empty_space height="20px"][vc_empty_space][vc_empty_space][/vc_column][/vc_row][vc_row match_height="yes" vertical_center="yes" row_type="full_width" type="full_screen" font_color="#ffffff"][vc_column width="1/2" parallax_repeat="cover" bg_image_postions="center center" wbc_animation="slideInDown" p_top="92" p_left="4%" p_right="4%" parallax_img="1535" p_bottom="250"][/vc_column][vc_column width="1/2" parallax_repeat="cover" bg_image_postions="center center" font_color="#ffffff" p_left="4%" p_right="4%" p_top="4%"][wbc_heading tag="div" align="center" title="``We’re in a great spot. 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. Stability and security of our societies organizations, agencies under Threat. ``Bad actors wouldn't tell how or when they might strike.``" font_size="25" m_bottom="80" max_width="750" m_left="auto" m_right="auto"][/vc_column][vc_column width="1/4" content_align="text-center"][wbc_icon icon_fontawesome="fas fa-exclamation-triangle" font_size="65" color="#ff6632"][wbc_countup heading_style="heading-1" count_to="1200" font_size="65" m_bottom="5" after="+" color="#000000"][wbc_heading tag="h2" heading_style="heading-2" align="center" title="Number of Alerts" font_size="25" color="#000000"][/vc_column][vc_column width="1/4" content_align="text-center"][wbc_icon icon_fontawesome="fas fa-eye" font_size="65" color="#ff6632"][wbc_countup heading_style="heading-1" count_to="6500" font_size="65" m_bottom="5" color="#000000" after="+"][wbc_heading tag="h2" heading_style="heading-2" align="center" title="Number of Detections" font_size="25" color="#000000"][/vc_column][vc_column width="1/4" content_align="text-center"][wbc_icon icon_fontawesome="fas fa-city" font_size="65" color="#ff6632"][wbc_countup heading_style="heading-1" count_to="16" font_size="65" m_bottom="5" color="#000000"][wbc_heading tag="h2" heading_style="heading-2" align="center" title="Industries" font_size="25" color="#000000"][/vc_column][vc_column width="1/4" content_align="text-center"][wbc_icon icon_fontawesome="fas fa-cloud-upload-alt" font_size="65" color="#ff6632"][wbc_countup heading_style="heading-1" count_to="150" font_size="65" m_bottom="5" color="#000000" after="+"][wbc_heading tag="h2" heading_style="heading-2" align="center" title="Integrations" font_size="25" color="#000000"][/vc_column][vc_column][/vc_column][/vc_row][vc_row row_type="full_width" type="container" bg_select="bg_color_section" p_top="60" p_bottom="60" border_color="#ebebeb" anchor="blog"][vc_column wbc_animation="slideInDown"][wbc_heading tag="h4" heading_style="heading-3" align="center" wbc_animation="slideInDown" title="WHAT IS AT STAKE? 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<p><b>Alert AI Alerts in LLM Metric Evaluation risks</b></p> <p> </p> <p><b>Introduction</b></p> <p> </p> <p>LLMs encounter many issues when running but is it easy to detect these issues? To solve this issue, Alert AI uses Detections. An LLM Alert is a detailed alert that describes errors and provides a recommendation to users and developers. When alerts aren’t used it is more difficult to detect errors and vulnerabilities in a model. Using Alerts makes it easier to detect issues in an LLM.</p> <p> </p> <p><b>Alert AI Generative AI security platform – Identifies risks, detects threats, generates Alerts in Generative AI applications, services, environments, deployments. </b>Alert AI security analytics pipelines extracts features, sessionizes, aggregates, classifies and continuously generates Alerts, Recommendations, AI Detection & Response, AI Forensics, Compliance & Governance, and Feedback loop to training, tuning, evaluation, inference pipelines.</p> <p> </p> <p>There are many different kinds of LLM Alerts that exist to detect issues within an LLM. Each of these alerts identifies an issue with the LLM along with additional details of the alert including the cause of the alert and the recommendation for the alert. These alerts are shown when the value of the model does not meet a given criteria or when a security risk occurs. Below are the list of alerts.</p> <p> </p> <p><b>Truthfulness Alert</b></p> <ul> <li aria-level="1">Name: Truthfulness percentage of model is too low <ul> <li aria-level="2">Description: Model’s output contains too much false information</li> <li aria-level="2">Metric/Metrics: Truthfulness</li> <li aria-level="2">Range, Type: Percentage, greater than 85%</li> <li aria-level="2">Recommendation: Provide dataset with more truthful information</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: TruthfulQA</li> <li aria-level="2">Category: Data and Information</li> <li aria-level="2">Cause: Model trained with dataset containing incorrect information</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low truthfulness metric in models. This alert is used for models that specialize in providing factual information.</p> <p> </p> <p><b>Informative Accuracy Alert</b></p> <ul> <li aria-level="1">Name: Informative Accuracy percentage of model is too low <ul> <li aria-level="2">Description: Model’s output has little to no informative accuracy</li> <li aria-level="2">Metric/Metrics: Informative Accuracy</li> <li aria-level="2">Range, Type: Percentage, greater than 80%</li> <li aria-level="2">Recommendation: Tweak model to gather more informative data</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: TruthfulQA</li> <li aria-level="2">Category: Data and Information</li> <li aria-level="2">Cause: Model did not retain enough information from dataset</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low informative accuracy metric in models. This alert is used for models that specialize in providing accurate information.</p> <p> </p> <p><b>Correctness Alert</b></p> <ul> <li aria-level="1">Name: Correctness percentage of model is too low <ul> <li aria-level="2">Description: Model’s output has too much incorrect information</li> <li aria-level="2">Metric/Metrics: Correctness</li> <li aria-level="2">Range, Type: Percentage, greater than 85%</li> <li aria-level="2">Recommendation: Provide dataset with more correct information</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: TruthfulQA, MBPP, HumanEval</li> <li aria-level="2">Category: Data and Information</li> <li aria-level="2">Cause: Model gathered incorrect information from dataset</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low correctness metric in models. This alert is used for models that specialize in providing factual information.</p> <p> </p> <p><b>Consistency Alert</b></p> <ul> <li aria-level="1">Name: Consistency percentage of model is too low <ul> <li aria-level="2">Description: Model’s output has little to no consistency in regards to question</li> <li aria-level="2">Metric/Metrics: Consistency</li> <li aria-level="2">Range, Type: Percentage, greater than 90%</li> <li aria-level="2">Recommendation: Tweak model to be more consistent to prompt</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: TruthfulQA, BIG</li> <li aria-level="2">Category: Question and answer</li> <li aria-level="2">Cause: Model does not understand question given</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low consistency metric in models. This alert is used for models that specialize in answering questions along with conversations between the model and user.</p> <p> </p> <p><b>Coverage Alert</b></p> <ul> <li aria-level="1">Name: Coverage percentage of model is too low <ul> <li aria-level="2">Description: Model does not cover enough topics</li> <li aria-level="2">Metric/Metrics: Coverage</li> <li aria-level="2">Range, Type: Percentage, greater than 90%</li> <li aria-level="2">Recommendation: Provide more training to model with various topics</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: TruthfulQA, BIG</li> <li aria-level="2">Category: Coverage</li> <li aria-level="2">Cause: Model did not train with enough topics</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low coverage metric in models. This alert is used for models that specialize in multiple fields of topics.</p> <p> </p> <p><b>Calibration Alert</b></p> <ul> <li aria-level="1">Name: Calibration value of model is too high <ul> <li aria-level="2">Description: Model keeps changing its output when being doubted by user</li> <li aria-level="2">Metric/Metrics: Calibration</li> <li aria-level="2">Range, Type: Decimal, less than 0.01</li> <li aria-level="2">Recommendation: Tweak model to maintain same output</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: TruthfulQA</li> <li aria-level="2">Category: Calibration</li> <li aria-level="2">Cause: Model is has little to no confidence in responses, calibration value is too high compared to accuracy</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a high calibration metric in models. A low calibration result would result in the model changing its answer constantly. This alert is used for the majority of models since the models require confidence in their answers.</p> <p> </p> <p><b>Accuracy Alert</b></p> <ul> <li aria-level="1">Name: Accuracy of model is too low <ul> <li aria-level="2">Description: Model is not providing accurate responses to given question</li> <li aria-level="2">Metric/Metrics: Accuracy</li> <li aria-level="2">Range, Type: Percentage, greater than 85%</li> <li aria-level="2">Recommendation: Train model with more accurate data</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: HellaSwag, BIG, MMLU, MLFlow LLM Evaluate, RAGAs, Arize Phoenix, DeepEval</li> <li aria-level="2">Category: Model accuracy</li> <li aria-level="2">Cause: Model did not learn well from training dataset</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low accuracy metric in models. This alert is used for models that specialize in factual information and answering questions.</p> <p> </p> <p><b>Perplexity Alert</b></p> <ul> <li aria-level="1">Name: Perplexity of model is too high <ul> <li aria-level="2">Description: Model cannot determine closest response that best matches question</li> <li aria-level="2">Metric/Metrics: Perplexity</li> <li aria-level="2">Range, Type: Integer, less than 10</li> <li aria-level="2">Recommendation: Train model to make connections</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: HellaSwag, DeepEval</li> <li aria-level="2">Category: Probability and Logistics</li> <li aria-level="2">Cause: Model did not train well with understanding connections</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low perplexity metric in models. This alert is used for models that specialize in factual information and answering questions.</p> <p> </p> <p><b>Log-Likelihood Alert</b></p> <ul> <li aria-level="1">Name: Log-Likelihood of model is too low <ul> <li aria-level="2">Description: Model cannot choose correct response to question</li> <li aria-level="2">Metric/Metrics: Log-Likelihood</li> <li aria-level="2">Range, Type: Decimal, Higher log-likelihood</li> <li aria-level="2">Recommendation: Train model to generate increased probability for dataset distribution</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: HellaSwag</li> <li aria-level="2">Category: Probability and Logistics</li> <li aria-level="2">Cause: Model cannot predict trends in dataset distribution well</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low log-Likelihood metric in models. This alert is used for models that specialize in dataset trends and predictions.</p> <p> </p> <p><b>Log-Probability Alert</b></p> <ul> <li aria-level="1">Name: Log-Probability of model is too low <ul> <li aria-level="2">Description: Model utilizing probability of responses that are too low</li> <li aria-level="2">Metric/Metrics: Log-Probability</li> <li aria-level="2">Range, Type: Decimal, Higher log-likelihood</li> <li aria-level="2">Recommendation: Train model to generate increased probability to detect better responses</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: HellaSwag</li> <li aria-level="2">Category: Probability and Logistics</li> <li aria-level="2">Cause: Model cannot predict trends in dataset distribution well</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low log-Probability metric in models. This alert is used for models that specialize in dataset trends and predictions.</p> <p> </p> <p><b>F1 Score Alert</b></p> <ul> <li aria-level="1">Name: F1 Score of model is too low <ul> <li aria-level="2">Description: Model is detecting too many false positive and false negatives</li> <li aria-level="2">Metric/Metrics: F1 Score</li> <li aria-level="2">Range, Type: Percentage, greater than 85%</li> <li aria-level="2">Recommendation: Train model to detection false positive and false negatives</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: HellaSwag, TriviaQA, MLFlow LLM Evaluate, RAGAs, Arize Phoenix, DeepEval</li> <li aria-level="2">Category: False detections</li> <li aria-level="2">Cause: Model cannot tell difference between true detections and false detections</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low F1 Score metric in models. This alert is used for models that check information or responses.</p> <p> </p> <p><b>Information Gain Alert</b></p> <ul> <li aria-level="1">Name: Information Gain value of model is too low <ul> <li aria-level="2">Description: Model is not gaining enough information from the dataset</li> <li aria-level="2">Metric/Metrics: Information Gain</li> <li aria-level="2">Range, Type: Percentage, greater than 80%</li> <li aria-level="2">Recommendation: Train model to gather more information from dataset</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: BIG</li> <li aria-level="2">Category: Data and Information</li> <li aria-level="2">Cause: Model did not learn enough information from training dataset</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low information gain metric in models. This alert is used for models that specialize in providing factual information.</p> <p> </p> <p><b>Response Quality Alert</b></p> <ul> <li aria-level="1">Name: Quality of model’s responses is too low <ul> <li aria-level="2">Description: Model is not generating good quality responses to questions</li> <li aria-level="2">Metric/Metrics: Response Quality</li> <li aria-level="2">Range, Type: Percentage, greater than 80%</li> <li aria-level="2">Recommendation: Train Model to use correct terminology and concepts</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: BIG</li> <li aria-level="2">Category: Questions and Answers</li> <li aria-level="2">Cause: Model is not using right terminology or concepts to respond to question</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low response quality metric in models. This alert is used for models that specialize in providing factual information and answering questions.</p> <p> </p> <p><b>Execution Accuracy Alert</b></p> <ul> <li aria-level="1">Name: Model’s execution accuracy is too low <ul> <li aria-level="2">Description: Model’s code cannot be executed well</li> <li aria-level="2">Metric/Metrics: Execution Accuracy</li> <li aria-level="2">Range, Type: Percentage, greater than 70%</li> <li aria-level="2">Recommendation: Train model to provide code responses that can run without errors</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: MBPP</li> <li aria-level="2">Category: Model Accuracy</li> <li aria-level="2">Cause: Model’s code samples contain too many coding errors</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low execution accuracy metric in models. This alert is used for models that specialize in providing code for given prompts.</p> <p> </p> <p><b>Pass @k Alert</b></p> <ul> <li aria-level="1">Name: Model’s k pass value is too low <ul> <li aria-level="2">Description: Not enough code responses from the model are passing the test cases</li> <li aria-level="2">Metric/Metrics: Pass @k</li> <li aria-level="2">Range, Type: Percentage based on k value, higher percentages for higher k value</li> <li aria-level="2">Recommendation: Train model to provide code responses that can pass all test cases</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: MBPP</li> <li aria-level="2">Category: Code Answers</li> <li aria-level="2">Cause: Code samples provided to model do not pass enough test cases</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low pass @k metric in models. This alert is used for models that specialize in providing code for given prompts.</p> <p> </p> <p><b>Code Quality Alert</b></p> <ul> <li aria-level="1">Name: Model’s code quality value is too low <ul> <li aria-level="2">Description: Code quality of model’s response are not good</li> <li aria-level="2">Metric/Metrics: Code Quality</li> <li aria-level="2">Range, Type: Percentage, greater than 70%</li> <li aria-level="2">Recommendation: Train model to provide better quality code responses</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: MBPP</li> <li aria-level="2">Category: Code Answers</li> <li aria-level="2">Cause: Code samples given to model do not have good quality</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low code quality metric in models. This alert is used for models that specialize in providing code responses for given prompts.</p> <p> </p> <p><b>Sample Efficiency Alert</b></p> <ul> <li aria-level="1">Name: Model’s sample efficiency is too low <ul> <li aria-level="2">Description: Model is not generating good code based on samples</li> <li aria-level="2">Metric/Metrics: Sample Efficiency</li> <li aria-level="2">Range, Type: Higher sample efficiency</li> <li aria-level="2">Recommendation: Provide more sample for model to train with</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: MBPP</li> <li aria-level="2">Category: Model Efficiency</li> <li aria-level="2">Cause: Model does not have enough sample in dataset</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low sample efficiency metric in models. This alert is used for models that specialize in providing efficient code responses for given prompts.</p> <p> </p> <p><b>Weighted Accuracy Alert</b></p> <ul> <li aria-level="1">Name: Model’s weighted accuracy is too low <ul> <li aria-level="2">Description: Number of correct answers chosen by model weighted is significantly less than total number of questions for task</li> <li aria-level="2">Metric/Metrics: Weighted Accuracy</li> <li aria-level="2">Range, Type: Percentage, greater than 70%</li> <li aria-level="2">Recommendation: Train model to learn information from subjects better</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: MMLU</li> <li aria-level="2">Category: Model Accuracy</li> <li aria-level="2">Cause: Model did not learn for sample dataset well</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low weighted accuracy metric in models. This alert is used for models that specialize in answering questions and providing factual information.</p> <p> </p> <p><b>Subject-wise Accuracy Alert</b></p> <ul> <li aria-level="1">Name: Model’s Subject-wise Accuracy is too low <ul> <li aria-level="2">Description: Model is not accurate when answering questions from one or more subjects</li> <li aria-level="2">Metric/Metrics: Subject-wise Accuracy</li> <li aria-level="2">Range, Type: Percentage, greater than 65%</li> <li aria-level="2">Recommendation: Train model to learn information from specific subjects better</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: MMLU</li> <li aria-level="2">Category: Model Accuracy</li> <li aria-level="2">Cause: Model did not learn of specific subjects from sample dataset well</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low subject-wise accuracy metric in models. This alert is used for models that specialize in answering questions, showing knowledge in multiple fields, and providing factual information.</p> <p> </p> <p><b>Macro-average Accuracy Alert</b></p> <ul> <li aria-level="1">Name: Model’s Macro-average Accuracy is too low <ul> <li aria-level="2">Description: Model is not accurate for each task disregarding number of questions</li> <li aria-level="2">Metric/Metrics: Macro-average Accuracy</li> <li aria-level="2">Range, Type: Percentage, greater than 70%</li> <li aria-level="2">Recommendation: Train model to learn information from subjects better</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: MMLU</li> <li aria-level="2">Category: Model Accuracy</li> <li aria-level="2">Cause: Model did not learn for sample dataset well</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low macro-average accuracy metric in models. This alert is used for models that specialize in answering questions, showing knowledge in multiple fields, and providing factual information.</p> <p> </p> <p><b>Micro-average Accuracy Alert</b></p> <ul> <li aria-level="1">Name: Model’s Micro-average Accuracy is too low <ul> <li aria-level="2">Description: Model is not accurate for each task disregarding content of tasks</li> <li aria-level="2">Metric/Metrics: Micro-average Accuracy</li> <li aria-level="2">Range, Type: Percentage, greater than 70%</li> <li aria-level="2">Recommendation: Train model to learn information from subjects better</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: MMLU</li> <li aria-level="2">Category: Model Accuracy</li> <li aria-level="2">Cause: Model did not learn for sample dataset well</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low micro-average accuracy metric in models. This alert is used for models that specialize in answering questions, showing knowledge in multiple fields, and providing factual information.</p> <p> </p> <p><b>Exact Match Alert</b></p> <ul> <li aria-level="1">Name: Not enough answers from model match correct answers for questions <ul> <li aria-level="2">Description: Model is not making enough correct responses</li> <li aria-level="2">Metric/Metrics: Exact Match</li> <li aria-level="2">Range, Type: Percentage, greater than 75%</li> <li aria-level="2">Recommendation: Train model with better dataset</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: TriviaQA, RAGAs</li> <li aria-level="2">Category: Response matching</li> <li aria-level="2">Cause: Model did not learn for the training dataset well</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low number of exact matches in a model. This alert is used for models that specialize in answering questions and providing correct information.</p> <p> </p> <p><b>Precision Alert</b></p> <ul> <li aria-level="1">Name: Model’s precision value is too low <ul> <li aria-level="2">Description: Model’s number of positive predictions out of predicted instances is too low</li> <li aria-level="2">Metric/Metrics: Precision</li> <li aria-level="2">Range, Type: Percentage, greater than 80%</li> <li aria-level="2">Recommendation: Train model to better understand question and to better gather data from training dataset</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: TriviaQA, MLFlow LLM Evaluate, RAGAs, Arize Phoenix, DeepEval</li> <li aria-level="2">Category: Model Accuracy</li> <li aria-level="2">Cause: Model did not gather information or understand questions well</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low precision metric in models. This alert is used for models that specialize in answering questions and providing predictions.</p> <p> </p> <p><b>Recall Alert</b></p> <ul> <li aria-level="1">Name: Model’s recall value is too low <ul> <li aria-level="2">Description: Model’s number of positive predictions out of actual instances is too low</li> <li aria-level="2">Metric/Metrics: Recall</li> <li aria-level="2">Range, Type: Percentage, greater than 80%</li> <li aria-level="2">Recommendation: Train model to better understand question and to better gather data from training dataset</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: TriviaQA, MLFlow LLM Evaluate, RAGAs, Arize Phoenix, DeepEval</li> <li aria-level="2">Category: Model Accuracy</li> <li aria-level="2">Cause: Model did not gather information or understand questions well</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low recall metric in models. This alert is used for models that specialize in answering questions and providing predictions.</p> <p> </p> <p><b>Answer Length Alert</b></p> <ul> <li aria-level="1">Name: Model’s answer length is too short <ul> <li aria-level="2">Description: Model’s responses to given question is too short</li> <li aria-level="2">Metric/Metrics: Answer length</li> <li aria-level="2">Range, Type: around average length of answers</li> <li aria-level="2">Recommendation: Train model to provide longer answers for responses</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: TriviaQA</li> <li aria-level="2">Category: Question and Answer</li> <li aria-level="2">Cause: Sample dataset given to model provides too many short answers</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a short answer length metric in models. This alert is used for models that specialize in providing long answers.</p> <p> </p> <p><b>BLEU Score Alert </b></p> <ul> <li aria-level="1">Name: Model’s BLEU score is too low <ul> <li aria-level="2">Description: Model’s response in comparison to reference texts is not good quality</li> <li aria-level="2">Metric/Metrics: BLEU Score</li> <li aria-level="2">Range, Type: Decimal, between 0 and 1, greater than 0.75</li> <li aria-level="2">Recommendation: Train model to understand reference texts better</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: MLFlow LLM Evaluate, RAGAs, DeepEval</li> <li aria-level="2">Category: Reference texts</li> <li aria-level="2">Cause: Model did not learn from training reference texts well</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low BLEU metric in models. This alert is used for models that specialize in referencing texts.</p> <p> </p> <p><b>ROGUE Score Alert </b></p> <ul> <li aria-level="1">Name: Model’s ROGUE score is too low <ul> <li aria-level="2">Description: Model’s response in comparison to reference texts is not similar</li> <li aria-level="2">Metric/Metrics: ROUGE Score</li> <li aria-level="2">Range, Type: Decimal, between 0 and 1, greater than 0.75</li> <li aria-level="2">Recommendation: Train model to understand reference texts better</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: MLFlow LLM Evaluate, RAGAs, DeepEval</li> <li aria-level="2">Category: Reference texts</li> <li aria-level="2">Cause: Model did not learn from training reference texts well</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low ROUGE metric in models. This alert is used for models that specialize in referencing texts.</p> <p> </p> <p><b>Mean Square Error Alert</b></p> <ul> <li aria-level="1">Name: Model’s Mean Square Error is too high <ul> <li aria-level="2">Description: Model’s predicted value compared to actual value has a large difference</li> <li aria-level="2">Metric/Metrics: Mean Square Error</li> <li aria-level="2">Range, Type: Decimal, less than 0.05</li> <li aria-level="2">Recommendation: Train model to understand reference texts better</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: MLFlow LLM Evaluate, Arize Phoenix, DeepEval</li> <li aria-level="2">Category: Reference texts</li> <li aria-level="2">Cause: Model did not learn from training reference texts well</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a high Mean Square Error metric in models. This alert is used for models that specialize in referencing texts.</p> <p> </p> <p><b>Retrieval Accuracy Alert</b></p> <ul> <li aria-level="1">Name: Model’s retrieval accuracy is too low <ul> <li aria-level="2">Description: Model is not providing useful information based on retrieval texts</li> <li aria-level="2">Metric/Metrics: Retrieval Accuracy</li> <li aria-level="2">Range, Type: Percentage, greater than 90%</li> <li aria-level="2">Recommendation: Train model to understand how to retrieve texts better</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: RAGAs</li> <li aria-level="2">Category: Reference texts, Model accuracy</li> <li aria-level="2">Cause: Model is not retrieving texts well</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low retrieval accuracy metric in models. This alert is used for models that specialize in referencing texts.</p> <p> </p> <p><b>Area Under the Curve Alert</b></p> <ul> <li aria-level="1">Name: Model’s Area Under the Curve is too low <ul> <li aria-level="2">Description: Model’s cannot distinguish classes well</li> <li aria-level="2">Metric/Metrics: Area Under the Curve</li> <li aria-level="2">Range, Type: Decimal, between 0 and 1, greater than 0.9</li> <li aria-level="2">Recommendation: Train model to better detect classes</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: Arize Phoenix, DeepEval</li> <li aria-level="2">Category: Model Evaluation</li> <li aria-level="2">Cause: Model cannot detect unique features for each class</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low retrieval accuracy metric in models. This alert is used for models that specialize in referencing texts.</p> <p> </p> <p><b>Latency Alert</b></p> <ul> <li aria-level="1">Name: Model’s Latency value is too high <ul> <li aria-level="2">Description: Model is taking too long to make predictions</li> <li aria-level="2">Metric/Metrics: Latency</li> <li aria-level="2">Range, Type: Integer, less than 200 ms</li> <li aria-level="2">Recommendation: Improve model’s algorithm to improve latency time</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: Arize Phoenix, DeepEval</li> <li aria-level="2">Category: Model runtime</li> <li aria-level="2">Cause: Model algorithm is too inefficient</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a high latency metric in models. This alert is used in the majority of models to check if the model is running quickly and for monitoring model performance.</p> <p> </p> <p><b>Uptime Alert</b></p> <ul> <li aria-level="1">Name: Model’s uptime value is too low <ul> <li aria-level="2">Description: Model is not running all the time</li> <li aria-level="2">Metric/Metrics: Uptime</li> <li aria-level="2">Range, Type: Percentage, greater than 99.9%</li> <li aria-level="2">Recommendation: Fix failures in model to improve model’s performance</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: Arize Phoenix</li> <li aria-level="2">Category: Model runtime</li> <li aria-level="2">Cause: Model is not operating due to failures in model</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a low uptime metric in models. This alert is used in the majority of models to check if the model is running quickly and for monitoring model performance.</p> <p> </p> <p><b> Data Drift Alert</b></p> <ul> <li aria-level="1">Name: Model’s data drift value too high <ul> <li aria-level="2">Description: Input Data changes too much overtime</li> <li aria-level="2">Metric/Metrics: Data Drift</li> <li aria-level="2">Range, Type: Minimal drift</li> <li aria-level="2">Recommendation: Provide data to model that is consistent to previous data inputted</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: Arize Phoenix</li> <li aria-level="2">Category: Data input</li> <li aria-level="2">Cause: Data provided to model is too different from previous data</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a high data drift metric in models. This alert is used in models that specialize in data collection and for providing predictions.</p> <p> </p> <p><b>Model Drift Alert </b></p> <ul> <li aria-level="1">Name: Model’s model drift value too high <ul> <li aria-level="2">Description: Model’s predictions are not consistent</li> <li aria-level="2">Metric/Metrics: Model Drift</li> <li aria-level="2">Range, Type: Minimal drift</li> <li aria-level="2">Recommendation: Train model to better detect trends in dataset</li> <li aria-level="2">Severity: Major</li> <li aria-level="2">Class: Arize Phoenix</li> <li aria-level="2">Category: Model predictions</li> <li aria-level="2">Cause: Model is not able to understand trends in dataset</li> <li aria-level="2">Provider{Library}: Metric Evaluation</li> </ul> </li> </ul> <p>This alert is used for detecting a high model drift metric in models. This alert is used in models that specialize in data collection and for providing predictions.</p> </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="wpb_raw_code wpb_content_element wpb_raw_html" > <div class="wpb_wrapper"> <table><thead> <tr> <th>Evaluation Name</th> <th>Description</th> <th>General Insights</th> <th>Risk/Security/Vulnerability</th> <th>Metric Type/Boolean/Analogous</th> <th>Range, Recommended Value</th> </tr></thead> <tbody> <tr> <td>TruthfulQA</td> <td>Benchmark to measure how truthful LLM is when generating answers to questions<br><br>38 categories with 817 questions<br><br>Questions are crafted in such a way that humans would answer incorrectly due to misconceptions or false beliefs</td> <td>Determines how truthful a model is<br><br>Used in the medical, legal, and educational fields to check where factual correctness<br><br>Balancing informativeness and truthfulness major challenge since isolating incorrect information is tricky</td> <td>Larger models are less truthful but more informative<br><br>Hard to determine correct answer due to questions being worded in a tricky way<br><br>Model can provide inappropriate or offensive answers<br><br>Model can make up facts based on misinterpretations</td> <td>Truthfulness(Percentage)<br>- Number of accurate answers the model made out of the total accurate answers<br><br>Informative Accuracy(Percentage)<br> - Number of informative answers the model made out of the total accurate answers<br><br>Correctness(Percentage)<br>- Number of correct contextual answers the model made out of the total accurate answers<br><br>Consistency(Percentage)<br>- Number of consistent answers the model made in regards to given question out of the total accurate answers<br><br>Coverage(Percentage)<br>- Number of topics the model can provide good answers out of total topics<br><br>Calibration(Percentage)<br>- How confident a model is out of its actual accuracy</td> <td>Truthfulness >= 85%<br><br>Informative Accuracy >= 80%<br><br>Correctness >= 85%<br><br>Consistency >= 90%<br><br>Coverage >= 90%<br><br>Calibration <= 0.05</td> </tr> <tr> <td>HellaSwag</td> <td>Challenge dataset for evaluating commonsense<br><br>Used to test NLP models<br><br>Contains a list of questions and multiple choice answers</td> <td>Determines how much commonsense a model has<br> Used to enhance model's ability to understand humans and logical thinking<br><br>Used to generate text that meet human expectations<br><br>Difficult for model to improve and understand context of a given scenario<br><br>Model may have difficulties predicting next steps accurately for given situation</td> <td>Model can misinterpret the context of the questions<br><br>Model can provide inappropriate or offensive answers<br><br>Model may not be able to differentiate different types of commonsense</td> <td>Accuracy(Percentage)<br>- Number of correct answers chosen by model out of total number of questions<br><br>Perplexity((Integer)<br>- Models' ability to predict probability distribution of data compared to data's actual distribution<br><br>Log-Likelihood(Decimal)<br>- Log-Probability of model choosing correct answer<br><br>F1 Score(Decimal between 0 or 1)<br>- Accuracy of the model using both false positives and false negatives</td> <td>Accuracy >= 85%<br><br>Perplexity <= 10<br><br>Log-Likelihood: Higher log-likelihood<br><br>Log-Probability: higher log-probability<br><br>F1 Score >= 85%</td> </tr> <tr> <td>BIG</td> <td>Intended to probe LLM and extend application of LLM future capabilities<br><br>Includes more than 200 tasks<br><br>Provides view containing model's performance across various tasks</td> <td>Determines how much information is gained from model's responses<br>Used for programs and applications including ai assistants and educational tools that require ability to learn new information<br>Model may have difficulties giving responses that coherent and relevant while providing information and high quality responses</td> <td>Excessive includes more than 200 tasks<br><br>Excessive variance of performance across various tasks<br><br>Insufficient probe of LLM future capabilities</td> <td>Information Gain(Bits)<br>- the amount of new information model has learned based on model's response<br><br>Response Quality(Integer between 1 to 5)<br> - relevance, informativeness, and clarity of model's response<br><br>Accuracy(Percentage)<br> - Number of correct answers chosen by model out of total<br> - How factually correct the model's answers are<br><br>Coverage(Percentage)<br> - Number of topics the model can provide good answers out of total topics<br><br>Consistency(Percentage) - Number of consistent answers the model made in regards to given question out of the total accurate answers</td> <td>Information Gain >= 80%<br><br>Response Quality >= 80%<br>Accuracy >= 80%<br><br>Coverage >= 80%<br><br>Consistency >= 85%</td> </tr> <tr> <td>MBPP</td> <td>1000 crowd-sourced Python programming problems<br><br>solvable by entry level programmers<br><br>problems consist of task description, code solution, and 3 automated test cases</td> <td>Determines how well a model can generate correct Python Code<br><br>Used for coding assistants, educational tools, and automated code generation<br><br>Model may not generate code that is syntactically correct and logically sound that needs to meets specific requirements</td> <td>Not enough test cases to assess whether model’s answer is correct or not<br><br>Model may generate malicious code<br><br>Model can generate code that is logically correct but semantically incorrect<br><br>The code the model generates can contain vulnerabilities</td> <td>Correctness(Percentage)<br>- Number of correct functional Python code answers the model made out of the total number of tasks<br><br>Execution Accuracy(Percentage)<br>- Number of functional Python code answers the model made that run without errors out of the total number of code snippets the model made<br><br>Pass@k(Percentage)<br>- Probability of one out of the k Python code answers the model made that passes all the test cases for a task given<br><br>Code Quality(Integer between 1 to 5)<br>- score that assesses readability, maintainability, and quality of code model made<br>- higher score indicates the quality is better<br><br>Sample Efficiency(Percentage)<br>- number of samples the model needs to generate a correct solution for the given task<br>- lower value means the model is more efficient</td> <td>Correctness >= 75%<br><br>Execution Accuracy >= 70%<br><br>Pass @k: Percentage increases when k value is larger<br><br>Code Quality >= 70%<br><br>Sample Efficiency: High sample efficiency</td> </tr> <tr> <td>MMLU</td> <td>measure knowledge acquired during pretraining by evaluating models specifically during zero-shot and few-shot settings<br><br>more challenging but similar to how humans are evaluated<br><br>covers 57 STEM subjects<br><br>ranges in difficulty from elementary level to advanced professional level<br><br>granularity and breadth of subjects ideal for identifying blind spots for model</td> <td>Determines the performance of a model by testing the model's knowledge across various subjects<br><br>Used to develop models that can handle questions and queries from various topics<br><br>Difficult for model to achieve good performance across all subjects</td> <td>Model can make up facts based on misinterpretations<br><br>Model may leak private data if this data is not properly secured<br><br>Model may struggle to generalize across tasks<br><br>Model is vulnerable to adversarial attacks</td> <td>Accuracy(Percentage)<br>- Number of correct answers chosen by model out of total number of questions<br><br>Weighted Accuracy(Percentage)<br>- Number of correct answers chosen by model weighted by the total number of questions for each task<br><br>Subject-wise Accuracy(Percentage)<br>- How accurate the model is when answering questions from a specific subject<br><br>Macro-average Accuracy(Percentage)<br>- How accurate a model is for each task disregarding the number of questions for each task<br>- Useful for understanding performance consistency for all tasks<br><br>Micro-average Accuracy(Percentage)<br>- How accurate a model is for each task disregarding the content of the question and the task the question belongs to<br>- Useful for measuring performance of the model for all tasks</td> <td>Accuracy >= 70%<br><br>Weighted Accuracy >= 70%<br>Subject-wise Accuracy >= 65% per subject<br><br>Macro-average Accuracy >= 70%<br><br>Micro-average Accuracy >= 70%</td> </tr> <tr> <td>TriviaQA</td> <td>reading comprehension dataset<br><br>contains over 650,000 question-answer-evidence triples<br><br>contains 95,000 question answer pairs<br><br>contains human-verified and machine-generated QA subsets</td> <td>Determines how well a model can answer trivia questions<br><br>Used for create applications such as quiz games, trivia application, and ai assistants<br><br> Model may have difficulties providing precise and knowledgeable answer while aiming for a high accuracy</td> <td>Model can provide inappropriate or offensive answers<br><br>Model can make up facts based on misinterpretations<br><br>Model may reveal private data if private data is included in training set</td> <td>Exact Match(Percentage)<br>- Number of answers from the model that exactly matches the correct answers out of the total number of questions<br>- Helps the model measure how precise correct answers are<br><br>F1 Score(Decimal between 0 or 1)<br>- Accuracy of the model using both false positives and false negatives<br><br>Precision(Percentage)<br>- Number of correct positive predictions model makes out of total predicted positive instances<br><br>Recall(Percentage)<br>- Number of correct positive predictions model makes out of total actual positive instances<br><br>Answer Length(Number of Words or Characters)<br>- average length of a model's answer<br>- helps understand if model is telling concise or long answers</td> <td>Exact Match >= 75%<br><br>F1 Score >= 80%<br><br>Precision >= 80%<br><br>Recall >= 80%<br><br>Answer length: around average length of answers</td> </tr> <tr> <td>HumanEval</td> <td>used to measure functional correctness for synthesizing programs from docstrings<br><br>contains 164 problems<br><br>covers programming, language comprehension, algorithms, simple mathematics, software interview questions</td> <td>Determines how well a model can generate correct and functional code for a programming question<br><br>Used for coding assistants, educational tools, and automated code generation<br><br>Model may have difficulties providing code that is a reliable and efficient for programming questions</td> <td>Model may generate malicious code<br><br>Model can generate code that is logically correct but semantically incorrect<br><br>The code the model generates can contain vulnerabilities</td> <td>Pass@k(Percentage)<br>- Probability of one out of the k code answers the model made that passes all the test cases for a task given<br><br>Correctness(Percentage)<br>- Number of correct functional code answers the model made out of the total number of tasks<br><br>Execution Accuracy(Percentage)<br>- Number of functional code answers the model made that run without errors out of the total number of code snippets the model made<br><br>Code Quality(Integer between 1 to 5)<br>- score that assesses readability, maintainability, and quality of code model made<br>- higher score indicates the quality is better<br><br>Sample Efficiency(Percentage)<br>- number of samples the model needs to generate a correct solution for the given task<br>- lower value means the model is more efficient</td> <td>Pass @k: Percentage increases when k value is larger<br><br>Correctness >= 70%<br><br>Execution Accuracy >= 65%<br><br>Code Quality >= 70%<br><br>Sample Efficiency: High sample efficiency</td> </tr> <tr> <td>MLFlow LLM Evaluate</td> <td>Open Source Library<br><br>MLFlow LLM Evaluate<br><br>Evaluation functionality comprised of 3 main components<br>- model to evaluate<br>- metrics<br>- evaluation data<br><br><br>mel to evaluate<br><br>metrics<br><br>evaluation data</td> <td>Determines how well a model performs for various metrics<br><br>Useful for monitoring and improving a model's performance<br><br>Model may have difficulties balancing multiple metrics to achieve a good performance</td> <td>Model may reveal private data if private data is included in training set<br><br>Models that are evaluated may be vulnerable to adversarial attacks<br><br>Models that are known to have vulnerabilities may be insecure</td> <td>Accuracy(Percentage)<br>- Number of correct answers chosen by model out of total number of questions<br><br>Precision(Percentage)<br>- Number of correct positive predictions model makes out of total predicted positive instances<br><br>Recall(Percentage)<br>- Number of correct positive predictions model makes out of total actual positive instances<br><br>F1 Score(Decimal between 0 or 1)<br>- Accuracy of the model using both false positives and false negatives<br><br>Perplexity((Integer)<br>- Models' ability to predict probability distribution of data compared to data's actual distribution<br><br>BLEU Score(Decimal between 0 or 1)<br>- measure the quality of text the model generates by comparing the model's text with one or more reference texts<br><br>ROUGE Score(Decimal between 0 or 1)<br>- measures how similar the generated text and reference text are based on the model's ability to evaluate summarization and translation<br>- higher the scores mean the model is summarize and translating better<br><br>Mean Square Error(Decimal)<br>- average of square differences between a model's predicted and actual values</td> <td>Evaluate Accuracy >= 85%<br><br>Evaluate Precision >= 85%<br><br>Evaluate Recall >= 85%<br><br>Evaluate F1 Score >= 85%<br><br>Evaluate BLEU Score >= 0.75<br><br>Evaluate ROUGE Score >= 0.75<br><br>Evaluate Mean Square Error <= 0.05</td> </tr> <tr> <td>RAGAs</td> <td>Open Source Library<br><br>framework that helps evaluate pipelines involving RAG<br><br>class of LLM applications that use external data for augmenting LLM's context</td> <td>Determines how well a model can retrieve documents and provide accurate answers<br><br>Used for applications involving customer service, question-answer retrieval, and information gathering<br><br>Model may have difficulties doing both information retrieval and coherent answer generation for responses that require accuracy</td> <td>Model may misinterpret information lead to misleading output<br><br>If model has access to confidential data, model may leak that information<br><br>Model can be manipulated to provide harmful information</td> <td>Accuracy(Percentage)<br>- Number of correct answers chosen by model out of total number of questions<br><br>Exact Match(Percentage)<br>- Number of answers from the model that exactly matches the correct answers out of the total number of questions<br>- Measures the correctness of the model's answers<br><br>F1 Score(Decimal between 0 or 1)<br>- Accuracy of the model using both false positives and false negatives<br><br>Precision(Percentage)<br>- Number of correct positive predictions model makes out of total predicted positive instances<br><br>Recall(Percentage)<br>- Number of correct positive predictions model makes out of total actual positive instances<br><br>BLEU Score(Decimal between 0 or 1)<br>- measure the quality of text the model generates by comparing the model's text with one or more reference texts<br><br>ROUGE Score(Decimal between 0 or 1)<br>- measures how similar the generated text and reference text are based on the model's ability to evaluate summarization and translation<br>- higher the scores mean the model is summarize and translating better<br><br>Retrieval Accuracy(Percentage)<br>- how effective a model is at providing useful information using documents the model retrieves out of the total documents</td> <td>Accuracy >= 85%<br><br>Exact Match >= 80%<br><br>F1 Score >= 85%<br><br>Precision >= 90%<br><br>Recall >= 90%<br><br>BLEU Score >= 0.75<br><br>ROUGE Score >= 0.75<br><br>Retrieval Accuracy >= 90%</td> </tr> <tr> <td>Arize Phoenix</td> <td>Open Source Library<br><br>Designed for experimentation, evaluation, and troubleshooting<br><br>allows users to visualize data, evaluate model performance, track issues, and export data for improvement easily</td> <td>Determines the model's ability to monitor and evaluate models that are deployed by evaluating certain metrics including accuracy, precision, and data drift<br><br>Useful for monitoring and enhancing model's performance<br><br>Model may have difficulties detecting and minimizing certain metric issues including data drift and model drift</td> <td>Model may be sensitive in detecting issues in a model leading to false alarms<br><br>Private data can be leaked from model if its not secured properly<br><br>Changes in input distribution can lead to model performing poorly</td> <td>Accuracy(Percentage)<br>- Number of correct predictions chosen by model out of total number of predictions<br><br>Precision(Percentage)<br>- Number of correct positive predictions model makes out of total predicted positive instances<br><br>Recall(Percentage)<br>- Number of correct positive predictions model makes out of total actual positive instances<br><br>F1 Score(Decimal between 0 or 1)<br>- Accuracy of the model using both false positives and false negatives<br><br>Mean Square Error(Decimal)<br>- average of square differences between a model's predicted and actual values<br><br>Area Under the Curve(Decimal between 0 to 1)<br>- measures the model's ability to distinguish classes<br>- higher AUC means the model is performing better<br><br>Latency(Integer in seconds or milliseconds)<br>- How long a model takes to make a prediction<br>- Lower value indicates the model is performing faster<br><br>Uptime(Percentage)<br>- Proportion of time model is operating and open out of total time<br>- Higher value means model is more reliable and available<br><br>Data Drift(Integer)<br>- Measures how much the input data changes over time compared to the data model used for training<br>- Lower score means the input data is very similar to the training data<br> <br>Model Drift(Integer)<br>- Measures how much the model's predictions changes over time<br>- Lower score means the model's predictions are consistent</td> <td>Accuracy >= 85%<br><br>Precision >= 85%<br><br>Recall >= 85%<br><br>F1 Score >= 85%<br><br>Mean Square Error <= 0.05<br><br>Area Under the Curve >= 0.90<br><br>Latency <= 200 ms<br><br>Uptime >= 99.9%<br><br>Data Drift: Minimal drift<br><br>Model Drift: Minimal drift</td> </tr> <tr> <td>DeepEval</td> <td>Open Source Library<br><br>Easy to build and iterate on LLM<br><br>Built under following principles<br>- unit tests LLM outputs<br>- plug and use 14 or more evaluation metrics for LLM<br>- dataset generation that is synthetic with evolution techniques<br>- simple customizable metrics and covers all use cases<br>- real-time evaluations in production</td> <td>Evaluates the framework for language models by using multiple performance metrics<br><br>Useful for assessments for models that are comprehensive<br><br>Model may have difficulties balancing different metrics for creating a model that is reliable and robust</td> <td>Private data can be leaked from model if its not secured properly<br><br>Data can be leaked if data in the test dataset is not isolated from the training dataset<br><br>Models that are evaluated may be vulnerable to adversarial attacks</td> <td>Accuracy(Percentage)<br>- Number of correct predictions chosen by model out of total number of predictions<br><br>Precision(Percentage)<br>- Number of correct positive predictions model makes out of total predicted positive instances<br><br>Recall(Percentage)<br>- Number of correct positive predictions model makes out of total actual positive instances<br><br>F1 Score(Decimal between 0 or 1)<br>- Accuracy of the model using both false positives and false negatives<br><br>Perplexity((Integer)<br>- Models' ability to predict probability distribution of data compared to data's actual distribution<br><br>BLEU Score(Decimal between 0 or 1)<br>- measure the quality of text the model generates by comparing the model's text with one or more reference texts<br><br>ROUGE Score(Decimal between 0 or 1)<br>- measures how similar the generated text and reference text are based on the model's ability to evaluate summarization and translation<br>- higher the scores mean the model is summarize and translating better<br><br>Mean Square Error(Decimal)<br>- average of square differences between a model's predicted and actual values<br><br>Area Under the Curve(Decimal between 0 to 1)<br>- measures the model's ability to distinguish classes<br>- higher AUC means the model is performing better<br><br>Latency(Integer in seconds or milliseconds)<br>- How long a model takes to make a prediction<br>- Lower value indicates the model is performing faster</td> <td>Accuracy >= 85%<br><br>Precision >= 85%<br><br>Recall >= 85%<br><br>F1 Score >= 85%<br><br>Perplexity <= 20<br><br>BLEU Score >= 0.75<br><br>ROUGE Score >= 0.75<br><br>Mean Square Error <= 0.05<br><br>Area Under the Curve >= 0.90<br><br>Latency <= 200 ms</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="wpb_text_column wpb_content_element " > <div class="wpb_wrapper"> <p><b>Conclusion</b></p> <p> </p> <p><b>Alert AI </b></p> <p>What is at stake AI & Gen AI in Business? We are addressing exactly that. Generative AI security solution for Healthcare , Pharma, Insurance, Life Sciences, Retail, Banking, Finance, Manufacturing.</p> <p>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.</p> <p>Despite the Security challenges, the promise of large language models is enormous.<br /> We are committed to enabling industries and enterprises to reap the benefits of large language models.</p> </div> </div> </div> </div> </div> </div> </div> <div class="clearfix"></div> </div> </div> </article>    </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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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-67418100f33f0" 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 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-6741810100287" 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 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, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-2-WHITE-BACKGROUND-300x300.png 300w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-2-WHITE-BACKGROUND-1024x1024.png 1024w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-2-WHITE-BACKGROUND-768x768.png 768w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-2-WHITE-BACKGROUND-1536x1536.png 1536w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-2-WHITE-BACKGROUND-500x500.png 500w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-2-WHITE-BACKGROUND-1000x1000.png 1000w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-2-WHITE-BACKGROUND-1140x1140.png 1140w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-2-WHITE-BACKGROUND-848x848.png 848w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-2-WHITE-BACKGROUND-320x320.png 320w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-2-WHITE-BACKGROUND-480x480.png 480w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-2-WHITE-BACKGROUND-800x800.png 800w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-2-WHITE-BACKGROUND.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">“We are in a world growing increasingly more dangerous..<br /> Threat actors strike and steal intelligence, seize and derail operations..``</span><img loading="lazy" decoding="async" width="150" height="150" src="https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-3-WHITE-BACKGROUND-150x150.png" class="attachment-thumbnail size-thumbnail" alt="ALERT AI 3 WHITE BACKGROUND" srcset="https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-3-WHITE-BACKGROUND-150x150.png 150w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-3-WHITE-BACKGROUND-300x300.png 300w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-3-WHITE-BACKGROUND-1024x1024.png 1024w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-3-WHITE-BACKGROUND-768x768.png 768w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-3-WHITE-BACKGROUND-1536x1536.png 1536w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-3-WHITE-BACKGROUND-500x500.png 500w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-3-WHITE-BACKGROUND-1000x1000.png 1000w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-3-WHITE-BACKGROUND-1140x1140.png 1140w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-3-WHITE-BACKGROUND-848x848.png 848w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-3-WHITE-BACKGROUND-320x320.png 320w, https://alertai.com/wp-content/uploads/2024/06/ALERT-AI-3-WHITE-BACKGROUND-480x480.png 480w, 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Manufacturing, Government.</p> <p>With over 100+ integrations, thousands of detections, easy to deploy and manage services Alert AI seamlessly integrates to provide 360 degrees Visibility, Vulnerability management, Adversarial threat detection , Privacy, Integrity, Trust, AI risks in Business.</p> <p>Enhance, Optimize, Manage security of Generative AI applications with Alert AI domain-specific security guardrails.</p> </div> </div><div class="widget widget_block"><div class="lnkdn_buttons"><div class="lnkdn-share-button"> <script type="IN/Share" data-url="https://alertai.com/llms-genai-pipelines-evaluation-metrics-risks/" data-counter=""></script> </div><div class="lnkdn-follow-button"> <script type="IN/FollowCompany" data-id="104405749" data-counter="right"></script> </div></div></div> </div> <div class="col-sm-6 col-lg-3"> <div class="widget wbc-recent-post-widget"><h4 class="widget-title">Top concerns | AI security</h4><ul class="wbc-recent-post-list"><li><div 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the integration of LLM Evaluation with Pip...</p></div></li></ul></div> </div> <div class="col-sm-6 col-lg-3"> <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" 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