[{"data":1,"prerenderedAt":585},["ShallowReactive",2],{"uc-trust-and-safety-content-moderation":3,"uc-regulations":365},{"useCase":4,"evidence":168,"blitsAiDeployments":247,"benchmarks":248,"indicative":255,"related":257,"indexability":363,"includeUnpublished":174},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":21,"patterns":24,"channels":28,"audience":31,"autonomy":32,"adoptionStage":33,"problem":34,"problemStats":35,"howItWorks":36,"valueDrivers":37,"kpis":42,"indicativeValue":46,"macroEstimates":74,"feasibility":75,"implementation":87,"risk":125,"blitsAi":146,"faq":148,"related":161,"datePublished":162,"dateModified":162,"lastVerified":163,"changelog":164,"slug":167},"AI content moderation for trust and safety at online platforms","Trust and safety moderation","AI content moderation for trust and safety","Hive Moderation reports Chatroulette cut inappropriate content incidence by over 95% and Tango cut harmful content exposure by over 80%.","published","AI that scans user generated content, such as live video, images, chat and audio, against a platform's own policy in real time, removes or hides the clearest violations, and routes borderline cases to a human moderator with its reasoning attached, so a platform can review far more content than a human team alone while keeping irreversible decisions with a person.",[12,13,14,15,16],"trust and safety AI","AI content moderation","automated content moderation","livestream moderation AI","platform safety AI",[18,19,20],"technology","media-and-entertainment","cross-industry",[22,23],"security-operations","case-management",[25,26,27],"computer-vision","classification-and-routing","anomaly-detection",[29,30],"api","internal-tools","back-office","supervised-agent","mainstream","Platforms that let people post, chat or stream in real time cannot review that volume by hand.\nA live video feed produces a new frame to check every fraction of a second, and a chat or comment\nsection can carry harassment, scams, CSAM, grooming or self harm content that needs to be\ncaught in seconds, not after a user reports it. Purely human moderation teams cannot keep pace\nwith this volume, and the delay between a violation appearing and a person reviewing it is itself\nthe harm: an exposed child, a scam that already collected money, a stream nobody removed before it\nwas screenshotted and shared elsewhere.\n\nKeyword lists and simple image hashing catch known, unchanged content but miss context, new\nphrasing and anything in live video or audio. Models that classify text, images, audio and video\nagainst a platform's own policy change what is possible: a violation can be scored and actioned\nwithin a second of appearing, at a volume no human team could review, while the decisions that\naffect someone's account permanently still go through a person.",[],"1. **Ingest content as it is created.** Frames from live video, chat messages, images and audio\n   are pulled into the moderation pipeline as they are posted or streamed, not on a delay.\n2. **Classify against the policy.** Vision, audio and language models score each item against the\n   platform's own violation categories (nudity, violence, harassment, CSAM, grooming, self harm, scams) and\n   return a confidence per category.\n3. **Act by severity and confidence.** The highest confidence, highest severity matches are\n   actioned automatically (blur, mute, end a stream, remove a message); everything else is\n   queued for a human, ranked by risk.\n4. **Give reviewers the reasoning.** A human moderator sees the flagged content, the category and\n   the confidence score, so they decide in seconds instead of watching a whole stream again from\n   the start.\n5. **Enforce and log.** Confirmed violations trigger the platform's own enforcement (strike,\n   mute, ban) with an auditable record of what was actioned, by what and when.\n6. **Support appeals.** A user can contest a decision; a different reviewer than the original\n   check looks at it again, and confirmed false positives feed back into the category thresholds.",[38,39,40,41],"risk-reduction","compliance","customer-experience","cost-to-serve",[43,44,45],"detection-rate-improvement","interactions-handled","cost-reduction",{"referenceOrg":47,"inputs":48,"formula":69,"currency":70,"period":71,"resultLabel":72,"caveat":73},"A social, dating or gaming platform with 10 million monthly active users",[49,55,62],{"key":50,"label":51,"low":52,"high":52,"unit":53,"note":54},"contentItems","User generated content items needing a moderation decision per year",50000000,"items per year","Editorial assumption for a platform of this scale, replace with your own content volume.",{"key":56,"label":57,"low":58,"high":59,"unit":60,"note":61},"reviewCostPerItem","Fully loaded human review cost per item",0.05,0.15,"USD per item","Editorial assumption for outsourced or in house moderation review, replace with your own.",{"key":63,"label":64,"low":65,"high":66,"unit":67,"note":68},"shareAutomated","Share of moderation decisions made without a human review",0.6,0.85,"fraction of items","Editorial assumption, replace with your own; ambiguous content, appeals and any account level action still need a person.","contentItems * shareAutomated * reviewCostPerItem","USD","per year","Manual review cost avoided","Counts only the human reviewer time saved per item on decisions the AI can make alone. It leaves out the cost of the moderation platform itself, the time spent on appeals and escalations, and the larger value: legal exposure, regulatory fines and user trust protected by removing harmful content faster than a human only team could.",[],{"complexity":76,"complexityNote":77,"dataPrerequisites":78,"integrations":82},"medium","Classifying known violation types in text and static images is mature. The effort is in real time video and audio at low latency, tuning thresholds so obvious violations are actioned automatically without over removing borderline but legitimate content, and building the human review and appeals path before launch, not after an incident forces one.",[79,80,81],"A written, versioned content policy with labelled examples per violation category and severity","A dataset of past moderation decisions, including confirmed false positives and appeal outcomes","Age and region flags where the policy differs by jurisdiction or user age",[83,84,85,86],"Content and chat pipelines that carry video, image, text and audio as it is created","A moderation review queue for human moderators, ranked by risk","The platform's own enforcement systems (mute, strike, temporary or permanent restriction)","Analytics and regulator transparency reporting",{"steps":88,"guardrails":104,"humanInTheLoop":109,"kpisToInstrument":110,"failureModes":115},[89,92,95,98,101],{"title":90,"detail":91},"Start from your written policy, not a generic model","Translate your own content policy into labelled examples per category and severity. A model tuned on someone else's definition of a violation will both over and under flag against yours.",{"title":93,"detail":94},"Set automation thresholds by severity, not by content type","Auto action only the highest confidence, highest severity matches; route medium confidence and borderline cases to a ranked human queue, and leave low risk content alone.",{"title":96,"detail":97},"Build the review queue and the appeal path before launch","Reviewers need the flagged content, the model's stated category and confidence, and one click actions. Users need a way to contest a decision: moderation without an appeal path erodes trust, and for an online platform within the scope of the EU Digital Services Act, does not meet the Article 20 internal complaint handling requirement.",{"title":99,"detail":100},"Instrument for evasion, not only accuracy","Track how violation patterns shift after every model update, since people adapting to evade detection change tactics quickly, and keep a manual override channel for new violation types the model has not seen yet.",{"title":102,"detail":103},"Localise thresholds and escalation per region","What counts as a violation, and who must review it, varies by jurisdiction and the age of the people affected. Do not run one global threshold across markets with different rules.",[105,106,107,108],"Human review before any permanent account level sanction; only reversible actions (mute, hide, temporary restriction) are fully automated","Confidence and severity thresholds tuned per category so only high confidence matches are auto actioned","An appeals path reviewed by a different person than the original decision, logged and reportable","Regular audits of false positive and false negative rates, per category and per language","Human moderators own every irreversible enforcement action, every appeal and any decision involving a minor or a law enforcement referral. They also review a sampled set of automated decisions every week to catch drift, and confirmed false positives adjust the category thresholds, not just the individual case.",[111,112,113,114],"Detection rate and false positive rate per violation category and language, on a labelled audit sample","Time from content posted to a violation being actioned","Appeal volume and the share of automated decisions overturned on appeal","Reviewer caseload and time to decision in the human queue",[116,119,122],{"title":117,"detail":118},"Confident wrong removals","The model removes borderline but legitimate content with high confidence, angering users and inviting regulatory scrutiny. Keep an appeals path with a different reviewer, and audit false positives per category on a schedule, not only after a complaint.",{"title":120,"detail":121},"Evasion drift","People adjust language, images or stream behaviour to slip under thresholds within days of a model update. Retrain and tune thresholds on a schedule, and treat a rising queue of unclear content as a signal, not noise.",{"title":123,"detail":124},"One threshold for every market","A single global policy misses local legal definitions of harmful content and who counts as a minor, creating regulatory exposure in some regions. Localise thresholds and escalation paths per jurisdiction.",{"euAiAct":126,"regulations":129,"guidance":133,"controls":140,"incidents":145},{"tier":127,"basis":128},"minimal","Classifying user generated content against a platform's own policy is not listed in Annex III, and the system does not generate or manipulate content, so it carries no use case specific duty under the EU AI Act. Platform content moderation is instead the direct subject of the EU Digital Services Act (Regulation (EU) 2022/2065), a separate regulation. Article 17 requires a statement of reasons for content moderation decisions, and Article 20 requires an online platform within its scope to offer an internal complaint handling system, though Article 19 exempts micro and small enterprises from that duty; see the cited guidance below.",[130,131,132],"eu-ai-act","gdpr","iso-42001",[134],{"title":135,"issuer":136,"region":137,"url":138,"note":139},"Digital Services Act: statement of reasons and internal complaint handling","European Union","europe","https://eur-lex.europa.eu/eli/reg/2022/2065/oj","Regulation (EU) 2022/2065. Article 17 requires providers of hosting services to give a statement of reasons for content moderation decisions. Article 20 requires online platforms to offer an internal complaint handling system for those decisions, but Article 19 exempts micro and small enterprises from that duty.",[141,142,143,144],"A documented, versioned content policy with an owner and a change log","False positive and false negative audits per category and language on a fixed schedule","An internal appeal path for every enforcement action, reviewed by a different person","Human review before any permanent account level sanction",[],{"howToBuild":147},"Blits.ai's guardrail layer is a fit for the text side of this: **guardrails** combine a\ndeterministic, multi language lexicon (hate speech, self harm and violence, plus profanity\ndetection across 25 languages) with an **LLM judged content check** against an admin authored\npolicy, applied to the input and output of a Blits.ai agent conversation. **Custom functions**\ncan call a platform's own enforcement API to action or queue a flagged turn.\n\nBorderline items can go to **human handover**, escalating the conversation to a live agent\nplatform such as Salesforce, Freshdesk or Zoho SalesIQ. **Test suites** run a labelled set of\nknown violation examples through the guardrail configuration after every policy change, and\n**anomaly detection** on agent conversations gives a human a review queue for flagged exchanges.\nThe platform can run in the EU or UAE region for data residency. Real time video and image\nmoderation, and scoring a platform's own chat, comments or messages outside a Blits.ai agent\nconversation, as used in the evidence on this page, needs a vision model and a moderation\npipeline connected as external tools; Blits.ai's guardrails cover the text side of an agent's\nown conversations.",[149,152,155,158],{"question":150,"answer":151},"How much can AI reduce harmful content on a platform?","It depends on the starting point and the content type. Hive Moderation reports that Chatroulette reduced the incidence rate of inappropriate content by over 95% in the three months after integration, Plato reduced user complaints of inappropriate content by more than 90%, and Tango reduced users' exposure to harmful content by more than 80% after continuously deploying new models. These are single vendor reported figures for specific platforms; measure your own before and after on your own content mix.",{"question":153,"answer":154},"Should every moderation decision be automated?","No. Automate the highest confidence, highest severity matches, and route anything ambiguous, or any permanent account action, to a human. An appeal path reviewed by a different person than the original decision is not optional: without one, users have no recourse from a wrong call.",{"question":156,"answer":157},"Is AI content moderation high risk under the EU AI Act?","Content moderation of user generated content is not listed in Annex III, so it is not high risk under the AI Act itself. It is however the direct subject of the EU Digital Services Act (Regulation (EU) 2022/2065), a separate regulation. Article 17 requires a statement of reasons for moderation decisions, and Article 20 requires an online platform within its scope to offer an internal complaint handling system, though Article 19 exempts micro and small enterprises from that duty.",{"question":159,"answer":160},"What causes false positives in AI content moderation, and how should they be handled?","A model can flag borderline but legitimate content with high confidence, especially content that resembles a violation out of context. Tune confidence and severity thresholds per category, audit false positive and false negative rates on a labelled sample on a fixed schedule rather than only after a complaint, and give every user an appeal path reviewed by a different person than the original decision.",[],"2026-09-30","2026-09-29",[165],{"date":162,"note":166},"First published","trust-and-safety-content-moderation",[169,202,222],{"title":170,"useCases":171,"organization":172,"vendors":176,"summary":180,"stage":181,"year":182,"channels":183,"languages":184,"metrics":185,"outcomeDisclosed":186,"sources":187,"verification":197,"grade":199,"id":200,"organizationSlug":201},"Plato: AI moderation for social gaming chat and profile images",[167],{"name":173,"anonymized":174,"region":175,"industry":19},"Plato",false,"global",[177],{"name":178,"role":179},"Hive","platform","Plato, a social gaming community where members play more than 45 games with one another, adopted Hive as its primary moderation solution as its user base grew. Hive analyses profile pictures and group chats against Plato's community guidelines, with an automatic text filter deployed in public group chats. Hive's own case study reports that Plato reduced user complaints of inappropriate content by more than 90%, especially in public group chats after it deployed that automatic filter.","scaled",2021,[29],[],[],true,[188,193],{"url":189,"title":190,"publisher":191,"archivedUrl":192},"https://hivemoderation.com/case-studies/plato","Plato case study","Hive Moderation","https://web.archive.org/web/20210523175130/https://hivemoderation.com/case-studies/plato",{"url":194,"title":195,"publisher":191,"date":196},"https://web.archive.org/web/20210523232633id_/https://hivemoderation.com/js/main.d887b37.js","Hive Moderation site JavaScript bundle (archived), defines the Plato case study route","2021-05-23",{"level":198,"checkedAt":163},"source-verified","C","plato-social-gaming-chat-moderation",null,{"title":203,"useCases":204,"organization":205,"vendors":207,"summary":209,"stage":181,"year":182,"channels":210,"languages":211,"metrics":212,"outcomeDisclosed":186,"sources":213,"verification":220,"grade":199,"id":221,"organizationSlug":201},"Tango: AI moderation for live video streaming",[167],{"name":206,"anonymized":174,"region":175,"industry":19},"Tango",[208],{"name":178,"role":179},"Tango, a live streaming platform with more than 500 million registered users, modernized its legacy in house moderation system with Hive's Vision Language Model and Visual and Text Moderation models to analyse live video streams and chat in real time. The vision language model reads multiple frames of a stream together to catch violations that single frame detection misses, and a text model covers harassment and solicitation in live chat, through a single API. Hive's own case study reports that, since partnering with Hive, Tango has reduced users' exposure to harmful content by more than 80%.",[29],[],[],[214,218],{"url":215,"title":216,"publisher":191,"archivedUrl":217},"https://hivemoderation.com/case-studies/tango","Tango case study","https://web.archive.org/web/20210523175116/https://hivemoderation.com/case-studies/tango",{"url":194,"title":219,"publisher":191,"date":196},"Hive Moderation site JavaScript bundle (archived), defines the Tango case study route",{"level":198,"checkedAt":163},"tango-live-streaming-content-moderation",{"title":223,"useCases":224,"organization":225,"vendors":227,"summary":229,"stage":181,"year":230,"channels":231,"languages":232,"metrics":233,"outcomeDisclosed":186,"sources":242,"verification":245,"grade":199,"id":246,"organizationSlug":201},"Chatroulette: AI moderation for random video chat",[167],{"name":226,"anonymized":174,"region":175,"industry":19},"Chatroulette",[228],{"name":178,"role":179},"Chatroulette, the random video chat service launched in 2009, partnered with Hive in early 2020 as part of a major platform relaunch to implement automated moderation of its live video streams. Hive's visual moderation model flags and closes unsafe streams, and the company's Chief Executive Officer describes user complaints about inappropriate content falling from hundreds a day to fewer than one a week after integration. Hive's own case study reports that the incidence rate of inappropriate content on Chatroulette fell by over 95% in the three months after integration.",2020,[29],[],[234],{"kpi":44,"value":235,"unit":236,"qualifier":237,"period":238,"claimant":239,"quote":240,"sourceUrl":241},1500000,"count","at-least","unsafe streams closed per month","vendor","Hive has helped Chatroulette close over 1.5 million unsafe streams a month with our best-in-class visual moderation model.","https://hivemoderation.com/case-studies/chatroulette",[243],{"url":241,"title":244,"publisher":191},"Chatroulette case study",{"level":198,"checkedAt":163},"chatroulette-video-chat-moderation",0,[249],{"kpi":44,"label":250,"unit":236,"aggregate":174,"higherIsBetter":186,"n":251,"nUpTo":247,"median":235,"min":235,"max":235,"byClaimant":252,"vendorOnly":186,"points":253},"Interactions handled",1,{"organization":247,"vendor":251,"regulator":247,"independent":247},[254],{"evidenceId":246,"organization":226,"value":235,"qualifier":237,"claimant":239,"grade":199,"pooled":186},{"low":235,"high":256},6375000,[258,294,307,338],{"slug":259,"title":260,"shortTitle":261,"definition":262,"status":9,"industries":263,"functions":266,"patterns":267,"audience":268,"autonomy":269,"adoptionStage":270,"evidenceCount":271,"publicEvidenceCount":271,"organizations":272,"bestGrade":285,"headline":286,"lastVerified":163,"indexable":186},"physical-security-video-analytics","AI video analytics and screening for physical security","Physical security video analytics","AI that watches camera feeds or walk through sensors at a site, flags a likely weapon, intrusion or theft in real time or on search, and leaves the verification and every response action to a human guard or investigator, rather than acting on its own.",[264,265,20],"retail-and-ecommerce","education",[22],[25,27,26],"employee-facing","assist","early-adopters",12,[273,274,275,276,277,278,279,280,281,282,283,284],"Allstone Quarries (ASQ)","Tepper Sports & Entertainment","Charlotte-Mecklenburg Schools","Chelsea School District","East Baton Rouge Parish Schools","El Centro Regional Medical Center","GAIL's Bakery","Gwinnett County Public Schools","Harry Rosen","Kogan","Old Cannery Furniture Warehouse","Utica City School District","B",{"kpi":287,"label":288,"unit":289,"n":290,"nUpTo":247,"kind":291,"value":292,"qualifier":293,"claimant":239,"organization":201,"vendorReported":186},"search-time-reduction","Search time reduction","percent",3,"median",90,"exact",{"slug":295,"title":296,"shortTitle":297,"definition":298,"status":9,"industries":299,"functions":300,"patterns":302,"audience":31,"autonomy":32,"adoptionStage":33,"evidenceCount":290,"publicEvidenceCount":290,"organizations":303,"bestGrade":285,"headline":201,"lastVerified":163,"indexable":186},"exam-and-assessment-integrity","AI assisted proctoring and integrity monitoring for remote exams","Exam and assessment integrity","AI that supports the integrity of a remote, high stakes exam by verifying a test taker's identity, analysing behaviour such as typing patterns, facial matching and session activity for signs of impersonation or unauthorized help, and flagging sessions for a trained human reviewer to decide, rather than letting a model issue an automated finding of misconduct on its own.",[265],[301,22],"operations",[27,25,26],[304,305,306],"Duolingo","Educational Testing Service (ETS)","Pearson VUE",{"slug":308,"title":309,"shortTitle":310,"definition":311,"status":9,"industries":312,"functions":316,"patterns":318,"audience":31,"autonomy":32,"adoptionStage":33,"evidenceCount":271,"publicEvidenceCount":320,"organizations":321,"bestGrade":285,"headline":332,"lastVerified":337,"indexable":186},"intelligent-document-processing","AI document intelligence for unstructured forms and documents","Intelligent document processing","AI that takes documents in any format, such as scanned forms, PDFs, photos, emails and handwritten notes, splits and classifies them, extracts the required fields with a confidence score, validates them against business rules and source systems, and sends only the uncertain cases to a person before the data enters the downstream process.",[20,313,314,315],"government","automotive","manufacturing",[301,23,317],"finance-and-accounting",[319,25,26],"document-processing",10,[322,323,324,325,326,327,328,329,330,331],"Ancine","Banorte","CI Financial","Daman (The National Health Insurance Company)","Hirschbach Motor Lines","HM Revenue and Customs","Pupuk Indonesia","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services","Volvo Group",{"kpi":333,"label":334,"unit":289,"n":335,"nUpTo":247,"kind":291,"value":336,"qualifier":293,"claimant":239,"organization":201,"vendorReported":186},"processing-time-reduction","Cycle time reduction",4,60,"2026-09-27",{"slug":339,"title":340,"shortTitle":341,"definition":342,"status":9,"industries":343,"functions":346,"patterns":348,"audience":268,"autonomy":32,"adoptionStage":270,"evidenceCount":352,"publicEvidenceCount":352,"organizations":353,"bestGrade":285,"headline":360,"lastVerified":337,"indexable":186},"security-alert-triage-and-investigation","AI for security alert triage and investigation in the SOC","Security alert triage","An AI agent in the security operations centre that picks up each new alert or user reported phishing email, gathers the evidence from the SIEM, endpoint, identity and threat intelligence tools, gives a verdict with its reasoning and a draft incident summary, and closes clear false positives while an analyst approves every containment action.",[20,344,18,313,345],"healthcare","professional-services",[22,347],"it-and-engineering",[349,26,350,351],"agentic-workflow","summarization","rag-knowledge-assistant",7,[354,355,356,357,358,359,329],"Avanade","Federal Housing Finance Agency","Human Managed","SEP2","St. Luke's University Health Network","TÜV SÜD",{"kpi":361,"label":362,"unit":289,"n":290,"nUpTo":247,"kind":291,"value":336,"qualifier":293,"claimant":201,"organization":201,"vendorReported":174},"productivity-gain","Productivity gain",{"indexable":186,"reasons":364},[],[366,371,376,382,390,397,403,410,418,425,432,438,444,450,457,464,470,477,482,488,494,501,506,511,516,522,527,532,538,543,551,557,563,569,574,579],{"id":130,"label":367,"issuer":136,"region":137,"url":368,"description":369,"useCases":370,"indexable":186},"EU AI Act","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",250,{"id":131,"label":372,"issuer":136,"region":137,"url":373,"description":374,"useCases":375,"indexable":186},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",223,{"id":132,"label":377,"issuer":378,"region":175,"url":379,"description":380,"useCases":381,"indexable":186},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",127,{"id":383,"label":384,"issuer":385,"region":386,"url":387,"description":388,"useCases":389,"indexable":186},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",95,{"id":391,"label":392,"issuer":393,"region":137,"url":394,"description":395,"useCases":396,"indexable":186},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",73,{"id":398,"label":399,"issuer":136,"region":137,"url":400,"description":401,"useCases":402,"indexable":186},"dora","DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",67,{"id":404,"label":405,"issuer":406,"region":137,"url":407,"description":408,"useCases":409,"indexable":186},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",50,{"id":411,"label":412,"issuer":413,"region":414,"url":415,"description":416,"useCases":417,"indexable":186},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":419,"label":420,"issuer":421,"region":414,"url":422,"description":423,"useCases":424,"indexable":186},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":426,"label":427,"issuer":428,"region":175,"url":429,"description":430,"useCases":431,"indexable":186},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",22,{"id":433,"label":434,"issuer":435,"region":386,"url":436,"description":437,"useCases":431,"indexable":186},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":439,"label":440,"issuer":136,"region":137,"url":441,"description":442,"useCases":443,"indexable":186},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",17,{"id":445,"label":446,"issuer":447,"region":137,"url":448,"description":449,"useCases":443,"indexable":186},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",{"id":451,"label":452,"issuer":453,"region":386,"url":454,"description":455,"useCases":456,"indexable":186},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",16,{"id":458,"label":459,"issuer":460,"region":175,"url":461,"description":462,"useCases":463,"indexable":186},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":465,"label":466,"issuer":136,"region":137,"url":467,"description":468,"useCases":469,"indexable":186},"eu-amlr","EU Anti Money Laundering Regulation","https://eur-lex.europa.eu/eli/reg/2024/1624/oj","Regulation (EU) 2024/1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":471,"label":472,"issuer":473,"region":386,"url":474,"description":475,"useCases":476,"indexable":186},"us-bsa","Bank Secrecy Act","FinCEN","https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":478,"label":479,"issuer":136,"region":137,"url":480,"description":481,"useCases":476,"indexable":186},"eu-accessibility-act","European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":483,"label":484,"issuer":485,"region":386,"url":486,"description":487,"useCases":476,"indexable":186},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https://www.fcc.gov/consumers/guides/stop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":489,"label":490,"issuer":491,"region":175,"url":492,"description":493,"useCases":271,"indexable":186},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":495,"label":496,"issuer":497,"region":386,"url":498,"description":499,"useCases":500,"indexable":186},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",11,{"id":502,"label":503,"issuer":136,"region":137,"url":504,"description":505,"useCases":500,"indexable":186},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":507,"label":508,"issuer":136,"region":137,"url":509,"description":510,"useCases":500,"indexable":186},"eu-psd2","PSD2","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":512,"label":513,"issuer":136,"region":137,"url":514,"description":515,"useCases":500,"indexable":186},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",{"id":517,"label":518,"issuer":519,"region":137,"url":520,"description":521,"useCases":320,"indexable":186},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",{"id":523,"label":524,"issuer":413,"region":414,"url":525,"description":526,"useCases":320,"indexable":186},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":528,"label":529,"issuer":136,"region":137,"url":530,"description":531,"useCases":320,"indexable":186},"mifid-ii","MiFID II","https://eur-lex.europa.eu/eli/dir/2014/65/oj","Directive 2014/65/EU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":533,"label":534,"issuer":535,"region":386,"url":536,"description":537,"useCases":352,"indexable":186},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",{"id":539,"label":540,"issuer":136,"region":137,"url":541,"description":542,"useCases":352,"indexable":186},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":544,"label":545,"issuer":546,"region":547,"url":548,"description":549,"useCases":550,"indexable":186},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":552,"label":553,"issuer":554,"region":137,"url":555,"description":556,"useCases":335,"indexable":186},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":558,"label":559,"issuer":560,"region":137,"url":561,"description":562,"useCases":335,"indexable":186},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":564,"label":565,"issuer":566,"region":414,"url":567,"description":568,"useCases":290,"indexable":186},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":570,"label":571,"issuer":136,"region":137,"url":572,"description":573,"useCases":290,"indexable":186},"eu-mar","EU Market Abuse Regulation","https://eur-lex.europa.eu/eli/reg/2014/596/oj","Regulation (EU) 596/2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":575,"label":576,"issuer":136,"region":137,"url":577,"description":578,"useCases":290,"indexable":186},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":580,"label":581,"issuer":582,"region":386,"url":583,"description":584,"useCases":290,"indexable":186},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790783075338]