[{"data":1,"prerenderedAt":582},["ShallowReactive",2],{"uc-ad-creative-generation-and-testing":3,"uc-regulations":356},{"useCase":4,"evidence":157,"blitsAiDeployments":250,"benchmarks":251,"indicative":263,"related":266,"indexability":354,"includeUnpublished":163},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":21,"channels":24,"audience":25,"autonomy":26,"adoptionStage":27,"segment":28,"problem":29,"problemStats":30,"howItWorks":31,"valueDrivers":32,"kpis":36,"indicativeValue":41,"macroEstimates":69,"feasibility":70,"implementation":81,"risk":122,"blitsAi":136,"faq":138,"related":148,"datePublished":152,"dateModified":152,"lastVerified":152,"changelog":153,"slug":156},"AI generated ad creative production and testing","Ad creative generation and testing","AI ad creative generation and testing","AI drafts and tests ad creative at scale. Lysol cut cost per asset 80% in a pilot; Google says asset generation helped Event Tickets Center speed up creative 5x.","published","Generative AI that produces the images, video and copy variants a paid media campaign needs, scores or tests them against real performance data, and lets a marketer pick, adjust and publish the ones that work, instead of a manual shoot and edit for every format and audience segment. This page covers making and testing the creative itself; deciding which offer or message to show which customer is covered separately under AI marketing personalization at scale.",[12,13,14,15],"AI ad creative","generative creative for advertising","AI powered creative testing","synthetic ad production",[17,18],"cross-industry","retail-and-ecommerce",[20],"marketing",[22,23],"content-generation","computer-vision",[],"employee-facing","copilot","early-adopters","paid-media","A paid media campaign needs many versions of the same idea: different sizes for different\nplacements, different hero images for different audience segments, different hooks to test\nagainst each other, refreshed often enough that people do not tune the ad out. Producing that\nmany variants by hand, with a photo or video shoot behind each one, is slow and expensive.\nGoogle frames this from the platform side: asset variety is a key ingredient of a successful\ncampaign, and advertisers have said that creating and scaling assets can be one of the hardest\nparts of building and optimizing one.\n\nLysol describes the same bottleneck from the brand side: building high quality, impactful\ncreative remains an expensive, time consuming process, and the team draws a clear line\nbetween experimenting with generative AI tools and actually shipping AI built creative.",[],"1. **Brief the model.** A marketer describes the product, the audience and the message, and can\n   ground the brief in market and competitor research the model helps assemble.\n2. **Generate variants at scale.** The model produces many concepts and asset variations: image\n   backgrounds, expanded or resized images, video cuts, headlines and descriptions, far more\n   than a team could brief and shoot by hand.\n3. **Score before spending media budget.** Some platforms score generated assets against\n   predicted performance, or against a small real budget test, before a variant reaches full\n   campaign spend.\n4. **A human picks and approves.** A marketer reviews the generated set, keeps or edits what\n   works, and rejects what does not; nothing generated is required to go live unreviewed.\n5. **Test and learn in production.** Winning variants run at scale, the campaign platform keeps\n   testing new ones against them, and results feed back into the next brief.",[33,34,35],"speed","employee-productivity","revenue-growth",[37,38,39,40],"productivity-gain","cost-reduction","conversion-rate-uplift","click-through-uplift",{"referenceOrg":42,"inputs":43,"formula":64,"currency":65,"period":66,"resultLabel":67,"caveat":68},"A consumer brand running USD 20 million a year in paid social and search media",[44,50,57],{"key":45,"label":46,"low":47,"high":47,"unit":48,"note":49},"annualMediaSpend","Annual paid media spend",20000000,"USD per year","The reference brand.",{"key":51,"label":52,"low":53,"high":54,"unit":55,"note":56},"creativeProductionShare","Share of media spend that a comparable creative production budget represents",0.1,0.2,"fraction of media spend","Editorial assumption, replace with your own creative production budget as a share of media spend.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"costReduction","Reduction in cost per creative asset from generative production",0.3,0.5,"fraction of creative production cost","Conservative against the benchmark on this page (Lysol reports an 80% reduction in cost per asset for its generative AI pilot), because that figure covers one 8 week pilot on one product line, not sustained production economics across a whole media budget.","annualMediaSpend * creativeProductionShare * costReduction","USD","per year","Creative production cost avoided","Gross production cost avoided only. It leaves out the cost of the generative tools and the human review time they still need, and it makes no claim about whether AI generated creative performs better, the same, or worse than what it replaces; Lysol reports only that its best AI asset performed nearly identical to its best traditional asset in short term sales lift studies, not that it beat it.",[],{"complexity":71,"complexityNote":72,"dataPrerequisites":73,"integrations":77},"low","The generative tools themselves are available inside the major ad platforms with little setup. The real work is process and governance: brand guidelines the model must follow, a review step before anything publishes, and rights clearance for any real person's likeness or voice used as a generation basis.",[74,75,76],"Brand guidelines: fonts, colors, tone of voice and claims the brand can substantiate","A library of approved product images or footage to ground generated variants","Historical creative performance data to brief and later score new variants against",[78,79,80],"The paid media platforms running the campaign (their own generative asset tools)","Digital asset management or brand guideline systems","Rights and consent records for any person whose likeness or voice is used",{"steps":82,"guardrails":98,"humanInTheLoop":103,"kpisToInstrument":104,"failureModes":109},[83,86,89,92,95],{"title":84,"detail":85},"Start with one campaign and one clear brief","Pick a live campaign with a well defined product and message, as Lysol did for one product line, rather than trying to generalize the process across every brand at once.",{"title":87,"detail":88},"Set a responsible AI policy before generating any likeness","Decide up front how consent and compensation work for any real person's face or voice used as the basis for AI generated video or images, and put it in writing before the pilot starts.",{"title":90,"detail":91},"Generate wide, then narrow with a human","Let the model produce far more concepts and variants than you would ever brief by hand, then have the marketing team pick the strongest few to refine, rather than publishing the first pass.",{"title":93,"detail":94},"Score against real performance, not opinion","Compare generated variants against your best existing asset with an actual sales or conversion lift test, not only internal preference, before scaling media spend behind them.",{"title":96,"detail":97},"Only then widen the process","Once a pilot shows generated creative can match or beat the traditional process on cost and performance, extend the brief, review and scoring steps to more product lines.",[99,100,101,102],"Every generated asset reviewed by a marketer against brand guidelines before it can publish","Written consent and compensation on file for any real person's likeness or voice used to generate creative","Generated content labelled as AI generated wherever the platform or the law requires it","No generated claim that goes beyond what the brand can substantiate","Marketers write the brief, review every generated variant before it is added to a live campaign, and own the responsible AI policy for using anyone's likeness or voice. A brand or legal reviewer signs off before a new product line or a new use of a real person's likeness goes live.",[105,106,107,108],"Cost per finished creative asset, generated versus the traditional process","Time from brief to a launch ready asset","Performance of generated variants against the best existing asset, on the same audience","Share of generated variants a reviewer rejects or substantially edits before publishing",[110,113,116,119],{"title":111,"detail":112},"Off brand or exaggerated output","The model drifts from brand guidelines or overstates a product claim. Catch it with a mandatory brand and claims review before anything publishes.",{"title":114,"detail":115},"A likeness used without real consent","An AI generated video uses a real person's face or voice without clear, compensated consent. Require signed consent on file before generation, not after.",{"title":117,"detail":118},"Comparing against a weak baseline","A generated variant looks like a win only because the traditional asset it replaced was already underperforming. Test against your actual best asset, not an average one.",{"title":120,"detail":121},"Volume without a review bottleneck plan","Generating far more variants than the team can review pushes weak review, or a temptation to skip it. Size the generation volume to what the review process can actually handle.",{"euAiAct":123,"regulations":126,"guidance":129,"controls":130,"incidents":135},{"tier":124,"basis":125},"limited","Article 50(2) puts a machine readable marking duty on the provider of the generative tool itself (Google, for example, marks Performance Max images with SynthID), with an exception only where the tool performs assistive standard editing or does not substantially alter the input. Article 50(4) separately requires the deploying brand to disclose when it generates or manipulates image, audio or video content that is a deep fake: content that resembles a real person, place or event and would falsely appear authentic, such as the synthetic \"digital twin\" likenesses used in the Lysol pilot. Content that is evidently artistic, creative, satirical or fictional is not exempt from that disclosure; the article only lets the brand make it in a manner that does not hamper the display or enjoyment of the work. The separate exception for content under human review or editorial control, with someone holding editorial responsibility, applies only to AI generated text published to inform the public on matters of public interest, not to image or video ad creative, so it does not exempt anything on this page. Lysol's pilot is a US deployment and the source does not describe it as operating under the EU AI Act.",[127,128],"eu-ai-act","gdpr",[],[131,132,133,134],"Written, dated consent from any real person whose likeness or voice is used as a generation basis","A brand and legal review step before any generated asset reaches a live campaign","AI generated content labelled in line with the platform's and the law's disclosure rules","Log of which assets in a campaign were AI generated, for audit and disclosure",[],{"howToBuild":137},"Blits.ai does not replace a dedicated ad creative platform, but an **AI agent** can run the\nbrief and review workflow around one. The agent draws on a **knowledge base** of approved\nbrand guidelines and substantiated product claims, uses the built in **image generation**\ntool to produce draft visuals and the built in **image search** tool, which searches stock\nphotography, for reference and placeholder imagery, and calls the ad platform's own\ngenerative and publishing APIs through **custom functions**.\n\nAn **agentic workflow** with **human in the loop approval** holds every generated asset for a\nmarketer to approve or reject against the brand guidelines before a custom function publishes\nit, and **guardrails** check generated copy against the same claims policy the knowledge base\nholds. **Test suites** can run a fixed set of briefs through the agent on every change to\ncatch drift from brand guidelines before it reaches a live campaign, and the workflow's **run\nhistory** keeps a full audit trail of every run, including which generated assets a marketer\napproved or rejected. The platform is model agnostic, so a marketing team can change the\nunderlying model without rebuilding the workflow.",[139,142,145],{"question":140,"answer":141},"Does AI generated ad creative actually perform better than creative made the normal way?","The public evidence on this page does not show a performance win, only a cost and speed one. Lysol reports that its best generative AI asset performed \"nearly identical\" to its best traditional asset in short term sales lift studies. Google says its asset generation feature helped Event Tickets Center speed up creative production 5x, and 1-800-FLOWERS.COM's CMO says the same feature saves the creative team time. Treat generative creative as a way to produce and test more variants for the same budget, not as a guaranteed lift.",{"question":143,"answer":144},"Who owns the risk if an AI generated ad uses someone's likeness?","Primarily the brand running the campaign, though the agency, the ad platform and the model provider can share responsibility depending on their role and contract. Lysol's pilot addressed the likeness risk by working with consenting, compensated actors as the basis for its AI generated likenesses and setting a responsible AI policy before generating anything, which is the standard any deployment should meet.",{"question":146,"answer":147},"Do AI generated ads need to be labelled as AI generated?","Yes, in the EU, where the AI Act's transparency duties have applied since 2 August 2026. Article 50(2) requires the provider of the generative tool to mark its own output as artificially generated in a machine readable way. Article 50(4) separately requires the deploying brand to disclose when an ad is a deep fake, meaning it resembles a real person, place or event and would falsely appear authentic; content that is evidently artistic, creative or fictional still needs disclosure, only in a manner that does not hamper the work. Google says it watermarks generated Performance Max images with SynthID and adds open metadata identifying them as AI generated.",[149,150,151],"personalized-marketing-at-scale","marketing-content-compliance-copilot","product-content-and-catalog-enrichment","2026-09-29",[154],{"date":152,"note":155},"First published","ad-creative-generation-and-testing",[158,204,231],{"title":159,"useCases":160,"organization":161,"vendors":167,"summary":174,"stage":175,"year":176,"channels":177,"languages":178,"metrics":180,"outcomeDisclosed":189,"sources":190,"verification":199,"grade":201,"id":202,"organizationSlug":203},"Lysol's end to end generative AI creative pilot",[156],{"name":162,"anonymized":163,"country":164,"region":165,"industry":166},"Lysol (Reckitt)",false,"US","north-america","manufacturing",[168,171],{"name":169,"role":170},"Google","platform",{"name":172,"role":173},"BCG","integrator","Lysol, a Reckitt brand, piloted a fully generative AI creative development process for its Pivot Laundry Sanitizer product, in partnership with BCG and Google. Over an 8 week sprint, the team used Gemini to generate and score hundreds of creative concepts, then used Veo and Imagen for synthetic production of broadcast ready 15 and 30 second video spots, with consenting, compensated actors used as the basis for AI generated likenesses, replacing a physical shoot.","pilot",2026,[],[179],"en",[181],{"kpi":38,"value":182,"unit":183,"qualifier":184,"period":185,"claimant":186,"quote":187,"sourceUrl":188},80,"percent","exact","8 week pilot, one product line","organization","We achieved an 80% reduction in cost per asset compared with our traditional process.","https://business.google.com/en-all/think/ai-excellence/lysol-generative-ai-creative-development-case-study/",true,[191,195],{"url":188,"title":192,"publisher":193,"date":194},"How Lysol slashed production costs by 80% and hit 10X speed with gen AI creative","Think with Google","2026-01-01",{"url":196,"title":197,"publisher":198},"https://www.reckitt.com/brands/lysol/","Lysol","Reckitt",{"level":200,"checkedAt":152},"source-verified","B","lysol-generative-ai-creative-pilot",null,{"title":205,"useCases":206,"organization":207,"vendors":209,"summary":211,"stage":175,"year":212,"channels":213,"languages":214,"metrics":215,"outcomeDisclosed":189,"sources":223,"verification":228,"grade":229,"id":230,"organizationSlug":203},"Event Tickets Center: 5x faster creative production with Performance Max asset generation",[156],{"name":208,"anonymized":163,"country":164,"region":165,"industry":18},"Event Tickets Center",[210],{"name":169,"role":170},"Event Tickets Center was one of the earliest beta testers of Google's asset generation feature in Performance Max. Google says the feature helped the Event Tickets Center team accelerate creative production 5x, with less time and effort.",2024,[],[179],[216],{"kpi":37,"value":217,"unit":218,"qualifier":184,"period":219,"claimant":220,"quote":221,"sourceUrl":222},5,"multiplier","beta period, Performance Max asset generation","vendor","Event Tickets Center was one of the earliest beta testers for asset generation in Performance Max, which has helped the team accelerate creative production by 5x with less time and effort.","https://blog.google/products/ads-commerce/ai-creativity-google-marketing-live/",[224],{"url":222,"title":225,"publisher":226,"date":227},"New ways Google AI can improve ads performance and creativity","Google Ads","2024-05-21",{"level":200,"checkedAt":152},"C","event-tickets-center-performance-max-asset-generation",{"title":232,"useCases":233,"organization":234,"vendors":236,"summary":238,"stage":175,"year":239,"channels":240,"languages":241,"metrics":242,"outcomeDisclosed":163,"sources":243,"verification":248,"grade":229,"id":249,"organizationSlug":203},"1-800-FLOWERS.COM: generated assets in Performance Max",[156],{"name":235,"anonymized":163,"country":164,"region":165,"industry":18},"1-800-FLOWERS.COM, Inc.",[237],{"name":169,"role":170},"1-800-FLOWERS.COM, Inc. adopted Google's generative asset feature in Performance Max. Its CMO, Jason John, says the generated assets save his creative team valuable time and let it craft personalized visuals that resonate with its audience.",2023,[],[179],[],[244],{"url":245,"title":246,"publisher":226,"date":247},"https://blog.google/products/ads-commerce/get-creative-with-generative-ai-in-performance-max/","Get creative with generative AI in Performance Max","2023-11-07",{"level":200,"checkedAt":152},"flowers-performance-max-generated-assets",0,[252,258],{"kpi":38,"label":253,"unit":183,"aggregate":189,"higherIsBetter":189,"n":254,"nUpTo":250,"median":182,"min":182,"max":182,"byClaimant":255,"vendorOnly":163,"points":256},"Cost reduction",1,{"organization":254,"vendor":250,"regulator":250,"independent":250},[257],{"evidenceId":202,"organization":162,"value":182,"qualifier":184,"claimant":186,"grade":201,"pooled":189},{"kpi":37,"label":259,"unit":218,"aggregate":189,"higherIsBetter":189,"n":254,"nUpTo":250,"median":217,"min":217,"max":217,"byClaimant":260,"vendorOnly":189,"points":261},"Productivity gain",{"organization":250,"vendor":254,"regulator":250,"independent":250},[262],{"evidenceId":230,"organization":208,"value":217,"qualifier":184,"claimant":220,"grade":229,"pooled":189},{"low":264,"high":265},600000,2000000,[267,298,321,338],{"slug":149,"title":268,"shortTitle":269,"definition":270,"status":9,"industries":271,"functions":275,"patterns":277,"audience":280,"autonomy":281,"adoptionStage":282,"evidenceCount":283,"publicEvidenceCount":284,"organizations":285,"bestGrade":201,"headline":293,"lastVerified":297,"indexable":189},"AI marketing personalization at scale","Marketing personalization at scale","AI that runs marketing campaigns at the level of the individual: it decides for each customer which product, offer, message or content to show next across email, app, web and paid media, and generates the matching copy and creative variants within brand and compliance rules. It is the marketing team's engine across many campaigns and channels, not an agent that converses with the customer.",[17,272,273,18,274],"travel-and-hospitality","media-and-entertainment","banking",[20,276],"sales",[278,279,22],"recommendation-and-personalization","prediction-and-scoring","back-office","supervised-agent","mainstream",8,7,[286,287,288,289,290,291,292],"Amazon","Catchtable","Commonwealth Bank of Australia","Radisson Hotel Group","Square Enix","Swarovski","Virgin Voyages",{"kpi":39,"label":294,"unit":183,"n":254,"nUpTo":250,"kind":295,"value":296,"qualifier":184,"claimant":220,"organization":287,"vendorReported":189},"Conversion uplift","reported",30,"2026-09-27",{"slug":150,"title":299,"shortTitle":300,"definition":301,"status":9,"industries":302,"functions":307,"patterns":310,"audience":25,"autonomy":26,"adoptionStage":27,"evidenceCount":217,"publicEvidenceCount":314,"organizations":315,"bestGrade":201,"headline":319,"lastVerified":297,"indexable":189},"AI copilot for marketing content with compliance pre review","Marketing content and compliance","A copilot that drafts campaign copy, product explainers and social posts on brand and in the customer's language from approved product facts, then runs a first pass compliance check against advertising rules and required disclosures, flagging unsupported claims and missing warnings before a human in marketing compliance approves publication.",[17,274,303,304,305,306],"insurance","payments","wealth-and-asset-management","pharma-and-life-sciences",[20,308,309],"regulatory-compliance","legal",[22,311,312,313],"rag-knowledge-assistant","classification-and-routing","translation",3,[316,317,318],"Ally Financial","JPMorgan Chase","Klarna",{"kpi":37,"label":259,"unit":183,"n":254,"nUpTo":250,"kind":295,"value":320,"qualifier":184,"claimant":186,"organization":316,"vendorReported":163},34,{"slug":151,"title":322,"shortTitle":323,"definition":324,"status":9,"industries":325,"functions":326,"patterns":328,"audience":280,"autonomy":281,"adoptionStage":282,"evidenceCount":329,"publicEvidenceCount":329,"organizations":330,"bestGrade":201,"headline":334,"lastVerified":297,"indexable":189},"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.",[18,17],[20,327],"operations",[22,23,312],4,[286,331,332,333],"eBay","Etsy","Walmart",{"kpi":335,"label":336,"unit":183,"n":254,"nUpTo":250,"kind":295,"value":337,"qualifier":184,"claimant":186,"organization":286,"vendorReported":163},"quality-score-uplift","Quality score uplift",40,{"slug":339,"title":340,"shortTitle":341,"definition":342,"status":9,"industries":343,"functions":345,"patterns":346,"audience":25,"autonomy":281,"adoptionStage":27,"evidenceCount":314,"publicEvidenceCount":314,"organizations":347,"bestGrade":229,"headline":350,"lastVerified":297,"indexable":189},"marketing-and-product-content-localization","AI localization of marketing, product and web content","Content localization","AI that translates and adapts an organization's commercial content, such as campaigns, emails, product pages, help content and websites, for each market and language, using the brand's glossary, style guide and past approved translations, and routes the output to human linguists and local marketers for review in proportion to how visible and risky the content is.",[17,18,344,166],"professional-services",[20],[313,22],[348,349,291],"Bosch Digital","Lionbridge",{"kpi":351,"label":352,"unit":218,"n":254,"nUpTo":250,"kind":295,"value":353,"qualifier":184,"claimant":220,"organization":291,"vendorReported":189},"processing-time-reduction","Cycle time reduction",10,{"indexable":189,"reasons":355},[],[357,364,369,377,384,391,397,404,412,419,426,433,439,445,452,459,465,472,478,484,490,497,502,508,513,518,523,529,535,541,548,554,560,566,571,576],{"id":127,"label":358,"issuer":359,"region":360,"url":361,"description":362,"useCases":363,"indexable":189},"EU AI Act","European Union","europe","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.",230,{"id":128,"label":365,"issuer":359,"region":360,"url":366,"description":367,"useCases":368,"indexable":189},"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.",207,{"id":370,"label":371,"issuer":372,"region":373,"url":374,"description":375,"useCases":376,"indexable":189},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":378,"label":379,"issuer":380,"region":165,"url":381,"description":382,"useCases":383,"indexable":189},"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.",92,{"id":385,"label":386,"issuer":387,"region":360,"url":388,"description":389,"useCases":390,"indexable":189},"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.",71,{"id":392,"label":393,"issuer":359,"region":360,"url":394,"description":395,"useCases":396,"indexable":189},"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":398,"label":399,"issuer":400,"region":360,"url":401,"description":402,"useCases":403,"indexable":189},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",50,{"id":405,"label":406,"issuer":407,"region":408,"url":409,"description":410,"useCases":411,"indexable":189},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":413,"label":414,"issuer":415,"region":408,"url":416,"description":417,"useCases":418,"indexable":189},"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":420,"label":421,"issuer":422,"region":165,"url":423,"description":424,"useCases":425,"indexable":189},"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.",22,{"id":427,"label":428,"issuer":429,"region":373,"url":430,"description":431,"useCases":432,"indexable":189},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",21,{"id":434,"label":435,"issuer":359,"region":360,"url":436,"description":437,"useCases":438,"indexable":189},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",17,{"id":440,"label":441,"issuer":442,"region":360,"url":443,"description":444,"useCases":438,"indexable":189},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",{"id":446,"label":447,"issuer":448,"region":165,"url":449,"description":450,"useCases":451,"indexable":189},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",16,{"id":453,"label":454,"issuer":455,"region":373,"url":456,"description":457,"useCases":458,"indexable":189},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":460,"label":461,"issuer":359,"region":360,"url":462,"description":463,"useCases":464,"indexable":189},"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":466,"label":467,"issuer":468,"region":165,"url":469,"description":470,"useCases":471,"indexable":189},"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":473,"label":474,"issuer":475,"region":165,"url":476,"description":477,"useCases":471,"indexable":189},"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":479,"label":480,"issuer":359,"region":360,"url":481,"description":482,"useCases":483,"indexable":189},"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":485,"label":486,"issuer":487,"region":373,"url":488,"description":489,"useCases":483,"indexable":189},"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":491,"label":492,"issuer":493,"region":165,"url":494,"description":495,"useCases":496,"indexable":189},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",11,{"id":498,"label":499,"issuer":359,"region":360,"url":500,"description":501,"useCases":496,"indexable":189},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":503,"label":504,"issuer":505,"region":360,"url":506,"description":507,"useCases":353,"indexable":189},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",{"id":509,"label":510,"issuer":407,"region":408,"url":511,"description":512,"useCases":353,"indexable":189},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":514,"label":515,"issuer":359,"region":360,"url":516,"description":517,"useCases":353,"indexable":189},"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":519,"label":520,"issuer":359,"region":360,"url":521,"description":522,"useCases":353,"indexable":189},"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":524,"label":525,"issuer":359,"region":360,"url":526,"description":527,"useCases":528,"indexable":189},"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.",9,{"id":530,"label":531,"issuer":532,"region":165,"url":533,"description":534,"useCases":284,"indexable":189},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",{"id":536,"label":537,"issuer":359,"region":360,"url":538,"description":539,"useCases":540,"indexable":189},"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.",6,{"id":542,"label":543,"issuer":544,"region":545,"url":546,"description":547,"useCases":217,"indexable":189},"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":549,"label":550,"issuer":551,"region":360,"url":552,"description":553,"useCases":329,"indexable":189},"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":555,"label":556,"issuer":557,"region":360,"url":558,"description":559,"useCases":329,"indexable":189},"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":561,"label":562,"issuer":563,"region":408,"url":564,"description":565,"useCases":314,"indexable":189},"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":567,"label":568,"issuer":359,"region":360,"url":569,"description":570,"useCases":314,"indexable":189},"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":572,"label":573,"issuer":359,"region":360,"url":574,"description":575,"useCases":314,"indexable":189},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":577,"label":578,"issuer":579,"region":165,"url":580,"description":581,"useCases":314,"indexable":189},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790683491996]