[{"data":1,"prerenderedAt":618},["ShallowReactive",2],{"uc-support-knowledge-article-generation":3,"uc-regulations":411},{"useCase":4,"evidence":187,"blitsAiDeployments":279,"benchmarks":280,"indicative":281,"related":284,"indexability":409,"includeUnpublished":193},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":21,"patterns":25,"channels":29,"audience":32,"autonomy":33,"adoptionStage":34,"problem":35,"problemStats":36,"howItWorks":37,"valueDrivers":38,"kpis":43,"indicativeValue":48,"macroEstimates":83,"feasibility":84,"implementation":97,"risk":143,"blitsAi":164,"faq":166,"related":176,"datePublished":182,"dateModified":182,"lastVerified":182,"changelog":183,"slug":186},"AI for support knowledge article generation and maintenance","Knowledge article generation","AI knowledge base article generation for support","AI drafts knowledge base articles from resolved tickets and flags gaps and stale content. NSF (ServiceNow Now Assist) and an IRS pilot use it to draft articles.","published","AI that drafts knowledge base articles from resolved tickets, cases and conversations, detects questions the knowledge base does not answer and articles that are outdated or contradict each other, and proposes new or revised articles for a knowledge owner to review and publish.",[12,13,14,15,16],"knowledge base article generation","AI knowledge authoring","knowledge gap detection","case to article","knowledge centered service with AI",[18,19,20],"cross-industry","government","automotive",[22,23,24],"knowledge-management","customer-service","it-and-engineering",[26,27,28],"content-generation","summarization","classification-and-routing",[30,31],"internal-tools","agent-desktop","employee-facing","copilot","emerging","Every support organization depends on its knowledge base, and almost every knowledge base is\nbehind. Agents solve a new problem, write a few lines in the ticket and move on; the fix never\nbecomes an article, so the next agent and the next customer start from scratch. Articles that do\nexist go stale when products, prices and procedures change, and nobody knows which ones, because\nchecking hundreds of articles against reality is nobody's full time job.\n\nThis matters more now than it used to. Self service portals, chatbots and AI agents all answer\nfrom the same knowledge base, so a gap or an outdated article is repeated at scale. The work AI can\ntake on is the drafting and the detection: turning resolved cases into structured first drafts,\nfinding clusters of questions with no good article, and flagging articles that conflict with\nnewer ones or have not been touched since a change. Publishing stays with a knowledge owner.",[],"1. **Mine resolved work.** When a ticket, case or chat is closed with a new or unusual resolution,\n   the AI reads the case notes, the conversation and the resolution steps.\n2. **Draft in the house format.** It writes a first draft in the organization's article template\n   (problem, environment, cause, resolution steps, related articles), without customer data.\n3. **Check for duplicates and conflicts.** The draft is compared with existing articles; the AI\n   proposes an update to an existing article instead of a new one where they overlap, and flags\n   contradictions.\n4. **Find the gaps.** Questions from search logs, chatbot conversations and tickets that retrieval\n   could not answer well are clustered and ranked by volume, each with a proposed article.\n5. **Flag stale content.** Articles are checked against release notes, policy changes and\n   feedback (thumbs down, reopened tickets), and the ones at risk are queued for their owner.\n6. **Review and publish.** A knowledge owner edits, approves and publishes; the article then feeds\n   agents, self service and AI assistants alike.",[39,40,41,42],"employee-productivity","cost-to-serve","customer-experience","speed",[44,45,46,47],"time-saved-per-task","productivity-gain","containment-rate","first-contact-resolution",{"referenceOrg":49,"inputs":50,"formula":78,"currency":79,"period":80,"resultLabel":81,"caveat":82},"A support organization that publishes or revises 2,000 knowledge articles a year",[51,57,64,71],{"key":52,"label":53,"low":54,"high":54,"unit":55,"note":56},"articles","Articles created or substantially revised per year",2000,"articles per year","The reference organization.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"hoursPerArticle","Author time per article without AI",1.5,3,"hours per article","Editorial assumption covering research, writing and formatting. Replace with your own.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"timeSavedShare","Share of author time saved by a reviewed AI draft",0.3,0.5,"fraction of author time","Editorial assumption. No public benchmark on this page quantifies it yet; review and testing time is kept with the author.",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77},"authorCost","Fully loaded cost of a knowledge author or senior agent",45,70,"USD per hour","Editorial assumption, replace with your own.","articles * hoursPerArticle * timeSavedShare * authorCost","USD","per year","Authoring time value released","Counts only authoring time. It leaves out the usually larger effect of a more complete, current knowledge base on self service containment, first contact resolution and new agent ramp up, and the cost of the platform and of the reviewers' time.",[],{"complexity":85,"complexityNote":86,"dataPrerequisites":87,"integrations":92},"low","Drafting from a closed case is straightforward. The effort is in a clear article template, clean ownership of content, removing customer data from drafts and getting authors to review instead of rewrite.",[88,89,90,91],"Closed tickets or cases with usable resolution notes","The current knowledge base with owners, review dates and article templates","Search, chatbot and ticket logs that show unanswered or poorly answered questions","Release notes and policy change records to detect stale articles",[93,94,95,96],"IT or customer service management platform (for example ServiceNow, Zendesk, Salesforce)","Knowledge base or content management system with an approval workflow","Search and chatbot analytics","Product release and policy change feeds",{"steps":98,"guardrails":117,"humanInTheLoop":123,"kpisToInstrument":124,"failureModes":130},[99,102,105,108,111,114],{"title":100,"detail":101},"Fix the template and the owners first","Agree one article template per content type and assign every article and category to an owner with a review date. Drafts without an owner never get published.",{"title":103,"detail":104},"Start with case to article drafting","Trigger a draft when an agent marks a resolution as reusable. Measure how much the reviewer changes, and tune the prompt until most drafts need edits rather than rewrites.",{"title":106,"detail":107},"Strip customer data from drafts","Mask names, account numbers and environment details that identify a customer before drafting, and have the reviewer confirm nothing personal remains.",{"title":109,"detail":110},"Add gap detection","Cluster the questions that search and chatbots failed to answer, rank them by volume and propose articles for the top clusters each week.",{"title":112,"detail":113},"Add stale content checks","Compare articles with release notes and policy changes, and use negative feedback and reopened tickets as signals. Queue at risk articles for their owner rather than editing them silently.",{"title":115,"detail":116},"Close the loop with the assistants","Track whether new articles actually reduce repeat questions and improve answers from self service and AI agents, and retire articles that nobody uses.",[118,119,120,121,122],"No article is published without approval by a named knowledge owner","Customer and personal data removed from drafts before review","Drafts cite the cases and sources they were derived from, for the reviewer to check","Updates to existing articles are proposed as changes with a visible difference, never applied silently","Articles on regulated topics (fees, legal rights, safety) go through the existing compliance review","Knowledge owners approve every new article and every change, and remain accountable for the content. Agents flag which resolutions are worth an article; reviewers sample published AI drafts monthly for accuracy against the source cases.",[125,126,127,128,129],"Share of AI drafts published with minor edits versus rewritten or rejected","Author time per published article, before and after","Time from first occurrence of a new issue to a published article","Unanswered question clusters closed per month","Self service and first contact resolution on topics with new or refreshed articles",[131,134,137,140],{"title":132,"detail":133},"Publishing the fix for one customer","A draft generalizes a workaround that only applied to one environment. Require the reviewer to confirm scope and prerequisites.",{"title":135,"detail":136},"Article sprawl","Every case becomes a new article and search gets worse. Prefer updating existing articles and merge duplicates.",{"title":138,"detail":139},"Customer data in the knowledge base","Details from the source case survive into a published article. Mask before drafting and check before approval.",{"title":141,"detail":142},"Stale content amplified by AI","Chatbots answer confidently from an outdated article. Tie review dates and stale content checks to the release process.",{"euAiAct":144,"regulations":147,"guidance":151,"controls":158,"incidents":163},{"tier":145,"basis":146},"minimal","Drafting internal or public help content that a person reviews and publishes is not a prohibited practice under Article 5 and is not listed in Annex III, so it is minimal risk. The articles are not a direct AI interaction, and the Article 50(4) disclosure for AI generated text published to inform the public does not apply where a person reviews the text and holds editorial responsibility. The Article 50 transparency duties do apply to chatbots that later answer customers from the articles.",[148,149,150],"eu-ai-act","gdpr","iso-42001",[152],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://artificialintelligenceact.eu/article/50/","Paragraph 4 requires disclosure of AI generated text published to inform the public on matters of public interest, unless it has undergone human review or editorial control and a person holds editorial responsibility.",[159,160,161,162],"Content ownership and review dates recorded for every article","Approval workflow that records who approved each AI drafted article","Personal data masking on case data used for drafting","Periodic audit of a sample of published AI drafts against their source cases",[],{"howToBuild":165},"On Blits.ai this is an **agentic workflow** that the service platform triggers through an API token\nwhen a case closes: **custom functions**\nread the ticket and resolution from the service platform (ServiceNow and Zendesk are in the\nintegration catalog, Freshdesk has a ready made tool), **PII masking** strips customer data, and an **AI agent** with\n**structured output** writes the draft in your template. The workflow compares the draft with the\nexisting **knowledge base** using hybrid retrieval to propose an update instead of a duplicate, and\n**human in the loop** approval holds every draft until a knowledge owner accepts or rejects it.\n\nFor gap detection, Blits.ai **analytics** already list untrained questions and unexpected answers\nfrom live conversations, and **response feedback** with thumbs and comments shows which answers\nfailed. Approved articles go into the document library with **version control and revert**, so\nevery assistant on the platform answers from the new version, and **test suites** rerun the\nquestions that exposed the gap to confirm it is closed. The platform is model agnostic and can run\nin the EU or UAE region.",[167,170,173],{"question":168,"answer":169},"Can AI write knowledge base articles on its own?","It can produce complete first drafts from resolved cases, but publishing should stay with a knowledge owner. The IRS IT service desk runs a limited production pilot that generates knowledge base articles from incident and case records, and expects it to feed its existing knowledge review and publication processes.",{"question":171,"answer":172},"Where do the biggest gains come from?","Less from faster writing than from a knowledge base that keeps up: fixes captured the day they are found, gaps closed by volume, stale articles flagged. That quality then flows into every self service channel and AI agent that answers from it.",{"question":174,"answer":175},"Is this a separate tool or part of the service platform?","Often it is a feature switched on in the service platform. The U.S. National Science Foundation uses ServiceNow Now Assist to generate responses, work notes and knowledge base articles. A separate build makes sense when knowledge lives in several systems or feeds several assistants.",[177,178,179,180,181],"enterprise-knowledge-search","email-and-ticket-reply-drafting","live-agent-assist","it-service-desk-resolution-agent","first-line-contact-centre-agent","2026-09-27",[184],{"date":182,"note":185},"First published","support-knowledge-article-generation",[188,217,238,258],{"title":189,"useCases":190,"organization":191,"vendors":196,"summary":197,"stage":198,"year":199,"channels":200,"languages":201,"metrics":203,"outcomeDisclosed":193,"sources":204,"verification":212,"grade":214,"id":215,"organizationSlug":216},"US Internal Revenue Service: generative AI resolution notes and knowledge articles at the IT service desk",[186],{"name":192,"anonymized":193,"country":194,"region":195,"industry":19},"Internal Revenue Service",false,"US","north-america",[],"The IRS User and Network Services IT service desk runs a generative AI pilot, described as a limited production challenge, that summarizes incident case notes for warm handoffs, writes resolution notes from the actions taken, and generates complete knowledge base articles from incident and case records. The agency expects the effort to feed its existing knowledge review and publication processes, save time on handoffs, shorten the mean time to restore and support more self service, but publishes no results.","pilot",2025,[30],[202],"en",[],[205,209],{"url":206,"title":207,"publisher":208},"https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory","2025 Federal Agency AI Use Case Inventory","Office of Management and Budget (GitHub)",{"url":210,"title":211,"publisher":208},"https://raw.githubusercontent.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory/main/Data/2025_individually_reported_AI_use_cases.csv","2025 individually reported AI use cases (entry TREAS-IRS-42, Ticket Management Generative AI Pilot)",{"level":213,"checkedAt":182},"source-verified","B","irs-service-desk-knowledge-article-generation","internal-revenue-service",{"title":218,"useCases":219,"organization":220,"vendors":222,"summary":226,"stage":227,"year":199,"channels":228,"languages":229,"metrics":230,"outcomeDisclosed":193,"sources":231,"verification":235,"grade":214,"id":236,"organizationSlug":237},"US National Science Foundation: ServiceNow Now Assist drafting responses, work notes and knowledge articles",[186],{"name":221,"anonymized":193,"country":194,"region":195,"industry":19},"U.S. National Science Foundation",[223],{"name":224,"role":225},"ServiceNow","platform","NSF's Office of Information and Resource Management uses ServiceNow's generative AI, Now Assist, on a FedRAMP High platform to generate content for its service operation, including responses, work notes and knowledge base articles, alongside recommendations and chatbots. It shows the common pattern of knowledge article drafting switched on inside an existing service management platform rather than built separately. No outcome figures are published.","production",[30],[202],[],[232,233],{"url":206,"title":207,"publisher":208},{"url":210,"title":234,"publisher":208},"2025 individually reported AI use cases (NSF entry 7, ServiceNow GenAI Now Assist)",{"level":213,"checkedAt":182},"nsf-servicenow-now-assist-knowledge-articles",null,{"title":239,"useCases":240,"organization":241,"vendors":243,"summary":246,"stage":227,"year":247,"channels":248,"languages":250,"metrics":251,"outcomeDisclosed":193,"sources":252,"verification":256,"grade":214,"id":257,"organizationSlug":237},"US Centers for Disease Control and Prevention: SmartFind knowledge bot for partner mailbox replies",[186,178],{"name":242,"anonymized":193,"country":194,"region":195,"industry":19},"Centers for Disease Control and Prevention",[244],{"name":245,"role":225},"Microsoft","CDC's National Center for Immunization and Respiratory Diseases runs SmartFind, an internal knowledge bot with a SharePoint component that helps program staff manage partner emails and lets mailbox managers use a shared knowledge base. The agency lists partner mailbox email management and knowledge base maintenance as the purpose. The bot matches free text questions to agency cleared answers and flags complex or unanswerable queries for manual review. Earlier public facing versions gave agency cleared answers to public and partner questions during the COVID-19 pandemic. No outcome figures are published.",2024,[249,30],"email",[202],[],[253,254],{"url":206,"title":207,"publisher":208},{"url":210,"title":255,"publisher":208},"2025 individually reported AI use cases (HHS entry, NCIRD SmartFind ChatBots, Public and Internal)",{"level":213,"checkedAt":182},"cdc-smartfind-knowledge-bot",{"title":259,"useCases":260,"organization":261,"vendors":263,"summary":266,"stage":227,"year":199,"channels":267,"languages":268,"metrics":269,"outcomeDisclosed":193,"sources":270,"verification":276,"grade":277,"id":278,"organizationSlug":237},"Rivian: grounded, shareable knowledge base for frequently asked questions",[186],{"name":262,"anonymized":193,"country":194,"region":195,"industry":20},"Rivian",[264],{"name":265,"role":225},"Google","Rivian uses NotebookLM to centralize answers to frequently asked questions from verified sources and share them as a knowledge base with interactive chat. Google Cloud reports that it reduced repetitive inquiries and saved employees time, without a figure. It is a simple example of turning scattered answers into shared, grounded knowledge.",[30],[202],[],[271],{"url":272,"title":273,"publisher":274,"date":275},"https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders","Real world gen AI use cases from the world's leading organizations","Google Cloud","2026-04-22",{"level":213,"checkedAt":182},"C","rivian-notebooklm-shared-knowledge-base",0,[],{"low":282,"high":283},40500,210000,[285,308,333,354,374],{"slug":177,"title":286,"shortTitle":287,"definition":288,"status":9,"industries":289,"functions":294,"patterns":296,"audience":32,"autonomy":299,"adoptionStage":300,"evidenceCount":301,"publicEvidenceCount":301,"organizations":302,"bestGrade":214,"headline":237,"lastVerified":182,"indexable":307},"AI enterprise knowledge search for employees","Enterprise knowledge search","An assistant that lets any employee ask a question in plain language and get a synthesized answer from the organization's own policies, procedures, product manuals and research, with citations to the source documents and only from documents the employee is allowed to see.",[18,290,291,292,19,293],"banking","wealth-and-asset-management","insurance","professional-services",[22,295,23],"operations",[297,298,27],"rag-knowledge-assistant","conversational-agent","assist","mainstream",4,[303,304,305,306],"Bank of America","Morgan Stanley","SIGNAL IDUNA","Wells Fargo",true,{"slug":178,"title":309,"shortTitle":310,"definition":311,"status":9,"industries":312,"functions":315,"patterns":316,"audience":32,"autonomy":33,"adoptionStage":300,"evidenceCount":317,"publicEvidenceCount":317,"organizations":318,"bestGrade":214,"headline":324,"lastVerified":182,"indexable":307},"AI reply drafting for customer email and support tickets","Email and ticket reply drafting","A copilot for asynchronous service work that drafts the reply to an incoming customer email, message or ticket once it has reached an agent: it summarizes the request, pulls the relevant customer data and approved knowledge, and drafts a reply in the organization's tone and the customer's language for the agent to check, edit and send. Live calls and chats, and the sorting of the inbox itself, are separate use cases.",[18,19,290,313,314],"telecommunications","technology",[23,295],[26,27,297,28],6,[242,319,320,321,322,323],"First National Bank","HYPE","Nomad eSIM","Transportation Security Administration","Turing",{"kpi":325,"label":326,"unit":327,"n":328,"nUpTo":279,"kind":329,"value":330,"qualifier":331,"claimant":332,"organization":320,"vendorReported":307},"processing-time-reduction","Cycle time reduction","percent",2,"reported",50,"approximately","vendor",{"slug":179,"title":334,"shortTitle":335,"definition":336,"status":9,"industries":337,"functions":340,"patterns":341,"audience":32,"autonomy":299,"adoptionStage":300,"evidenceCount":343,"publicEvidenceCount":344,"organizations":345,"bestGrade":214,"headline":350,"lastVerified":182,"indexable":307},"Real time AI assist for contact centre agents","Live agent assist","A real time copilot for human contact centre agents during a live call or chat: it transcribes the conversation as it happens, surfaces the relevant knowledge and next step, drafts responses, and writes the after call summary and CRM notes, while the agent stays in control of what is said and done.",[18,290,292,313,338,339,314],"healthcare","retail-and-ecommerce",[23,295],[342,297,27,26],"speech-analytics",7,5,[346,347,348,349,305],"DBS Bank","Definity","Oportun","SEB",{"kpi":45,"label":351,"unit":327,"n":328,"nUpTo":279,"kind":329,"value":352,"qualifier":353,"claimant":332,"organization":347,"vendorReported":307},"Productivity gain",15,"exact",{"slug":180,"title":355,"shortTitle":356,"definition":357,"status":9,"industries":358,"functions":359,"patterns":360,"audience":32,"autonomy":362,"adoptionStage":300,"evidenceCount":363,"publicEvidenceCount":317,"organizations":364,"bestGrade":214,"headline":370,"lastVerified":182,"indexable":307},"AI agent for IT service desk resolution","IT service desk resolution","An AI agent in Microsoft Teams, Slack or the intranet that takes the high volume IT support queue, such as password and MFA resets, account unlocks, VPN, device and software requests, and resolves common requests by acting in the identity and IT service management systems, handing the rest to the right resolver group with the context attached.",[18,290,314,339,338],[24,295],[298,361,297,28],"agentic-workflow","supervised-agent",8,[365,303,366,367,368,369],"7-Eleven Vietnam","Equinix","IBM","Mercari US","Vituity",{"kpi":371,"label":372,"unit":327,"n":328,"nUpTo":279,"kind":329,"value":373,"qualifier":353,"claimant":332,"organization":368,"vendorReported":307},"employee-adoption","Employee adoption",94,{"slug":181,"title":375,"shortTitle":376,"definition":377,"status":9,"industries":378,"functions":381,"patterns":382,"audience":384,"autonomy":362,"adoptionStage":300,"segment":385,"evidenceCount":386,"publicEvidenceCount":387,"organizations":388,"bestGrade":214,"headline":405,"lastVerified":182,"indexable":307},"AI agent for first line contact centre service","First line contact centre","An AI agent that answers the first line of inbound customer contact on phone, chat and messaging, resolves general and routine questions end to end in the customer's own language, and routes everything complex, sensitive or regulated to the right human team with the context attached.",[18,290,379,313,380,339,291],"payments","travel-and-hospitality",[23],[298,383,297,28],"voice-agent","customer-facing","front-office",25,18,[389,390,303,391,392,393,394,395,396,397,398,399,400,401,402,403,404],"Air India","Airbnb","Bank of the Philippine Islands","BT Group","Commonwealth Bank of Australia","Ingka Group","JetBlue","Klarna","Lufthansa Group","Mobily","NatWest Group","Pegasus Airlines","Telkomsel","Together Credit Union","Vodafone Germany","Vodafone",{"kpi":46,"label":406,"unit":327,"n":343,"nUpTo":279,"kind":407,"value":408,"qualifier":353,"claimant":237,"organization":237,"vendorReported":193},"Containment rate","median",47,{"indexable":307,"reasons":410},[],[412,417,422,429,436,442,449,455,463,469,476,482,489,495,501,506,513,519,525,531,537,543,549,554,559,566,572,577,582,589,595,601,607,612],{"id":148,"label":413,"issuer":154,"region":155,"url":414,"description":415,"useCases":416,"indexable":307},"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.",197,{"id":149,"label":418,"issuer":154,"region":155,"url":419,"description":420,"useCases":421,"indexable":307},"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.",180,{"id":150,"label":423,"issuer":424,"region":425,"url":426,"description":427,"useCases":428,"indexable":307},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":430,"label":431,"issuer":432,"region":195,"url":433,"description":434,"useCases":435,"indexable":307},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","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.",83,{"id":437,"label":438,"issuer":154,"region":155,"url":439,"description":440,"useCases":441,"indexable":307},"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.",66,{"id":443,"label":444,"issuer":445,"region":155,"url":446,"description":447,"useCases":448,"indexable":307},"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.",64,{"id":450,"label":451,"issuer":452,"region":155,"url":453,"description":454,"useCases":408,"indexable":307},"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.",{"id":456,"label":457,"issuer":458,"region":459,"url":460,"description":461,"useCases":462,"indexable":307},"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.",36,{"id":464,"label":465,"issuer":466,"region":459,"url":467,"description":468,"useCases":386,"indexable":307},"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.",{"id":470,"label":471,"issuer":472,"region":425,"url":473,"description":474,"useCases":475,"indexable":307},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":477,"label":478,"issuer":479,"region":195,"url":480,"description":481,"useCases":475,"indexable":307},"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":483,"label":484,"issuer":485,"region":155,"url":486,"description":487,"useCases":488,"indexable":307},"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.",16,{"id":490,"label":491,"issuer":492,"region":425,"url":493,"description":494,"useCases":352,"indexable":307},"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.",{"id":496,"label":497,"issuer":154,"region":155,"url":498,"description":499,"useCases":500,"indexable":307},"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":502,"label":503,"issuer":154,"region":155,"url":504,"description":505,"useCases":500,"indexable":307},"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.",{"id":507,"label":508,"issuer":509,"region":195,"url":510,"description":511,"useCases":512,"indexable":307},"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":514,"label":515,"issuer":154,"region":155,"url":516,"description":517,"useCases":518,"indexable":307},"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.",12,{"id":520,"label":521,"issuer":522,"region":195,"url":523,"description":524,"useCases":518,"indexable":307},"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.",{"id":526,"label":527,"issuer":528,"region":425,"url":529,"description":530,"useCases":518,"indexable":307},"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":532,"label":533,"issuer":154,"region":155,"url":534,"description":535,"useCases":536,"indexable":307},"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.",11,{"id":538,"label":539,"issuer":540,"region":195,"url":541,"description":542,"useCases":536,"indexable":307},"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":544,"label":545,"issuer":458,"region":459,"url":546,"description":547,"useCases":548,"indexable":307},"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.",10,{"id":550,"label":551,"issuer":154,"region":155,"url":552,"description":553,"useCases":548,"indexable":307},"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":555,"label":556,"issuer":154,"region":155,"url":557,"description":558,"useCases":548,"indexable":307},"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":560,"label":561,"issuer":562,"region":155,"url":563,"description":564,"useCases":565,"indexable":307},"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.",9,{"id":567,"label":568,"issuer":569,"region":195,"url":570,"description":571,"useCases":363,"indexable":307},"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.",{"id":573,"label":574,"issuer":154,"region":155,"url":575,"description":576,"useCases":363,"indexable":307},"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":578,"label":579,"issuer":154,"region":155,"url":580,"description":581,"useCases":317,"indexable":307},"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":583,"label":584,"issuer":585,"region":586,"url":587,"description":588,"useCases":344,"indexable":307},"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.",{"id":590,"label":591,"issuer":592,"region":155,"url":593,"description":594,"useCases":301,"indexable":307},"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":596,"label":597,"issuer":598,"region":155,"url":599,"description":600,"useCases":301,"indexable":307},"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":602,"label":603,"issuer":604,"region":459,"url":605,"description":606,"useCases":61,"indexable":307},"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":608,"label":609,"issuer":154,"region":155,"url":610,"description":611,"useCases":61,"indexable":307},"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":613,"label":614,"issuer":615,"region":195,"url":616,"description":617,"useCases":61,"indexable":307},"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.",1790598301852]