[{"data":1,"prerenderedAt":647},["ShallowReactive",2],{"uc-enterprise-knowledge-search":3,"uc-regulations":444},{"useCase":4,"evidence":217,"blitsAiDeployments":324,"benchmarks":325,"indicative":332,"related":335,"indexability":442,"includeUnpublished":223},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":23,"patterns":27,"channels":31,"audience":35,"autonomy":36,"adoptionStage":37,"problem":38,"problemStats":39,"howItWorks":40,"valueDrivers":41,"kpis":46,"indicativeValue":53,"macroEstimates":93,"feasibility":94,"implementation":106,"risk":152,"blitsAi":193,"faq":195,"related":205,"datePublished":212,"dateModified":212,"lastVerified":212,"changelog":213,"slug":216},"AI enterprise knowledge search for employees","Enterprise knowledge search","Enterprise knowledge search with generative AI","An AI assistant gives employees cited answers from internal documents they may see. Morgan Stanley says 98% of its Financial Advisor teams adopted its assistant.","published","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.",[12,13,14,15],"enterprise search assistant","internal knowledge assistant","policy and procedure assistant","employee copilot for internal knowledge",[17,18,19,20,21,22],"cross-industry","banking","wealth-and-asset-management","insurance","government","professional-services",[24,25,26],"knowledge-management","operations","customer-service",[28,29,30],"rag-knowledge-assistant","conversational-agent","summarization",[32,33,34],"internal-tools","microsoft-teams","agent-desktop","employee-facing","assist","mainstream","In a bank or insurer the knowledge that staff need to do their job correctly is scattered across\ncredit and risk policies, operating procedures, product manuals, compliance guidance and internal\nresearch: many documents in several systems, each with versions. Keyword search returns\na list of PDFs. Staff ask a colleague, use an outdated copy, or give a customer a wrong answer.\n\nRetrieval augmented assistants change the interaction: ask a question, get an answer with the\nparagraphs it came from. The hard problems are not the model. They are permissions (an answer\nmust never come from a document the person may not see), currency (the answer must come from\nthe version in force), and trust (people must be able to check the source quickly). Done well,\none governed retrieval layer can serve the service desk, HR, frontline and specialist assistants\ninstead of each building its own.",[],"1. **Ingest and index approved sources.** Policies, procedures and research are ingested from\n   the document management system, intranet and knowledge base, with owner, version and access\n   rights kept as metadata.\n2. **Retrieve with permissions.** When an employee asks, retrieval runs only over documents\n   their role and entitlements allow, combining semantic and keyword search.\n3. **Answer with citations.** The model answers only from the retrieved passages and cites each\n   document and section, so the employee can open the source.\n4. **Refuse when unsure.** If retrieval finds nothing relevant or sources conflict, the assistant\n   says so and points to the owner, rather than guessing.\n5. **Learn from gaps.** Unanswered and badly rated questions go to content owners, who fix or\n   write the missing document.",[42,43,44,45],"employee-productivity","speed","compliance","customer-experience",[47,48,49,50,51,52],"search-time-reduction","handling-time-reduction","employee-adoption","interactions-handled","accuracy","productivity-gain",{"referenceOrg":54,"inputs":55,"formula":88,"currency":89,"period":90,"resultLabel":91,"caveat":92},"A bank with 5,000 employees who regularly look up policies and procedures",[56,61,68,75,81],{"key":57,"label":58,"low":59,"high":59,"unit":57,"note":60},"employees","Employees who search internal knowledge regularly",5000,"The reference organization.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"searchHoursPerWeek","Hours per week each spends finding and reading internal information",2,4,"hours per employee per week","Editorial assumption. Replace with a time study of your own staff.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"timeSaved","Share of that time saved",0.1,0.25,"fraction of search time","Editorial assumption, replace with a time study of your own. No source on this page reports a measured share of search time saved. For comparison, Google Cloud reports that information searches by less experienced SIGNAL IDUNA agents are 30% faster (read as speed, that is about 23% less time per search, since 1 / 1.3 is about 0.77), and that Wells Fargo's tool reduced the workflow for query resolution by about 20%, without saying whether that is time.",{"key":76,"label":77,"low":78,"high":78,"unit":79,"note":80},"workingWeeks","Working weeks per year",45,"weeks","Editorial assumption.",{"key":82,"label":83,"low":84,"high":85,"unit":86,"note":87},"hourlyCost","Fully loaded cost per hour",50,80,"USD per hour","Editorial assumption. Replace with your own blended cost.","employees * searchHoursPerWeek * workingWeeks * timeSaved * hourlyCost","USD","per year","Employee time released","Released time, not cash. It leaves out the value of fewer wrong answers to customers and fewer policy breaches, which is often larger, and the cost of cleaning up and maintaining the content.",[],{"complexity":95,"complexityNote":96,"dataPrerequisites":97,"integrations":101},"medium","A prototype over a folder of PDFs is quick to build. Production takes longer: connecting several document systems, carrying access rights into the index, handling versions and retirement, and building evaluation sets per domain so answer quality can be measured.",[98,99,100],"An inventory of authoritative sources with an owner and review date per document","Access rights per document or collection that can be carried into the index","A set of real questions per domain with expected answers, for evaluation",[102,103,104,105],"Document management and intranet (for example SharePoint or Confluence)","Identity provider and entitlement data for permission aware retrieval","The channels where employees work (Teams, the agent desktop, the intranet)","Feedback routing to content owners",{"steps":107,"guardrails":126,"humanInTheLoop":132,"kpisToInstrument":133,"failureModes":139},[108,111,114,117,120,123],{"title":109,"detail":110},"Start with one domain and its owners","Pick a domain with heavy lookup volume and willing owners, such as operations procedures or product terms. Clean its documents before indexing anything.",{"title":112,"detail":113},"Carry permissions into retrieval","Index access rights with every chunk and filter at query time. Test with accounts of different roles that restricted content never appears.",{"title":115,"detail":116},"Build the evaluation set","Collect a few hundred real questions with expected answers and sources, and run them on every change to content, retrieval settings or model.",{"title":118,"detail":119},"Make citations the product","Show the source passage next to the answer with a link. Staff trust and adopt tools whose answers they can check in seconds.",{"title":121,"detail":122},"Close the loop with content owners","Send unanswered questions and negative feedback to owners weekly, and retire documents that are superseded.",{"title":124,"detail":125},"Offer it as a shared layer","Expose the same governed retrieval to the other assistants (service desk, HR, contact centre) so permissions, residency and versions are enforced in one place.",[127,128,129,130,131],"Permission aware retrieval, tested with role based test accounts","Answers only from retrieved passages, with citations, and refusal when nothing relevant is found","Only the version in force is indexed; superseded documents are removed","Prompt injection defences for content from shared or external sources","Query logs protected and retained according to policy","Content owners are accountable for their documents and review flagged answers. Employees remain responsible for decisions they take on the basis of an answer, and high impact decisions (credit, compliance, customer remediation) still follow their documented approval steps.",[134,135,136,137,138],"Answer accuracy and citation correctness on the evaluation set, per domain","Share of questions answered versus refused","Weekly active users among target employees","Time to find information in a time study, before and after","Negative feedback and content gaps closed per month",[140,143,146,149],{"title":141,"detail":142},"Oversharing through search","The assistant surfaces documents that were technically accessible but never meant to be widely read. Review permissions before indexing, not after an incident.",{"title":144,"detail":145},"Confident answers from old versions","Superseded policies stay in the index. Index only the version in force and track effective dates.",{"title":147,"detail":148},"Answers without sources","Staff cannot verify and either distrust the tool or trust it blindly. Always show the cited passage.",{"title":150,"detail":151},"Many point solutions","Every department builds its own index with its own permissions. Build one governed layer and reuse it.",{"euAiAct":153,"regulations":156,"guidance":163,"controls":182,"incidents":188},{"tier":154,"basis":155},"limited","Article 50(1) requires that people who interact directly with an AI system are informed of it, unless this is obvious from the context, as it usually is for an internal assistant. The system would be high risk only if it were intended for an Annex III purpose, such as assessing the creditworthiness of natural persons (point 5(b)) or making decisions on or evaluating workers (point 4(b)).",[157,158,159,160,161,162],"eu-ai-act","gdpr","dora","iso-42001","nist-ai-rmf","mas-ai-risk-management",[164,170,176],{"title":165,"issuer":166,"region":167,"url":168,"note":169},"Artificial Intelligence (AI) Model Risk Management, information paper","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/publications/monographs-or-information-paper/2024/artificial-intelligence-model-risk-management","Good practices for AI and generative AI model risk management observed in a thematic review of banks in mid 2024, covering governance and oversight, risk management systems and processes, and development and deployment.",{"title":171,"issuer":172,"region":173,"url":174,"note":175},"OWASP Top 10 for LLM Applications","OWASP Gen AI Security Project","global","https://genai.owasp.org/llm-top-10/","The 2025 list covers prompt injection (including through retrieved content), sensitive information disclosure and vector and embedding weaknesses, the main security risks of retrieval assistants.",{"title":177,"issuer":178,"region":179,"url":180,"note":181},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://artificialintelligenceact.eu/article/50/","Providers must design AI systems that interact directly with people so that those people are informed they are interacting with AI, unless this is obvious from the context. Applies from 2 August 2026.",[183,184,185,186,187],"Inventory entry with an accountable owner and the list of indexed sources","Permission tests per role before each new source is added","Evaluation set runs on every change to content, retrieval or model","Document ownership and review dates enforced for indexed content","Monitoring of refusals, negative feedback and unusual query patterns",[189],{"title":190,"url":191,"note":192},"CVE-2025-32711: AI command injection in Microsoft 365 Copilot","https://nvd.nist.gov/vuln/detail/CVE-2025-32711","A vulnerability recorded by NVD in June 2025, not a reported breach: AI command injection in Microsoft 365 Copilot allowed an unauthorized attacker to disclose information over a network. It shows that an enterprise assistant can be made to disclose information through injected instructions.",{"howToBuild":194},"On Blits.ai this is an **AI agent** over a **knowledge base** with **hybrid retrieval** (dense\nvectors plus BM25), ingesting PDFs, Office documents, email files and crawled web pages,\nand a central document library with **version control** and revert, so owners can replace\nsuperseded documents. SQL knowledge bases add structured data where answers need numbers.\nSharePoint and OneDrive are available as ready made tools, and Confluence is in the integration\ncatalog.\n\nThe same agent serves **Microsoft Teams**, the intranet through the web widget, and other\nassistants through the **REST API**, so one retrieval layer backs several front doors.\n**Guardrails** block prompt injection and unsafe output, **PII masking** protects personal\ndata, and **role based access control** limits who manages which agents and content. **Test\nsuites** with LLM grading that uses knowledge base evidence run the evaluation set on every\nchange, and **analytics** with response feedback show untrained questions and unexpected answers\nfor content owners. The platform is model agnostic and\ncan run in the EU or UAE region.",[196,199,202],{"question":197,"answer":198},"How is this different from the search we already have?","Search returns documents; the assistant returns an answer with the paragraphs it came from. Google Cloud reports that Wells Fargo's retrieval tool for branch bankers reduced the workflow for query resolution by about 20%, and that information searches by less experienced SIGNAL IDUNA service agents are 30% faster.",{"question":200,"answer":201},"Will employees actually use it?","Two wealth managers have published usage figures. Morgan Stanley said in June 2024 that 98% of its Financial Advisor teams had adopted its AI @ Morgan Stanley Assistant. Bank of America reports more than 23 million interactions in 2024 with ask MERRILL and ask PRIVATE BANK, a volume figure that does not say what share of employees use the tools.",{"question":203,"answer":204},"How do we stop it from showing confidential documents?","Carry each document's access rights into the index and filter at query time, then test with accounts of different roles. Review what is technically accessible before you index it, because an assistant makes forgotten oversharing easy to find.",[206,207,208,209,210,211],"wealth-advisor-knowledge-assistant","hr-and-policy-assistant","it-service-desk-resolution-agent","live-agent-assist","support-knowledge-article-generation","governed-text-to-sql-analytics","2026-09-27",[214],{"date":212,"note":215},"First published","enterprise-knowledge-search",[218,255,278,309],{"title":219,"useCases":220,"organization":221,"vendors":226,"summary":229,"stage":230,"year":231,"channels":232,"languages":233,"metrics":235,"outcomeDisclosed":244,"sources":245,"verification":249,"grade":252,"id":253,"organizationSlug":254},"Bank of America: ask MERRILL and ask PRIVATE BANK knowledge assistants for advisers",[216,206],{"name":222,"anonymized":223,"country":224,"region":225,"industry":19},"Bank of America",false,"US","north-america",[227],{"name":222,"role":228},"in-house","Merrill and Bank of America Private Bank teams use ask MERRILL and ask PRIVATE BANK, built on the technology behind Erica, to curate the information they need for clients. For more complex requests, the chat can connect teams with experts at the bank. The bank reports more than 23 million interactions with the two tools in 2024.","scaled",2024,[32],[234],"en",[236],{"kpi":50,"value":237,"unit":238,"qualifier":239,"period":240,"claimant":241,"quote":242,"sourceUrl":243},23000000,"count","at-least","calendar year 2024","organization","In 2024, there were more than 23 million interactions with ask MERRILL and ask PRIVATE BANK, an increase of 1 million over 2023, helping employees more proactively connect with clients about timely and relevant opportunities.","https://newsroom.bankofamerica.com/content/newsroom/press-releases/2025/04/ai-adoption-by-bofa-s-global-workforce-improves-productivity--cl.html",true,[246],{"url":243,"title":247,"publisher":222,"date":248},"AI Adoption by BofA's Global Workforce Improves Productivity, Client Service","2025-04-08",{"level":250,"checkedAt":251},"source-verified","2026-09-26","B","bank-of-america-ask-merrill-and-ask-private-bank","bank-of-america",{"title":256,"useCases":257,"organization":258,"vendors":260,"summary":264,"stage":230,"year":265,"channels":266,"languages":267,"metrics":268,"outcomeDisclosed":244,"sources":269,"verification":275,"grade":252,"id":276,"organizationSlug":277},"Morgan Stanley: AI @ Morgan Stanley Assistant gives advisers access to the firm's intellectual capital",[216,206],{"name":259,"anonymized":223,"country":224,"region":225,"industry":19},"Morgan Stanley",[261],{"name":262,"role":263},"OpenAI","model-provider","Morgan Stanley Wealth Management fully rolled out the AI @ Morgan Stanley Assistant in September 2023, a generative AI chatbot that gives Financial Advisors quick access to the firm's intellectual capital. The rollout followed the firm's March 2023 announcement of OpenAI as its strategic partner. In its June 2024 release the firm said that 98% of Financial Advisor teams had adopted it. It was followed by AI @ Morgan Stanley Debrief, which drafts meeting notes and follow up emails with client consent.",2023,[32],[234],[],[270],{"url":271,"title":272,"publisher":259,"date":273,"archivedUrl":274},"https://www.morganstanley.com/press-releases/ai-at-morgan-stanley-debrief-launch","Launch of AI @ Morgan Stanley Debrief","2024-06-26","https://web.archive.org/web/20260903124043/https://www.morganstanley.com/press-releases/ai-at-morgan-stanley-debrief-launch",{"level":250,"checkedAt":212},"morgan-stanley-ai-assistant-knowledge-search","morgan-stanley",{"title":279,"useCases":280,"organization":281,"vendors":284,"summary":293,"stage":294,"year":295,"channels":296,"languages":297,"metrics":299,"outcomeDisclosed":244,"sources":300,"verification":305,"grade":306,"id":307,"organizationSlug":308},"SIGNAL IDUNA: Co SI knowledge assistant for health insurance service agents",[216,209],{"name":282,"anonymized":223,"country":283,"region":179,"industry":20},"SIGNAL IDUNA","DE",[285,288,291],{"name":286,"role":287},"Google Cloud","platform",{"name":289,"role":290},"Boston Consulting Group","integrator",{"name":292,"role":290},"Deloitte","SIGNAL IDUNA, a German insurer, built Co SI with Google Cloud, BCG and Deloitte: a knowledge assistant that helps customer service agents answer complex health insurance questions. Google Cloud reports that for less experienced agents, information searches are 30% faster and inquiries that previously needed further escalation dropped from 27% to 3%.","production",2025,[34],[298],"de",[],[301],{"url":302,"title":303,"publisher":286,"archivedUrl":304},"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","https://web.archive.org/web/20251027121348/https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders",{"level":250,"checkedAt":212},"C","signal-iduna-co-si-knowledge-assistant",null,{"title":310,"useCases":311,"organization":312,"vendors":314,"summary":316,"stage":294,"year":295,"channels":317,"languages":318,"metrics":319,"outcomeDisclosed":244,"sources":320,"verification":322,"grade":306,"id":323,"organizationSlug":308},"Wells Fargo: retrieval tool for branch bankers on policies and procedures",[216],{"name":313,"anonymized":223,"country":224,"region":225,"industry":18},"Wells Fargo",[315],{"name":286,"role":287},"Wells Fargo deployed a retrieval augmented tool for branch bankers that finds the relevant policies and procedures during customer interactions. Google Cloud reports that it reduced the workflow for query resolution by about 20%, without saying whether that means time, steps or effort. The bank uses reusable APIs on Apigee to scale generative AI across teams.",[32],[234],[],[321],{"url":302,"title":303,"publisher":286,"archivedUrl":304},{"level":250,"checkedAt":212},"wells-fargo-branch-policy-retrieval",0,[326],{"kpi":50,"label":327,"unit":238,"aggregate":223,"higherIsBetter":244,"n":328,"nUpTo":324,"median":237,"min":237,"max":237,"byClaimant":329,"vendorOnly":223,"points":330},"Interactions handled",1,{"organization":328,"vendor":324,"regulator":324,"independent":324},[331],{"evidenceId":253,"organization":222,"value":237,"qualifier":239,"claimant":241,"grade":252,"pooled":244},{"low":333,"high":334},2250000,18000000,[336,351,375,393,412,427],{"slug":206,"title":337,"shortTitle":338,"definition":339,"status":9,"industries":340,"functions":341,"patterns":343,"audience":35,"autonomy":36,"adoptionStage":37,"segment":344,"evidenceCount":345,"publicEvidenceCount":345,"organizations":346,"bestGrade":252,"headline":308,"lastVerified":251,"indexable":244},"AI knowledge assistant for wealth advisors and relationship managers","Advisor knowledge assistant","A conversational assistant that answers a wealth advisor's or relationship manager's questions in seconds from the firm's own research, house view, product documentation and policies, with every answer linked to the source document so the advisor can check it before using it with a client.",[19,18],[24,342,26],"sales",[28,29],"front-office",6,[222,347,348,259,349,350],"Citi","JPMorgan Chase","UBS","Yes Bank",{"slug":207,"title":352,"shortTitle":353,"definition":354,"status":9,"industries":355,"functions":358,"patterns":360,"audience":35,"autonomy":362,"adoptionStage":363,"evidenceCount":364,"publicEvidenceCount":65,"organizations":365,"bestGrade":252,"headline":369,"lastVerified":212,"indexable":244},"AI assistant for HR and policy questions","HR and policy assistant","An employee self service assistant that answers questions on leave, pay and tax forms, benefits, expenses, travel and conduct policies from the organization's own HR documents, personalized to the employee's country and role, and starts simple HR transactions such as leave requests or employment letters in the HR system.",[17,18,356,357],"technology","healthcare",[359,24],"human-resources",[28,29,361],"agentic-workflow","supervised-agent","early-adopters",5,[222,366,367,368],"IBM","Turing","Vituity",{"kpi":49,"label":370,"unit":371,"n":64,"nUpTo":324,"kind":372,"value":373,"qualifier":374,"claimant":241,"organization":366,"vendorReported":223},"Employee adoption","percent","reported",99,"exact",{"slug":208,"title":376,"shortTitle":377,"definition":378,"status":9,"industries":379,"functions":381,"patterns":383,"audience":35,"autonomy":362,"adoptionStage":37,"evidenceCount":385,"publicEvidenceCount":345,"organizations":386,"bestGrade":252,"headline":390,"lastVerified":212,"indexable":244},"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.",[17,18,356,380,357],"retail-and-ecommerce",[382,25],"it-and-engineering",[29,361,28,384],"classification-and-routing",8,[387,222,388,366,389,368],"7-Eleven Vietnam","Equinix","Mercari US",{"kpi":49,"label":370,"unit":371,"n":64,"nUpTo":324,"kind":372,"value":391,"qualifier":374,"claimant":392,"organization":389,"vendorReported":244},94,"vendor",{"slug":209,"title":394,"shortTitle":395,"definition":396,"status":9,"industries":397,"functions":399,"patterns":400,"audience":35,"autonomy":36,"adoptionStage":37,"evidenceCount":403,"publicEvidenceCount":364,"organizations":404,"bestGrade":252,"headline":409,"lastVerified":212,"indexable":244},"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.",[17,18,20,398,357,380,356],"telecommunications",[26,25],[401,28,30,402],"speech-analytics","content-generation",7,[405,406,407,408,282],"DBS Bank","Definity","Oportun","SEB",{"kpi":52,"label":410,"unit":371,"n":64,"nUpTo":324,"kind":372,"value":411,"qualifier":374,"claimant":392,"organization":406,"vendorReported":244},"Productivity gain",15,{"slug":210,"title":413,"shortTitle":414,"definition":415,"status":9,"industries":416,"functions":418,"patterns":419,"audience":35,"autonomy":420,"adoptionStage":421,"evidenceCount":65,"publicEvidenceCount":65,"organizations":422,"bestGrade":252,"headline":308,"lastVerified":212,"indexable":244},"AI for support knowledge article generation and maintenance","Knowledge article generation","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.",[17,21,417],"automotive",[24,26,382],[402,30,384],"copilot","emerging",[423,424,425,426],"Centers for Disease Control and Prevention","Internal Revenue Service","U.S. National Science Foundation","Rivian",{"slug":211,"title":428,"shortTitle":429,"definition":430,"status":9,"industries":431,"functions":433,"patterns":435,"audience":35,"autonomy":36,"adoptionStage":363,"evidenceCount":437,"publicEvidenceCount":437,"organizations":438,"bestGrade":252,"headline":308,"lastVerified":212,"indexable":244},"Governed text to SQL analytics assistant","Governed SQL analytics","An assistant that turns a business user's plain language question into a query against governed data, runs it under that user's own data permissions and returns the table or chart together with the SQL and the tables used, so routine ad hoc questions no longer queue for the data team.",[17,18,20,380,356,432],"pharma-and-life-sciences",[434,382],"analytics-and-reporting",[29,436,28],"code-generation",3,[439,440,441],"Bayer","LinkedIn","Uber Technologies",{"indexable":244,"reasons":443},[],[445,450,455,461,467,472,479,486,491,498,505,511,518,524,530,535,542,548,554,560,566,572,578,583,588,595,601,606,611,618,624,630,636,641],{"id":157,"label":446,"issuer":178,"region":179,"url":447,"description":448,"useCases":449,"indexable":244},"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":158,"label":451,"issuer":178,"region":179,"url":452,"description":453,"useCases":454,"indexable":244},"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":160,"label":456,"issuer":457,"region":173,"url":458,"description":459,"useCases":460,"indexable":244},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":161,"label":462,"issuer":463,"region":225,"url":464,"description":465,"useCases":466,"indexable":244},"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":159,"label":468,"issuer":178,"region":179,"url":469,"description":470,"useCases":471,"indexable":244},"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":473,"label":474,"issuer":475,"region":179,"url":476,"description":477,"useCases":478,"indexable":244},"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":480,"label":481,"issuer":482,"region":179,"url":483,"description":484,"useCases":485,"indexable":244},"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.",47,{"id":162,"label":487,"issuer":166,"region":167,"url":488,"description":489,"useCases":490,"indexable":244},"MAS AI risk management guidelines","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":492,"label":493,"issuer":494,"region":167,"url":495,"description":496,"useCases":497,"indexable":244},"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":499,"label":500,"issuer":501,"region":173,"url":502,"description":503,"useCases":504,"indexable":244},"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":506,"label":507,"issuer":508,"region":225,"url":509,"description":510,"useCases":504,"indexable":244},"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":512,"label":513,"issuer":514,"region":179,"url":515,"description":516,"useCases":517,"indexable":244},"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":519,"label":520,"issuer":521,"region":173,"url":522,"description":523,"useCases":411,"indexable":244},"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":525,"label":526,"issuer":178,"region":179,"url":527,"description":528,"useCases":529,"indexable":244},"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":531,"label":532,"issuer":178,"region":179,"url":533,"description":534,"useCases":529,"indexable":244},"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":536,"label":537,"issuer":538,"region":225,"url":539,"description":540,"useCases":541,"indexable":244},"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":543,"label":544,"issuer":178,"region":179,"url":545,"description":546,"useCases":547,"indexable":244},"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":549,"label":550,"issuer":551,"region":225,"url":552,"description":553,"useCases":547,"indexable":244},"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":555,"label":556,"issuer":557,"region":173,"url":558,"description":559,"useCases":547,"indexable":244},"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":561,"label":562,"issuer":178,"region":179,"url":563,"description":564,"useCases":565,"indexable":244},"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":567,"label":568,"issuer":569,"region":225,"url":570,"description":571,"useCases":565,"indexable":244},"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":573,"label":574,"issuer":166,"region":167,"url":575,"description":576,"useCases":577,"indexable":244},"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":579,"label":580,"issuer":178,"region":179,"url":581,"description":582,"useCases":577,"indexable":244},"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":584,"label":585,"issuer":178,"region":179,"url":586,"description":587,"useCases":577,"indexable":244},"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":589,"label":590,"issuer":591,"region":179,"url":592,"description":593,"useCases":594,"indexable":244},"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":596,"label":597,"issuer":598,"region":225,"url":599,"description":600,"useCases":385,"indexable":244},"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":602,"label":603,"issuer":178,"region":179,"url":604,"description":605,"useCases":385,"indexable":244},"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":607,"label":608,"issuer":178,"region":179,"url":609,"description":610,"useCases":345,"indexable":244},"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":612,"label":613,"issuer":614,"region":615,"url":616,"description":617,"useCases":364,"indexable":244},"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":619,"label":620,"issuer":621,"region":179,"url":622,"description":623,"useCases":65,"indexable":244},"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":625,"label":626,"issuer":627,"region":179,"url":628,"description":629,"useCases":65,"indexable":244},"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":631,"label":632,"issuer":633,"region":167,"url":634,"description":635,"useCases":437,"indexable":244},"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":637,"label":638,"issuer":178,"region":179,"url":639,"description":640,"useCases":437,"indexable":244},"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":642,"label":643,"issuer":644,"region":225,"url":645,"description":646,"useCases":437,"indexable":244},"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.",1790598298836]