[{"data":1,"prerenderedAt":543},["ShallowReactive",2],{"uc-power-line-vegetation-management":3,"uc-regulations":331},{"useCase":4,"evidence":174,"blitsAiDeployments":244,"benchmarks":245,"indicative":252,"related":255,"indexability":329,"includeUnpublished":180},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":25,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"problem":32,"problemStats":33,"howItWorks":39,"valueDrivers":40,"kpis":45,"indicativeValue":48,"macroEstimates":70,"feasibility":71,"implementation":84,"risk":126,"blitsAi":156,"faq":158,"related":168,"datePublished":169,"dateModified":169,"lastVerified":169,"changelog":170,"slug":173},"AI vegetation management for power lines","Power line vegetation management","AI vegetation management for utility power lines","Satellite imagery and AI show where trees threaten power lines, so utilities trim by risk, not by calendar. National Grid in Massachusetts and Entergy use it.","published","AI that analyses satellite, aerial or lidar imagery of the land along power lines to estimate where and how fast vegetation will grow into the lines or fall onto them, and turns that into a risk based trimming and hazard tree removal plan, replacing fixed trimming cycles and manual patrols.",[12,13,14,15,16],"satellite vegetation management","utility vegetation management with AI","risk based tree trimming","hazard tree detection","right of way vegetation monitoring",[18],"energy-and-utilities",[20,21],"network-operations","field-service",[23,24],"computer-vision","prediction-and-scoring",[26,27],"internal-tools","mobile-app","employee-facing","copilot","early-adopters","grid","Vegetation growing into or falling onto lines interrupts supply. The NERC transmission vegetation\nstandard notes that major outages and operational problems have resulted from overgrown vegetation\ninterfering with transmission lines, and AiDASH calls vegetation one of the grid's biggest threats.\nAiDASH says vegetation programs have for decades largely followed a fixed formula: trim a set share\nof the system each year and repeat the cycle. Its case studies describe Entergy on a standardized\nfive year cycle, maintaining about 20% of its system each year, and National Grid's Massachusetts\nnetwork on a typical five year cycle, where deciding whether a circuit needed pruning took manual\nfield reviews.\n\nAiDASH notes that growth rates vary by region, species, weather and circuit, so a uniform cycle\nmeans unnecessary work in some areas and elevated risk in others. According to AiDASH,\nNational Grid had deferred work for four years running rather than fund it, and in the year after\npruning its average circuit saw only an 8% reduction in customers interrupted and no significant\nimprovement in tree events or customer minutes interrupted. The same case study describes rising\ncosts for routine maintenance and strict regulations to prevent outages and manage fire risk.",[34],{"statement":35,"sourceTitle":36,"sourceUrl":37,"year":38},"NERC's transmission vegetation standard FAC-003-5 states that major outages and operational problems have resulted from interference between overgrown vegetation and transmission lines.","FAC-003-5 Transmission Vegetation Management","https://www.nerc.com/pa/Stand/Reliability%20Standards/FAC-003-5.pdf",2021,"1. **Image the whole network.** Satellite imagery, supplemented by aerial or lidar data where\n   needed, covers every span, repeated as often as the budget allows.\n2. **Measure the vegetation.** Computer vision models identify trees, their height, their distance\n   to the conductors and signs of poor health, span by span.\n3. **Predict growth and risk.** Models combine species, growth rates, weather and outage history to\n   estimate when each span will become a risk.\n4. **Plan the work.** Circuits are scheduled for trimming when their risk warrants it, hazard trees\n   are prioritised for removal, and budgets are allocated where they avoid the most outages.\n5. **Dispatch and audit.** Work goes to contractors with maps, and new imagery checks that the\n   work was done and done well.\n6. **Measure reliability.** Tree related outages, customers interrupted and minutes interrupted are\n   tracked per circuit, and the results tune the risk model.",[41,42,43,44],"cost-to-serve","risk-reduction","customer-experience","employee-productivity",[46,47],"cost-savings","cost-reduction",{"referenceOrg":49,"inputs":50,"formula":65,"currency":66,"period":67,"resultLabel":68,"caveat":69},"An electric distribution utility with 20,000 line miles",[51,58],{"key":52,"label":53,"low":54,"high":55,"unit":56,"note":57},"vegBudget","Annual vegetation management spend",20000000,40000000,"USD per year","Editorial assumption for a network of this size, replace with your own budget.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"savingShare","Share of spend saved by trimming on risk instead of on a fixed cycle",0.03,0.1,"fraction of vegetation spend","Editorial assumption, deliberately below the 20% expense reduction AiDASH claims in its own marketing on the Entergy page, which is a vendor average and not an evidence record. For comparison, AiDASH reports USD 2M in efficiencies in National Grid's first few years and USD 1M in avoided cost on about 13,500 line miles, without saying whether the two overlap.","vegBudget * savingShare","USD","per year","Vegetation management spend avoided or redeployed","Counts spend only. It leaves out imagery and software costs, the reliability value of fewer tree events and customer minutes interrupted (which AiDASH reports for National Grid's worked circuits) or of beating reliability targets (which AiDASH reports for Entergy), avoided storm restoration cost and reduced wildfire risk.",[],{"complexity":72,"complexityNote":73,"dataPrerequisites":74,"integrations":79},"medium","Vendors deliver the imagery and models as a service. The work for the utility is a clean network model in GIS, outage history per circuit, and changing contracts and planning from fixed cycles to risk based work.",[75,76,77,78],"A GIS model of the network with spans, circuits and voltage","Outage history per circuit with cause codes","Trimming history and contractor work records","Local knowledge of species, growth and regulatory clearance rules",[80,81,82,83],"Geographic information system of the network","Outage management system for cause coded outage history","Work management system and contractor portals","Mobile apps for crews and auditors",{"steps":85,"guardrails":101,"humanInTheLoop":106,"kpisToInstrument":107,"failureModes":113},[86,89,92,95,98],{"title":87,"detail":88},"Prove it on real territory first","Run the imagery and risk model on a large, representative area and compare its risk ranking with outage history and a field check before changing the plan. National Grid, according to AiDASH, ran its 2020 proof of concept on its entire Massachusetts footprint rather than a portion of it, and the first model run produced its FY2021 work plan.",{"title":90,"detail":91},"Agree the clearance rules and risk appetite","Encode regulatory clearance requirements and decide how much risk the utility accepts per circuit type, so the model plans to rules, not just to growth.",{"title":93,"detail":94},"Move the plan from cycles to risk","Schedule circuits by predicted risk, keep a floor of mandatory inspections, and give planners the final say on the plan.",{"title":96,"detail":97},"Change the contracts","Contractor agreements built on miles trimmed per cycle need to change to work orders by span and risk, with imagery based audits.",{"title":99,"detail":100},"Measure reliability per circuit","Track tree related events, customers interrupted and minutes interrupted on treated circuits against the previous cycle and against untreated comparable circuits.",[102,103,104,105],"Regulatory clearance and inspection obligations always override the model's schedule","Planners approve the annual plan and any deferral of work on a high risk circuit","Field verification of high risk findings before removal of trees on private land","Imagery of private property used only for network maintenance purposes","Vegetation planners and arborists review the model's risk ranking, approve the work plan and decide on hazard tree removals. Field crews confirm conditions on site and report back, and reliability engineers review outcomes per circuit each year.",[108,109,110,111,112],"Tree related outage events per 100 line miles, before and after","Customers interrupted and customer minutes interrupted from tree causes (SAIFI and SAIDI contribution)","Vegetation spend per line mile","Share of high risk spans treated before the storm or fire season","Audit pass rate of contractor work checked against new imagery",[114,117,120,123],{"title":115,"detail":116},"Model trusted over the rulebook","A span with low predicted risk still has a legal clearance obligation. Keep regulatory rules as hard constraints.",{"title":118,"detail":119},"Stale imagery","Imagery that is too old misses storm damage and fast growth. Agree refresh frequency by region and season.",{"title":121,"detail":122},"No change in contracts","Contractors paid per mile on a cycle keep trimming on the cycle. Align contracts with risk based work orders.",{"title":124,"detail":125},"Benefits that cannot be shown","Reliability varies with weather, so one good year proves little. Compare treated and comparable untreated circuits over several years.",{"euAiAct":127,"regulations":130,"guidance":135,"controls":150,"incidents":155},{"tier":128,"basis":129},"context-dependent","Annex III point 2 makes AI systems high risk when they are intended as safety components in the management and operation of the supply of electricity. Recital 55 defines such components as systems used to directly protect the physical integrity of critical infrastructure or the health and safety of persons and property. A system that only feeds a multi year trimming plan, which vegetation planners review and approve before crews act, informs maintenance rather than directly protecting the network, and is then usually minimal risk. The assessment changes when the design acts directly on protection, for example when vegetation risk scores automatically trigger fire risk protection settings or switch lines off without a person deciding; such a system should be assessed as a possible safety component. Standard GDPR duties apply where imagery shows private property or people.",[131,132,133,134],"eu-ai-act","gdpr","nist-ai-rmf","iso-42001",[136,140,146],{"title":36,"issuer":137,"region":138,"url":37,"note":139},"North American Electric Reliability Corporation (NERC)","north-america","The mandatory reliability standard for vegetation clearances on North American transmission lines, which any AI based plan must still meet.",{"title":141,"issuer":142,"region":143,"url":144,"note":145},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 2 lists safety components in the management and operation of critical infrastructure, including electricity supply.",{"title":147,"issuer":142,"region":143,"url":148,"note":149},"Recital 55, safety components of critical infrastructure","https://artificialintelligenceact.eu/recital/55/","Defines safety components as systems that directly protect the physical integrity of critical infrastructure or the health and safety of persons and property, the test that decides the tier here.",[151,152,153,154],"Documented mapping of regulatory clearance rules into the planning model","Annual review of model performance against tree related outages per circuit","Records of approvals for deferred work on high risk spans","Data protection rules for imagery of private land",[],{"howToBuild":157},"The imagery analysis and risk model come from the utility's vegetation platform. Blits.ai adds\nthe conversations around the work. An **AI agent** with a **SQL knowledge base** over risk scores,\nwork plans and outage history lets planners ask which circuits are due and why, and a\n**knowledge base** with the utility's clearance rules and vegetation policy answers questions\nconsistently.\n\nFor customers and landowners, an agent on **web chat, WhatsApp, SMS and voice** explains planned\ntrimming on their property, answers policy questions from the approved knowledge base and books\nor changes access appointments through **custom functions**, with **human handover** to a\nvegetation specialist for disputes and tree removal requests. **Agentic workflows** can prepare\nadvance notices for approval, **guardrails** and **PII masking** protect customer data, and the\nplatform is model agnostic with EU and UAE data residency.",[159,162,165],{"question":160,"answer":161},"What results do utilities report from AI vegetation management?","AiDASH reports three sets of figures for National Grid. Its latest account of the Massachusetts program gives average improvements of 22% in tree events, 29% in customers impacted and 43% in customer minutes interrupted on circuits worked in FY2022 to 2025, measured 12 months after each circuit is worked; a case study on the Massachusetts service area reports declines of 30%, 38% and 55% on the same measures in the year after pruning; and its page on a NextGrid Alliance Summit 2025 talk by National Grid's vegetation strategy manager lists decreases of 26.4%, 30.2% and 46.5%, without naming a service area. The pages do not say which period the improvements are compared against, so treat them as indicative. For Entergy, AiDASH reports that it beat its vegetation reliability (Veg SAIFI) targets by over 30% in 2020 and 2021 on flat budgets, and quotes an Entergy vice president saying vegetation impacts to customers improved by more than 20% within the first year.",{"question":163,"answer":164},"Does satellite imagery replace field patrols and lidar?","Not fully. AiDASH's National Grid case study describes satellite imagery as a quick way to see vegetation conditions over an entire service area and a foundation for building a plan, and its later account notes that National Grid now also has lidar data and drone data from three in house drone pilots alongside the satellite outputs. Our advice: keep lidar, drones or field checks for precise clearance measurement and for verifying high risk findings before work on private land.",{"question":166,"answer":167},"Is AI vegetation management regulated as high risk AI?","Usually not under the EU AI Act when it only informs a maintenance plan that planners approve, because it does not directly protect the network in the sense of Recital 55. A design in which the risk scores directly trigger protective actions, such as fire risk settings or switching lines off, needs a proper assessment as a possible safety component. Existing clearance rules, such as NERC FAC-003 for North American transmission lines, still apply to the plan it produces.",[],"2026-09-27",[171],{"date":169,"note":172},"First published","power-line-vegetation-management",[175,221],{"title":176,"useCases":177,"organization":178,"vendors":182,"summary":186,"stage":187,"year":38,"channels":188,"languages":189,"metrics":191,"outcomeDisclosed":204,"sources":205,"verification":216,"grade":218,"id":219,"organizationSlug":220},"National Grid: satellite and AI based, condition driven vegetation management in Massachusetts",[173],{"name":179,"anonymized":180,"country":181,"region":138,"industry":18},"National Grid",false,"US",[183],{"name":184,"role":185},"AiDASH","platform","National Grid began working with AiDASH in 2020 in Massachusetts, a service area of over 13,500 line miles and more than 1.3 million customers. According to AiDASH, the utility had been on a five year trim cycle and had deferred work for four years running rather than fund it; in August 2020 it ran a proof of concept on its entire Massachusetts footprint, and the first model run produced its FY2021 work plan. It adopted the Intelligent Vegetation Management System in 2021, which uses satellite imagery and AI to show vegetation conditions across the network, and moved to condition based trimming, with circuits now on cycles of four to seven years. AiDASH reports $1M in avoided cost from dropping manual field reviews and $2M in efficiencies in the first few years, without saying whether the two overlap. It reports three sets of reliability results: on Massachusetts circuits worked in FY2022 to 2025, average improvements of 22% in tree events, 29% in customers impacted and 43% in customer minutes interrupted, measured 12 months after each circuit is worked; in its case study a 30% decline in tree related events, 38% in customers interrupted and 55% in customer minutes interrupted in the year after circuits were pruned; and from a talk by National Grid's vegetation strategy manager at the NextGrid Alliance Summit 2025, decreases of 26.4%, 30.2% and 46.5% on the same three measures.","production",[26],[190],"en",[192,200],{"kpi":46,"value":193,"unit":194,"currency":66,"qualifier":195,"period":196,"claimant":197,"quote":198,"sourceUrl":199},2000000,"currency","exact","in the first few years of adopting IVMS","vendor","$2M in efficiencies realized – in the first few years of adopting IVMS.","https://www.aidash.com/resource/national-grid-delivers-tangible-value-with-ivms/",{"kpi":46,"value":201,"unit":194,"currency":66,"qualifier":195,"period":202,"claimant":197,"quote":203,"sourceUrl":199},1000000,"not stated","$1M in avoided cost – with technology removing the need for time consuming manual processes like field reviews to determine if a circuit needs to be pruned.",true,[206,210,212],{"url":207,"title":208,"publisher":184,"date":209},"https://www.aidash.com/resource/real-results-from-national-grids-vegetation-program/","Real Results from National Grid's Vegetation Program","2025-10-14",{"url":199,"title":211,"publisher":184},"National Grid delivers tangible value with IVMS",{"url":213,"title":214,"publisher":184,"date":215},"https://www.aidash.com/resource/how-national-grid-cut-vegetation-related-impacts-by-up-to-43-per-cent/","How National Grid Cut Vegetation-Related Impacts by Up to 43%","2026-09-23",{"level":217,"checkedAt":169},"source-verified","C","national-grid-satellite-vegetation-management",null,{"title":222,"useCases":223,"organization":224,"vendors":226,"summary":228,"stage":187,"year":229,"channels":230,"languages":231,"metrics":232,"outcomeDisclosed":204,"sources":233,"verification":242,"grade":218,"id":243,"organizationSlug":220},"Entergy: satellite and AI vegetation management across its operating companies",[173],{"name":225,"anonymized":180,"country":181,"region":138,"industry":18},"Entergy",[227],{"name":184,"role":185},"Entergy, which serves more than 3 million customers through operating companies in Arkansas, Louisiana, Mississippi and Texas, uses the AiDASH Intelligent Vegetation Management System, which analyses satellite imagery with AI to show where vegetation threatens its power lines. According to AiDASH, Entergy moved from a standardized five year trim cycle to risk based maintenance, tailoring intervals so that some areas may need trimming every two to three years while others can go as long as thirteen years. AiDASH reports that Entergy beat its vegetation reliability (Veg SAIFI) targets by over 30% in 2020 and 2021 on flat vegetation budgets, and quotes an Entergy vegetation management analyst saying all SAIFI targets were exceeded for all operating companies in the first year after IVMS was implemented and again in the second. A later AiDASH case study quotes Entergy's Vice President of Power Delivery Services saying vegetation impacts to customers improved by more than 20% within the first year.",2020,[26],[190],[],[234,238],{"url":235,"title":236,"publisher":184,"date":237},"https://www.aidash.com/resource/entergys-journey-to-success-satellite-powered-vegetation-management/","Entergy's Journey to Success: Satellite-Powered Vegetation Management","2023-10-17",{"url":239,"title":240,"publisher":184,"date":241},"https://www.aidash.com/resource/entergy-case-study/","From Fixed Cycles to Risk-Based: How Entergy Improved Reliability by More Than 20%","2026-06-04",{"level":217,"checkedAt":169},"entergy-satellite-vegetation-management",0,[246],{"kpi":46,"label":247,"unit":194,"currency":66,"aggregate":180,"higherIsBetter":204,"n":248,"nUpTo":244,"median":193,"min":193,"max":193,"byClaimant":249,"vendorOnly":204,"points":250},"Cost savings",1,{"organization":244,"vendor":248,"regulator":244,"independent":244},[251],{"evidenceId":219,"organization":179,"value":193,"qualifier":195,"claimant":197,"grade":218,"pooled":204},{"low":253,"high":254},600000,4000000,[256,280,299,314],{"slug":257,"title":258,"shortTitle":259,"definition":260,"status":9,"industries":261,"functions":263,"patterns":265,"audience":268,"autonomy":269,"adoptionStage":30,"segment":270,"evidenceCount":271,"publicEvidenceCount":271,"organizations":272,"bestGrade":279,"headline":220,"lastVerified":169,"indexable":204},"predictive-network-maintenance","AI for predictive network maintenance in telecom","Predictive network maintenance","Machine learning that spots the early signs of network failure, such as degrading cells, faulty customer equipment, ageing hardware or planned digging near fibre, and triggers a preventive fix, a remote reset or a targeted intervention before customers lose service.",[262],"telecommunications",[20,21,264],"operations",[266,24,267],"anomaly-detection","agentic-workflow","back-office","supervised-agent","network",6,[273,274,275,276,277,278],"KDDI","Orange","Telefónica España","Telstra","Verizon","Vodafone","B",{"slug":281,"title":282,"shortTitle":283,"definition":284,"status":9,"industries":285,"functions":286,"patterns":288,"audience":293,"autonomy":269,"adoptionStage":294,"segment":295,"evidenceCount":296,"publicEvidenceCount":248,"organizations":297,"bestGrade":279,"headline":220,"lastVerified":169,"indexable":204},"network-outage-communication-agent","AI agent for network outage detection and customer communication","Outage communication","An AI agent that turns network alarms into a clear picture of which customers are affected by an outage and why, tells them proactively by message, app or phone with a cause and an estimated fix time, answers their questions during the incident, and updates them until service is restored.",[262],[287,20,21],"customer-service",[266,289,290,291,292],"classification-and-routing","content-generation","conversational-agent","voice-agent","customer-facing","emerging","front-office",2,[298],"Comcast",{"slug":300,"title":301,"shortTitle":302,"definition":303,"status":9,"industries":304,"functions":306,"patterns":307,"audience":28,"autonomy":308,"adoptionStage":30,"segment":309,"evidenceCount":296,"publicEvidenceCount":296,"organizations":310,"bestGrade":279,"headline":220,"lastVerified":313,"indexable":204},"freight-rail-rolling-stock-predictive-maintenance","AI predictive maintenance for freight rail rolling stock","Rail rolling stock predictive maintenance","Machine vision and machine learning that inspect freight railcar wheels, bearings and other running gear as trains pass wayside sensors and camera portals at track speed, learn what a healthy wheel or a healthy reading looks like, and flag the ones that need attention before a crack, an overheating bearing or a worn wheel causes a service failure or a derailment.",[305],"logistics-and-transportation",[264,21],[23,266,24],"assist","mechanical-and-safety",[311,312],"BNSF Railway","Norfolk Southern","2026-09-28",{"slug":315,"title":316,"shortTitle":317,"definition":318,"status":9,"industries":319,"functions":321,"patterns":322,"audience":28,"autonomy":308,"adoptionStage":30,"segment":323,"evidenceCount":324,"publicEvidenceCount":324,"organizations":325,"bestGrade":279,"headline":220,"lastVerified":169,"indexable":204},"industrial-asset-predictive-maintenance","AI predictive maintenance for industrial and energy assets","Industrial predictive maintenance","Machine learning that learns the normal behaviour of industrial and energy equipment from sensor and process data, flags early signs of degradation weeks or months before a failure, and turns them into prioritised maintenance work, so plants and utilities plan repairs instead of reacting to breakdowns.",[18,320],"manufacturing",[264,21],[266,24],"asset-management",3,[326,327,328],"Duke Energy","Georgia-Pacific","Shell",{"indexable":204,"reasons":330},[],[332,337,342,349,355,361,368,375,383,390,397,403,410,417,423,428,435,441,447,453,459,465,471,476,481,488,495,500,505,513,520,526,532,537],{"id":131,"label":333,"issuer":142,"region":143,"url":334,"description":335,"useCases":336,"indexable":204},"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":338,"issuer":142,"region":143,"url":339,"description":340,"useCases":341,"indexable":204},"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":134,"label":343,"issuer":344,"region":345,"url":346,"description":347,"useCases":348,"indexable":204},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":133,"label":350,"issuer":351,"region":138,"url":352,"description":353,"useCases":354,"indexable":204},"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":356,"label":357,"issuer":142,"region":143,"url":358,"description":359,"useCases":360,"indexable":204},"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":362,"label":363,"issuer":364,"region":143,"url":365,"description":366,"useCases":367,"indexable":204},"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":369,"label":370,"issuer":371,"region":143,"url":372,"description":373,"useCases":374,"indexable":204},"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":376,"label":377,"issuer":378,"region":379,"url":380,"description":381,"useCases":382,"indexable":204},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":384,"label":385,"issuer":386,"region":379,"url":387,"description":388,"useCases":389,"indexable":204},"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":391,"label":392,"issuer":393,"region":345,"url":394,"description":395,"useCases":396,"indexable":204},"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":398,"label":399,"issuer":400,"region":138,"url":401,"description":402,"useCases":396,"indexable":204},"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":404,"label":405,"issuer":406,"region":143,"url":407,"description":408,"useCases":409,"indexable":204},"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":411,"label":412,"issuer":413,"region":345,"url":414,"description":415,"useCases":416,"indexable":204},"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":418,"label":419,"issuer":142,"region":143,"url":420,"description":421,"useCases":422,"indexable":204},"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":424,"label":425,"issuer":142,"region":143,"url":426,"description":427,"useCases":422,"indexable":204},"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":429,"label":430,"issuer":431,"region":138,"url":432,"description":433,"useCases":434,"indexable":204},"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":436,"label":437,"issuer":142,"region":143,"url":438,"description":439,"useCases":440,"indexable":204},"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":442,"label":443,"issuer":444,"region":138,"url":445,"description":446,"useCases":440,"indexable":204},"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":448,"label":449,"issuer":450,"region":345,"url":451,"description":452,"useCases":440,"indexable":204},"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":454,"label":455,"issuer":142,"region":143,"url":456,"description":457,"useCases":458,"indexable":204},"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":460,"label":461,"issuer":462,"region":138,"url":463,"description":464,"useCases":458,"indexable":204},"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":466,"label":467,"issuer":378,"region":379,"url":468,"description":469,"useCases":470,"indexable":204},"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":472,"label":473,"issuer":142,"region":143,"url":474,"description":475,"useCases":470,"indexable":204},"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":477,"label":478,"issuer":142,"region":143,"url":479,"description":480,"useCases":470,"indexable":204},"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":482,"label":483,"issuer":484,"region":143,"url":485,"description":486,"useCases":487,"indexable":204},"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":489,"label":490,"issuer":491,"region":138,"url":492,"description":493,"useCases":494,"indexable":204},"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":496,"label":497,"issuer":142,"region":143,"url":498,"description":499,"useCases":494,"indexable":204},"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":501,"label":502,"issuer":142,"region":143,"url":503,"description":504,"useCases":271,"indexable":204},"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":506,"label":507,"issuer":508,"region":509,"url":510,"description":511,"useCases":512,"indexable":204},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":514,"label":515,"issuer":516,"region":143,"url":517,"description":518,"useCases":519,"indexable":204},"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.",4,{"id":521,"label":522,"issuer":523,"region":143,"url":524,"description":525,"useCases":519,"indexable":204},"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":379,"url":530,"description":531,"useCases":324,"indexable":204},"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":142,"region":143,"url":535,"description":536,"useCases":324,"indexable":204},"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":138,"url":541,"description":542,"useCases":324,"indexable":204},"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.",1790598306951]