[{"data":1,"prerenderedAt":543},["ShallowReactive",2],{"uc-media-archive-metadata-tagging":3,"uc-regulations":332},{"useCase":4,"evidence":169,"blitsAiDeployments":232,"benchmarks":233,"indicative":240,"related":243,"indexability":330,"includeUnpublished":175},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":25,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"problem":32,"problemStats":33,"howItWorks":34,"valueDrivers":35,"kpis":39,"indicativeValue":44,"macroEstimates":72,"feasibility":73,"implementation":85,"risk":126,"blitsAi":146,"faq":148,"related":161,"datePublished":164,"dateModified":164,"lastVerified":164,"changelog":165,"slug":168},"AI metadata tagging and indexing for media archives","Media archive metadata tagging","AI metadata tagging for media archives","AI tags video and audio archives with searchable metadata. The ABC analysed a million video records in two weeks; RTVE has contracted an AI archive tagging service.","published","AI that watches and listens to a broadcaster's or publisher's video and audio archive and generates rich, structured metadata, such as what is shown, who appears, spoken content, on screen text, logos and objects, so that content makers can find and reuse footage through natural language search instead of relying on the sparse, inconsistent tags an archive accumulated by hand over decades.",[12,13,14,15],"AI video archive tagging","automated media indexing","AI metadata generation for broadcast archives","content archive AI cataloguing",[17],"media-and-entertainment",[19,20],"knowledge-management","operations",[22,23,24],"computer-vision","speech-analytics","classification-and-routing",[26,27],"internal-tools","api","back-office","copilot","early-adopters","content operations","Broadcasters and publishers sit on archives that go back decades: newscasts, interviews,\ndocumentaries and raw, unpublished footage, described by whatever labelling convention was in use\nat the time it was catalogued, often no more than a title, a date and a one line description written\nby hand. Content makers who want to reuse this material, for an anniversary package, a breaking\nstory that needs context, or a compilation, have to search on those sparse terms or scrub through\nhours of tape themselves.\n\nModern content operations make this worse before AI makes it better: the volume of new footage\ngrows every year, on demand and streaming platforms want the archive searchable at the level of a\nclip rather than a programme, and content makers want to search for specific shots in natural\nlanguage, such as the ABC's example of a cricketer with zinc on their nose, rather than the exact\nkeywords a manual index was built around. Multimodal models can watch video and listen to audio,\nand describe both in natural language, which makes richer, clip level metadata practical at\narchive scale.",[],"1. **Ingest.** Video and audio files, old and new, are pulled into an AI enhanced digital asset or\n   archive management system, either as a one off backfill of the historical archive or as part of\n   the daily ingest pipeline for new footage.\n2. **Analyse.** A multimodal model segments the file and, for each segment, describes what is shown\n   (people, objects, scenes, on screen text and logos), transcribes and translates what is said,\n   and recognises known people and public figures, going well beyond fixed keyword lists to\n   describe the actual visual and audio content.\n3. **Structure the output.** The generated descriptions are turned into structured, searchable\n   metadata: entities, timecodes, categories and free text descriptions, consistent across decades\n   of inconsistent source material.\n4. **Human review.** A documentalist or archivist validates and corrects the AI generated metadata,\n   particularly for sensitive categories (identifying named individuals, historically significant\n   events), and that correction feeds back into improving the model over time.\n5. **Search and reuse.** Content makers search the archive in natural language rather than exact\n   keywords, and can retrieve a specific clip in seconds instead of scrubbing through the source\n   tape, freeing time for editorial work rather than manual search.",[36,37,38],"employee-productivity","cost-to-serve","revenue-growth",[40,41,42,43],"search-time-reduction","productivity-gain","interactions-handled","revenue-uplift",{"referenceOrg":45,"inputs":46,"formula":67,"currency":68,"period":69,"resultLabel":70,"caveat":71},"A broadcaster with a 50,000 hour video archive",[47,53,60],{"key":48,"label":49,"low":50,"high":50,"unit":51,"note":52},"archiveHours","Archive hours to catalogue",50000,"hours","The reference archive.",{"key":54,"label":55,"low":56,"high":57,"unit":58,"note":59},"humanTaggingCostPerHour","Fully loaded cost of manual cataloguing per hour of footage",15,40,"USD per hour of footage","Editorial assumption for archivist or documentalist time. Replace with your own cost.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"aiCoverageShare","Share of the archive AI tagging can cover before human review",0.5,0.9,"fraction of archive hours","Editorial assumption, replace with your own. Panorama Audiovisual reports RTVE's medium term goal of cataloguing 198,220 hours of material from its regional centres, and a 2025 contracted scope of 20,000 hours (whose printed components, 18,000 plus 5,000 plus 5,000, sum to 28,000); either figure is a small share of that goal. The ABC's one million video records analysed within two weeks says nothing about the share of an archive AI can cover. Neither source supports a specific coverage share.","archiveHours * humanTaggingCostPerHour * aiCoverageShare","USD","one off","Archive cataloguing cost avoided","Gross cataloguing cost avoided only. It leaves out the cost of running the AI service, the documentalist time still needed to validate and correct output, and the licensing revenue or editorial value the newly discoverable footage may unlock.",[],{"complexity":74,"complexityNote":75,"dataPrerequisites":76,"integrations":80},"medium","The AI analysis itself is largely a managed multimodal model call; the harder work is the archive's own metadata schema and validation workflow, migrating inconsistent decades old records into a structure the AI's output can populate, and integrating with the existing asset management system rather than building a parallel one.",[77,78,79],"Digitized video and audio files, or a digitization pipeline for analogue source material","A target metadata schema (entities, categories, timecodes) that both legacy and AI generated records can populate","Existing catalogue records to migrate or reconcile with newly generated metadata",[81,82,83,84],"Digital asset or media asset management (MAM) system","Multimodal AI model or vendor platform for video and audio analysis","Search index that serves natural language queries over the generated metadata","Documentalist or archivist review and correction interface",{"steps":86,"guardrails":102,"humanInTheLoop":107,"kpisToInstrument":108,"failureModes":113},[87,90,93,96,99],{"title":88,"detail":89},"Pilot on a bounded, representative slice","Choose a few thousand hours that span the archive's range of formats and eras before committing to the full backfill, and measure both metadata quality and documentalist correction time.",{"title":91,"detail":92},"Design the metadata schema before the pipeline","Agree the target categories, entities and timecodes the organization actually needs to search on, so the AI's output is structured to that schema from the start rather than reworked later.",{"title":94,"detail":95},"Build the human review step in, not on","Give documentalists an interface to validate and correct AI generated metadata as part of the ingest workflow, especially for named people and sensitive events, rather than treating review as an afterthought.",{"title":97,"detail":98},"Prioritise by reuse value, not just volume","Catalogue the material most likely to be reused first (anniversaries, recurring formats, frequently requested topics) so the pilot demonstrates value quickly, then expand to the full backfill.",{"title":100,"detail":101},"Integrate into daily ingest","Once quality is proven on the backfill, apply the same pipeline to new footage as it is ingested, so the archive stops growing its backlog even as historical material catches up.",[103,104,105,106],"Human validation of AI generated metadata before it is published as authoritative, particularly identification of named individuals","A documented policy for what the system may not do, such as making rights or licensing decisions from metadata alone","Access controls on sensitive archive material that mirror the organization's existing editorial and legal restrictions","Version history so a correction to AI generated metadata is auditable and reversible","Documentalists and archivists validate and correct AI generated metadata as part of the ingest workflow, with particular attention to identifying named people, historically sensitive footage and anything that will inform a licensing or rights decision; their corrections are the mechanism that improves the model's output over time, not a one time quality check.",[109,110,111,112],"Search time for a content maker to locate a specific clip, before and after","Share of the archive with AI generated metadata, backfill and new intake separately","Documentalist correction rate on AI generated metadata, by category","Archive material reused in new productions or licensed, before and after",[114,117,120,123],{"title":115,"detail":116},"Confident but wrong identification","Multimodal models can misidentify people or events with fluent, plausible sounding metadata. Require human validation before any AI identified person or event is treated as authoritative.",{"title":118,"detail":119},"Metadata schema drift","AI generated categories that do not map cleanly to the archive's existing schema fragment search rather than improving it. Fix the target schema before the pipeline runs at scale.",{"title":121,"detail":122},"Backlog blindness","A backfill project that does not also cover new intake just moves the backlog forward in time. Build the pipeline for daily ingest from the start, even if the backfill runs separately.",{"title":124,"detail":125},"Sensitive content surfaced without control","Making decades of raw, unpublished footage newly searchable can surface material that was never meant for wide internal access. Apply the organization's existing access and editorial controls to AI generated search results, not just to the original files.",{"euAiAct":127,"regulations":130,"guidance":133,"controls":140,"incidents":145},{"tier":128,"basis":129},"context-dependent","Cataloguing objects, scenes, logos and spoken content is not listed in Annex III and is typically minimal risk. Annex III point 1(a) covers remote biometric identification: the automated, one to many matching of a person's face or voice, without their active involvement and typically at a distance, against a reference database of identified individuals to establish who they are, in so far as its use is permitted under relevant Union or national law; it excludes one to one biometric verification. A feature that recognises and names a specific person in archive footage by comparing them against such a database meets that definition and is high risk, while grouping similar looking footage without assigning an identity does not. Article 6(3) lets a provider assess a listed system itself as not high risk when it performs only a narrow procedural task, but that derogation is unlikely to cover a system whose purpose is naming an individual, so treat person recognition as high risk by default. Point 1(b) covers biometric categorisation, inferring a sensitive or protected attribute from a person's face or voice. RTVE's 2025 contract specifies speaker gender identification as a feature; whether that counts as a protected attribute under point 1(b) is contested, since Recital 54 ties that category to attributes protected under GDPR Article 9(1), and sex or gender is not listed there. Treat gender inference from voice as potentially high risk and apply the same governance the organization uses for other biometric systems until that question is settled.",[131,132],"eu-ai-act","gdpr",[134],{"title":135,"issuer":136,"region":137,"url":138,"note":139},"Annex III: High-Risk AI Systems Referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 1(a) covers remote biometric identification, the automated, one to many comparison of a face or voice against a database of identified individuals, relevant where archive tagging recognises and names specific people. Point 1(b) covers biometric categorisation, inferring a sensitive attribute; whether gender inferred from voice, such as RTVE's 2025 contracted speaker gender identification feature, falls under this point is contested and treated here as potentially high risk rather than settled.",[141,142,143,144],"Human validation of any AI generated identification of a named person before publication","Inventory entry for the tagging system, its model provider and the categories of personal data it processes","Documented retention and access policy for AI generated metadata, aligned with the archive's existing editorial controls","Bias and accuracy checks on person recognition across demographic groups before wide rollout",[],{"howToBuild":147},"Blits.ai's own strength is conversational and structured text retrieval, not multimodal video\nanalysis, so the visual and audio tagging step itself calls an external multimodal model or\nvendor platform through a **custom function**. Where Blits.ai fits directly is the audio side and\neverything downstream: its **self hosted transcription and speaker diarization** produces word\nlevel, speaker separated transcripts with automatic language detection, which can feed the same\nmetadata pipeline as the visual tags, and its **knowledge base** with **hybrid retrieval** makes\nthe resulting transcripts and generated metadata searchable in natural language once they are\ningested as documents.\n\nA practical build is an internal **AI agent** for documentalists: it calls the tagging or\ndiarization pipeline through **custom functions** and presents the generated metadata for review\ninside an internal tool, letting a documentalist approve or reject entries in conversation rather\nthan through a separate validation screen. **Guardrails** shape what the agent may do, the\nplatform's **PII masking at the gateway** covers personal data as transcripts pass through it,\nthe **run history and audit trail** record every decision for later review, and the whole\nworkflow can run as an **agentic workflow** with **human in the loop approval** before AI\ngenerated metadata, especially named person identification, is published as authoritative.",[149,152,155,158],{"question":150,"answer":151},"Can AI tag an entire decades old video archive automatically?","It can generate a first pass of rich metadata at a scale manual cataloguing cannot match. The ABC analysed one million video records within two weeks using Gemini in Vertex AI. RTVE has pursued automatic metadata for its archive since 2020 and, under a 2025 tender, contracted Crosspoint and Amplify to automatically analyse 20,000 hours of content; separately, a NexTReT case study (undated) describes a similar service built into RTVE's ARCA system, with an interface for documentalists to validate the results.",{"question":153,"answer":154},"Does AI metadata tagging replace archivists and documentalists?","No, not in the deployments we found. The ABC describes a human in the loop correction process that reviews and refines the AI generated metadata to ensure consistency and meet its quality standards, and NexTReT's RTVE deployment built an interface for documentalists to validate the results. We recommend treating human review of named people and sensitive material as a guardrail on any such system, not an afterthought.",{"question":156,"answer":157},"Is recognising people in archive footage a high risk use under the EU AI Act?","It can be. Object, scene and logo detection is typically minimal risk, but recognising a specific named person by matching their face or voice against a database of known individuals is remote biometric identification under Annex III point 1(a), which is high risk. RTVE's 2025 contract specifies speaker gender identification as a feature, not confirmed live by any source we found; whether inferring gender from voice counts as biometric categorisation under point 1(b) is contested, since that category is tied to attributes protected under GDPR and sex is not among them. We recommend treating gender inference from voice as potentially high risk and applying the same governance as other biometric systems.",{"question":159,"answer":160},"What does a broadcaster get back for cataloguing its archive with AI?","Faster search for content makers (the ABC's case study describes finding the clip they need in seconds instead of taking an hour to find the right video and then scrubbing through hours of tape), a larger share of the archive that is actually discoverable and reusable, and, for organizations that license footage, a larger catalogue of material that can be found and cleared for reuse in the first place.",[162,163],"audio-and-video-transcription-and-captioning","product-content-and-catalog-enrichment","2026-09-28",[166],{"date":164,"note":167},"First published","media-archive-metadata-tagging",[170,202],{"title":171,"useCases":172,"organization":173,"vendors":177,"summary":181,"stage":182,"year":183,"channels":184,"languages":185,"metrics":186,"outcomeDisclosed":187,"sources":188,"verification":197,"grade":199,"id":200,"organizationSlug":201},"RTVE: automatic metadata for the Documentary Archive",[168],{"name":174,"anonymized":175,"country":176,"region":137,"industry":17},"Radiotelevisión Española (RTVE)",false,"ES",[178],{"name":179,"role":180},"NexTReT","integrator","NexTReT describes an AI based automatic metadata service for RTVE's Documentary Archive, integrated into its ARCA document management system with an interface for documentalists to validate the results, to make decades of audiovisual heritage material searchable and reusable. The case study is undated and does not say when the service ran or whether it later ended. Separately, RTVE ran a 2025 tender for automatic archive metadata, reported by Panorama Audiovisual; see the verification note for why that tender is not folded into this record.","production",2026,[26],[],[],true,[189,194],{"url":190,"title":191,"publisher":192,"date":193},"https://www.panoramaaudiovisual.com/en/2025/09/04/crosspoint-and-amplify-will-manage-the-automatic-metadata-of-the-rtve-archive-using-ai/","Crosspoint and Amplify will manage the automatic metadata of the RTVE Archive using AI","Panorama Audiovisual","2025-09-04",{"url":195,"title":196,"publisher":179},"https://nextret.net/en/casos-de-exito/automatizacion-del-metadatado-audiovisual-en-rtve/","Success Story: Audiovisual Metadata Automation at RTVE",{"level":198,"checkedAt":164},"source-verified","C","rtve-archive-metadata-automation",null,{"title":203,"useCases":204,"organization":205,"vendors":209,"summary":213,"stage":182,"year":214,"channels":215,"languages":216,"metrics":218,"outcomeDisclosed":187,"sources":227,"verification":230,"grade":199,"id":231,"organizationSlug":201},"Australian Broadcasting Corporation: AI tagging of the CoDA video archive",[168],{"name":206,"anonymized":175,"country":207,"region":208,"industry":17},"Australian Broadcasting Corporation","AU","asia-pacific",[210],{"name":211,"role":212},"Google Cloud","platform","The ABC modernized its 90 year old archive with Gemini in Vertex AI, adding rich, AI generated metadata to CoDA (Content Digital Archives), its open access platform for journalists, producers and editors. Gemini catalogs video segments, flags incidental footage, and powers semantic search so content makers can describe what they are looking for in natural language instead of scanning vague, human written tags. The editability of the AI generated metadata is presented as a key advantage, with human oversight reviewing and refining output for consistency and to meet the ABC's quality standards, and a human in the loop correction process feeding back into the models over time. The ABC has since integrated the tagging into its daily content pipeline so new uploads are described automatically.",2025,[26],[217],"en",[219],{"kpi":42,"value":220,"unit":221,"qualifier":222,"period":223,"claimant":224,"quote":225,"sourceUrl":226},1000000,"count","exact","within two weeks","vendor","Analysed one million video records within two weeks with the Gemini API in Vertex AI","https://cloud.google.com/customers/abc",[228],{"url":226,"title":229,"publisher":211},"Unlocking Australian history: How the ABC used Gemini in Vertex AI to tag a million video archives in weeks",{"level":198,"checkedAt":164},"abc-archive-ai-metadata-tagging",0,[234],{"kpi":42,"label":235,"unit":221,"aggregate":175,"higherIsBetter":187,"n":236,"nUpTo":232,"median":220,"min":220,"max":220,"byClaimant":237,"vendorOnly":187,"points":238},"Interactions handled",1,{"organization":232,"vendor":236,"regulator":232,"independent":232},[239],{"evidenceId":231,"organization":206,"value":220,"qualifier":222,"claimant":224,"grade":199,"pooled":187},{"low":241,"high":242},375000,1800000,[244,271,293,313],{"slug":162,"title":245,"shortTitle":246,"definition":247,"status":9,"industries":248,"functions":251,"patterns":252,"audience":28,"autonomy":29,"adoptionStage":255,"evidenceCount":256,"publicEvidenceCount":257,"organizations":258,"bestGrade":199,"headline":264,"lastVerified":270,"indexable":187},"AI transcription, subtitles and captions for audio and video","Transcription and captioning","AI that transcribes recorded audio and video, such as podcasts, broadcasts, lessons, interviews and hearings, in several languages, separates the speakers and produces timed transcripts, subtitles and captions for a human editor to check, delivered as files for publishing or the archive.",[17,249,250],"education","cross-industry",[20,19],[23,253,254],"translation","content-generation","mainstream",6,5,[259,260,261,262,263],"Ateme","Comeen","Pacers Sports & Entertainment","Sveriges Television (SVT)","Warner Bros. Discovery",{"kpi":265,"label":266,"unit":267,"n":236,"nUpTo":232,"kind":268,"value":269,"qualifier":222,"claimant":224,"organization":263,"vendorReported":187},"cost-reduction","Cost reduction","percent","reported",50,"2026-09-26",{"slug":163,"title":272,"shortTitle":273,"definition":274,"status":9,"industries":275,"functions":277,"patterns":279,"audience":28,"autonomy":280,"adoptionStage":255,"evidenceCount":281,"publicEvidenceCount":281,"organizations":282,"bestGrade":287,"headline":288,"lastVerified":292,"indexable":187},"AI product content and catalog enrichment for online retail","Product content and catalog enrichment","AI that writes and repairs product content at catalog scale: it drafts titles, descriptions and image alt text, and extracts missing attributes such as color, size and material from supplier text and product images, then checks its own output before the content is published to the store and to search engines. A human owns the rules, the quality thresholds and the exceptions.",[276,250],"retail-and-ecommerce",[278,20],"marketing",[254,22,24],"supervised-agent",4,[283,284,285,286],"Amazon","eBay","Etsy","Walmart","B",{"kpi":289,"label":290,"unit":267,"n":236,"nUpTo":232,"kind":268,"value":57,"qualifier":222,"claimant":291,"organization":283,"vendorReported":175},"quality-score-uplift","Quality score uplift","organization","2026-09-27",{"slug":294,"title":295,"shortTitle":296,"definition":297,"status":9,"industries":298,"functions":300,"patterns":301,"audience":303,"autonomy":29,"adoptionStage":255,"evidenceCount":304,"publicEvidenceCount":304,"organizations":305,"bestGrade":287,"headline":309,"lastVerified":292,"indexable":187},"ambient-clinical-documentation","AI ambient scribe for clinical documentation","Ambient clinical documentation","An AI scribe that listens, with the patient's consent, to the conversation between a clinician and a patient and drafts the clinical note, and often the letter or after visit summary, for the clinician to review, edit and sign in the health record. It documents; it does not diagnose or decide on treatment.",[299],"healthcare",[20,19],[23,302,254],"summarization","employee-facing",3,[306,307,308],"Great Ormond Street Hospital for Children NHS Foundation Trust","Kaiser Permanente","US Department of Veterans Affairs, Veterans Health Administration",{"kpi":310,"label":311,"unit":267,"n":236,"nUpTo":232,"kind":268,"value":312,"qualifier":222,"claimant":291,"organization":306,"vendorReported":175},"handling-time-reduction","Handling time reduction",8.2,{"slug":314,"title":315,"shortTitle":316,"definition":317,"status":9,"industries":318,"functions":322,"patterns":323,"audience":303,"autonomy":29,"adoptionStage":255,"evidenceCount":257,"publicEvidenceCount":257,"organizations":324,"bestGrade":287,"headline":201,"lastVerified":292,"indexable":187},"meeting-summarization-and-action-items","AI meeting summarization and action items","Meeting summaries and action items","AI that summarizes internal and operational meetings, such as team, project, board and case meetings: it transcribes an online or in person meeting with the participants' knowledge and produces a summary, decisions and action items with owners and dates for the organizer to check and share. It is the general purpose tool; client advice meetings and sales calls, which feed a regulated record or a sales pipeline, have their own pages.",[250,319,320,321],"government","technology","professional-services",[19,20],[302,23],[325,326,327,328,329],"U.S. Department of Labor","Ministry of Justice","Softcat","Trace3","Government Digital Service",{"indexable":187,"reasons":331},[],[333,338,343,351,359,365,372,379,386,393,400,406,413,419,425,430,437,443,449,455,461,467,473,478,483,490,497,502,507,514,520,526,532,537],{"id":131,"label":334,"issuer":136,"region":137,"url":335,"description":336,"useCases":337,"indexable":187},"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":132,"label":339,"issuer":136,"region":137,"url":340,"description":341,"useCases":342,"indexable":187},"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":344,"label":345,"issuer":346,"region":347,"url":348,"description":349,"useCases":350,"indexable":187},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":352,"label":353,"issuer":354,"region":355,"url":356,"description":357,"useCases":358,"indexable":187},"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.",83,{"id":360,"label":361,"issuer":136,"region":137,"url":362,"description":363,"useCases":364,"indexable":187},"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":366,"label":367,"issuer":368,"region":137,"url":369,"description":370,"useCases":371,"indexable":187},"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":373,"label":374,"issuer":375,"region":137,"url":376,"description":377,"useCases":378,"indexable":187},"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":380,"label":381,"issuer":382,"region":208,"url":383,"description":384,"useCases":385,"indexable":187},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","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":387,"label":388,"issuer":389,"region":208,"url":390,"description":391,"useCases":392,"indexable":187},"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":394,"label":395,"issuer":396,"region":347,"url":397,"description":398,"useCases":399,"indexable":187},"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":401,"label":402,"issuer":403,"region":355,"url":404,"description":405,"useCases":399,"indexable":187},"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":407,"label":408,"issuer":409,"region":137,"url":410,"description":411,"useCases":412,"indexable":187},"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":414,"label":415,"issuer":416,"region":347,"url":417,"description":418,"useCases":56,"indexable":187},"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":420,"label":421,"issuer":136,"region":137,"url":422,"description":423,"useCases":424,"indexable":187},"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":426,"label":427,"issuer":136,"region":137,"url":428,"description":429,"useCases":424,"indexable":187},"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":431,"label":432,"issuer":433,"region":355,"url":434,"description":435,"useCases":436,"indexable":187},"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":438,"label":439,"issuer":136,"region":137,"url":440,"description":441,"useCases":442,"indexable":187},"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":444,"label":445,"issuer":446,"region":355,"url":447,"description":448,"useCases":442,"indexable":187},"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":450,"label":451,"issuer":452,"region":347,"url":453,"description":454,"useCases":442,"indexable":187},"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":456,"label":457,"issuer":136,"region":137,"url":458,"description":459,"useCases":460,"indexable":187},"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":462,"label":463,"issuer":464,"region":355,"url":465,"description":466,"useCases":460,"indexable":187},"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":468,"label":469,"issuer":382,"region":208,"url":470,"description":471,"useCases":472,"indexable":187},"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":474,"label":475,"issuer":136,"region":137,"url":476,"description":477,"useCases":472,"indexable":187},"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":479,"label":480,"issuer":136,"region":137,"url":481,"description":482,"useCases":472,"indexable":187},"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":484,"label":485,"issuer":486,"region":137,"url":487,"description":488,"useCases":489,"indexable":187},"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":491,"label":492,"issuer":493,"region":355,"url":494,"description":495,"useCases":496,"indexable":187},"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.",8,{"id":498,"label":499,"issuer":136,"region":137,"url":500,"description":501,"useCases":496,"indexable":187},"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":503,"label":504,"issuer":136,"region":137,"url":505,"description":506,"useCases":256,"indexable":187},"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":508,"label":509,"issuer":510,"region":511,"url":512,"description":513,"useCases":257,"indexable":187},"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":515,"label":516,"issuer":517,"region":137,"url":518,"description":519,"useCases":281,"indexable":187},"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":521,"label":522,"issuer":523,"region":137,"url":524,"description":525,"useCases":281,"indexable":187},"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":527,"label":528,"issuer":529,"region":208,"url":530,"description":531,"useCases":304,"indexable":187},"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":533,"label":534,"issuer":136,"region":137,"url":535,"description":536,"useCases":304,"indexable":187},"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":538,"label":539,"issuer":540,"region":355,"url":541,"description":542,"useCases":304,"indexable":187},"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.",1790598302823]