[{"data":1,"prerenderedAt":585},["ShallowReactive",2],{"uc-wildfire-smoke-detection-cameras":3,"uc-regulations":365},{"useCase":4,"evidence":178,"blitsAiDeployments":273,"benchmarks":274,"indicative":275,"related":278,"indexability":363,"includeUnpublished":184},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":24,"audience":27,"autonomy":28,"adoptionStage":29,"segment":30,"problem":31,"problemStats":32,"howItWorks":33,"valueDrivers":34,"kpis":37,"indicativeValue":40,"macroEstimates":61,"feasibility":62,"implementation":75,"risk":120,"blitsAi":157,"faq":159,"related":169,"datePublished":173,"dateModified":173,"lastVerified":173,"changelog":174,"slug":177},"AI wildfire smoke detection cameras for utilities","Wildfire smoke detection cameras","AI wildfire detection cameras for utilities","AI cameras scan for smoke and alert fire dispatch. Arizona Public Service says its cameras notify it about 45 minutes before the first 911 call on average.","published","AI wildfire smoke detection is computer vision on tower cameras that spots early smoke, has a person confirm it, and alerts fire dispatch with a triangulated location. Utilities install the cameras near power lines and share the feeds with fire agencies, to find ignitions in remote areas before anyone calls 911.",[12,13,14,15],"AI wildfire detection cameras","AI smoke detection cameras","early wildfire detection for utilities","wildfire camera network",[17],"energy-and-utilities",[19,20],"network-operations","risk-management",[22,23],"computer-vision","anomaly-detection",[25,26],"internal-tools","api","employee-facing","copilot","early-adopters","grid","Some fires start where a 911 call would be a long time coming. John Truett, fire management officer at\nthe Arizona Department of Forestry and Fire Management, told the Associated Press that earlier\ndetection means \"we can launch aircraft and personnel to it and keep those fires as small as we can\".\nBrent Pascua, a battalion chief at the California Department of Forestry and Fire Protection (Cal\nFire), said in the same report that for the fires where no 911 call would come for a long time it is\nextremely helpful to have the AI always monitoring the camera, and that in many cases his service\nstarted a response before 911 was even called. Neal Driscoll, founder of ALERTCalifornia, said the AI\non the cameras is \"actually beating 911 calls\".\n\nUtilities have a direct interest. Xcel Energy says AI cameras help protect communities, natural\nresources and power infrastructure, and that they build on measures it already runs, such as power\nline inspections, vegetation management and aerial surveillance. Arizona Public Service says its AI\ncameras have detected smoke starts within minutes of initially burning. A person cannot watch every\nridge continuously, so software scans the images from each camera and a person decides what is real.",[],"1. **Install cameras on existing structures.** High definition cameras go on cell towers and\n   other tall existing structures in high risk areas. Xcel Energy says each station sweeps 360 degrees\n   every minute.\n2. **Scan for smoke.** A computer vision model checks every sweep for the shape and movement of smoke\n   and must tell it from look alikes. APS says its system studies the way smoke looks to avoid\n   mistaking other images like fog or dust for wildfire, and in AP's account of an Arizona detection\n   the analysts checked that the smoke was not a cloud or dust.\n3. **Verify with a person.** An analyst at the vendor's intelligence center reviews each possible\n   detection before an alert goes out. Xcel Energy describes this step, AP's account of an APS camera\n   detection says human analysts verified it was not a cloud or dust, and the founder of ALERTCalifornia\n   says human intervention keeps false positives low and trains the AI.\n4. **Locate and alert.** Two or more cameras triangulate the smoke, and the system sends the location\n   and live video to the utility's fire team, the local dispatch center and fire agencies.\n5. **Respond and learn.** Agencies launch ground or air response, the utility decides whether to change\n   operations near its lines, and the outcome is fed back to improve the model.",[35,36],"risk-reduction","speed",[38,39],"false-positive-reduction","detection-rate-improvement",{"referenceOrg":41,"inputs":42,"formula":56,"currency":57,"period":58,"resultLabel":59,"caveat":60},"A utility that deploys 40 wildfire detection cameras",[43,49],{"key":44,"label":45,"low":46,"high":47,"unit":44,"note":48},"cameras","Cameras in service",30,60,"Editorial assumption. Arizona Public Service had nearly 40 active cameras in May 2026 and planned 71 by the end of that summer, according to the Associated Press.",{"key":50,"label":51,"low":52,"high":52,"unit":53,"note":54,"sourceUrl":55},"pricePerCamera","Annual service price per camera",50000,"USD per camera per year","The Associated Press reports that Pano AI charges around USD 50,000 a year per camera, including fire risk analysis and a 24 hour intelligence center. Other vendors may price differently.","https:\u002F\u002Fwww.adn.com\u002Fnation-world\u002F2026\u002F05\u002F04\u002Fstates-across-the-wildfire-prone-western-us-are-using-ai-for-early-detection","cameras * pricePerCamera","USD","per year","Annual cost of the detection service","This is the cost side only. The value of earlier detection is avoided fire damage, liability, outage time and harm to people, and no public source in this library gives a per fire figure for a utility. Compare the cost with your own estimate of avoided losses, and add installation, power and connectivity costs.",[],{"complexity":63,"complexityNote":64,"dataPrerequisites":65,"integrations":70},"medium","Cameras and models are supplied as a managed service. The utility needs sites with power and connectivity, usually on towers it does not own, permits, agreements with fire dispatch centers on how alerts are received, and a decision on who acts on an alert. Coverage needs overlapping cameras: Xcel Energy notes that accuracy improves as the stations' reach overlaps.",[66,67,68,69],"A map of high risk areas, line routes and existing towers or structures","Terrain and viewshed analysis to place cameras with overlapping coverage","Contacts and alert procedures for every fire agency and dispatch center in the territory","A record of past ignitions and 911 call times to compare detection time",[71,72,73,74],"Camera vendor platform and its intelligence center","Fire dispatch and emergency management systems","Utility operations center and outage management system","Weather stations and fire risk models",{"steps":76,"guardrails":95,"humanInTheLoop":100,"kpisToInstrument":101,"failureModes":107},[77,80,83,86,89,92],{"title":78,"detail":79},"Choose sites by risk and coverage","Place cameras where ignitions near lines are most likely and where two cameras can see the same ground, so smoke can be triangulated. Xcel Energy's Bear Creek detection used two stations, one installed for CORE Electric and one for Xcel Energy.",{"title":81,"detail":82},"Agree the alert path with agencies","Decide who receives a verified alert, by what channel and how quickly, and give dispatch centers and fire agencies access to live feeds. APS gave dispatch centers access to live camera feeds and fire detection information.",{"title":84,"detail":85},"Run a season before counting results","Log every alert with its time, verification result and outcome, and compare the time to the first 911 call or other detection. Count false alarms and missed fires found by other means.",{"title":87,"detail":88},"Tune verification and thresholds","Review false alarm causes with the vendor, adjust where cameras point and the alert rules, and record the changes.",{"title":90,"detail":91},"Connect to operations","Decide how a confirmed fire near a line affects grid operations, and keep any shutoff decision with a person in the control room.",{"title":93,"detail":94},"Share and expand","Open feeds to more agencies once alerts are reliable, and add cameras to gaps found in coverage reviews. PGE shares its network with more than 215 users from 50 agencies.",[96,97,98,99],"A person verifies each detection before an alert goes to dispatch, at least until false alarm rates are known","The camera system never operates the grid; any decision to shut off power stays with the control room","Cameras are aimed and masked to avoid recording private property and people beyond what fire detection needs","Alert, verification and response times are logged for every event","Vendor analysts verify possible detections, dispatchers decide how to respond, and fire commanders direct suppression. Utility fire mitigation staff decide what the alert means for operations near lines and review each season's alerts, false alarms and response times with agencies.",[102,103,104,105,106],"Minutes from first smoke to verified alert, and to first dispatch","Minutes ahead of the first 911 call or other detection, where there is one","Verified detections per season and false alarms per verified detection","Fire size at first response","Share of high risk territory covered by two or more cameras",[108,111,114,117],{"title":109,"detail":110},"Alert fatigue","Too many false alarms make dispatchers ignore alerts. Keep a human verification step and track false alarms per detection.",{"title":112,"detail":113},"Coverage gaps","Terrain hides smoke from a single camera, and a fire starts in the gap. Plan overlapping coverage and review blind spots every season.",{"title":115,"detail":116},"Detection without response","An early alert nobody acts on changes nothing. Agree the alert path and response times with agencies before the cameras go live.",{"title":118,"detail":119},"Unverified time savings","A claimed lead time over 911 is easy to overstate, because it depends on which fires are counted. Record one comparison method and publish the sample size.",{"euAiAct":121,"regulations":124,"guidance":130,"controls":151,"incidents":156},{"tier":122,"basis":123},"context-dependent","Annex III point 2 makes AI systems high risk when they are intended to be used as safety components in the management and operation of the supply of electricity. Recital 55 describes safety components as \"systems used to directly protect the physical integrity of critical infrastructure or the health and safety of persons and property but which are not necessary in order for the system to function\", and gives \"systems for monitoring water pressure or fire alarm controlling systems\" as examples. Monitoring and alarm systems that do not switch anything can therefore fall inside the term, and having a person decide after an alert does not by itself remove a system from Annex III. The tier turns on whether the utility uses the cameras as a safety component in operating electricity supply, for example to protect lines, people or property, or only as a situational awareness tool for fire agencies. A provider that concludes the system is not high risk must rely on the Article 6(3) derogation, which requires the system not to pose a significant risk of harm to health, safety or fundamental rights, and must document that assessment before the system is put into service. A design in which a detection triggers protective shutoff or reclosing settings automatically is the clearest case for high risk treatment. Cameras that record people or private property also bring GDPR duties.",[125,126,127,128,129],"eu-ai-act","nis2","gdpr","nist-ai-rmf","iso-42001",[131,137,141,145],{"title":132,"issuer":133,"region":134,"url":135,"note":136},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https:\u002F\u002Fartificialintelligenceact.eu\u002Fannex\u002F3\u002F","Point 2 lists safety components in the management and operation of critical infrastructure, including electricity supply.",{"title":138,"issuer":133,"region":134,"url":139,"note":140},"Recital 55, safety components of critical infrastructure","https:\u002F\u002Fartificialintelligenceact.eu\u002Frecital\u002F55\u002F","Describes safety components as systems that directly protect the physical integrity of critical infrastructure or the health and safety of persons and property, with monitoring and fire alarm systems as examples. Whether the cameras are used that way decides the tier here.",{"title":142,"issuer":133,"region":134,"url":143,"note":144},"Article 6, classification rules for high risk AI systems","https:\u002F\u002Fartificialintelligenceact.eu\u002Farticle\u002F6\u002F","Paragraph 3 lets a provider treat an Annex III system as not high risk only after documenting its assessment before the system is put into service.",{"title":146,"issuer":147,"region":148,"url":149,"note":150},"AI Risk Management Framework","National Institute of Standards and Technology","north-america","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework for mapping, measuring and managing AI risk, used by utilities that run the cameras in the United States.",[152,153,154,155],"Written alert procedure signed with each fire agency, with named contacts","Logs of every alert, verification decision and response time","Seasonal review of false alarms, missed fires and coverage gaps","Privacy review of camera placement and recording",[],{"howToBuild":158},"The cameras, smoke detection model and verification team come from a specialist vendor. Blits.ai\nadds the conversations and records around the alerts. An **AI agent** with a **SQL knowledge base**\nover alert logs lets fire mitigation staff ask how many alerts a camera raised this season, how\nmany were false and how long each took to reach dispatch, and a **knowledge base** with the\nutility's alert procedures and agency contacts answers operations staff consistently.\n**Custom functions** read the vendor's alert API and write entries to the utility's incident log.\n\nFor customers and the public, an agent on **web chat, WhatsApp, SMS and voice** answers questions\nabout a nearby fire alert or a planned power shutoff from approved content, with **human handover**\nto the contact center for anything else. An **agentic workflow** can prepare customer notices for\napproval by a duty manager before they are sent. **Guardrails** and **PII masking** protect customer\ndata, **monitors** and **test suites** check the agent after every change, and the platform is model\nagnostic with EU and UAE data residency.",[160,163,166],{"question":161,"answer":162},"How much earlier do AI cameras detect wildfires?","Two utilities report figures. Arizona Public Service says its cameras notify it about 45 minutes faster on average than the first 911 call, as quoted by the Associated Press in May 2026. A Portland General Electric manager wrote in 2023 that in summer 2022 the cameras detected one fire south of Hagg Lake 140 minutes before traditional 911 and satellite detection. The first is an average with no stated sample, and the second is a single fire, so neither predicts your own lead time.",{"question":164,"answer":165},"Does a person check every detection?","The utilities on this page describe a human step. Xcel Energy says analysts at Pano verify a potential fire before the system triangulates its location and notifies fire agencies, and AP's account of an APS camera detection says human analysts verified it was not a cloud or dust before alerting the state forest service and the utility. Keep the step until your own false alarm rate is known.",{"question":167,"answer":168},"How much do AI wildfire cameras cost?","The Associated Press reports that Pano AI charges around USD 50,000 a year per camera, including fire risk analysis and a 24 hour intelligence center. Cost is described as one of the biggest obstacles to adoption, and the report notes that false alarms are costly in time and attention.",[170,171,172],"power-line-vegetation-management","storm-outage-prediction-and-restoration","industrial-asset-predictive-maintenance","2026-10-13",[175],{"date":173,"note":176},"First published","wildfire-smoke-detection-cameras",[179,218,244],{"title":180,"useCases":181,"organization":182,"vendors":187,"summary":191,"stage":192,"year":193,"channels":194,"languages":195,"metrics":197,"outcomeDisclosed":198,"sources":199,"verification":213,"grade":215,"id":216,"organizationSlug":217},"Arizona Public Service: Pano AI smoke detection cameras and fire dispatch centers",[177],{"name":183,"anonymized":184,"country":185,"region":148,"industry":17,"wikidata":186},"Arizona Public Service (APS)",false,"US","Q1242804",[188],{"name":189,"role":190},"Pano AI","platform","Arizona Public Service operates Pano AI cameras that watch 24 hours a day for early signs of wildfire in high risk areas and alert APS fire mitigation staff and fire dispatch centers when they detect smoke and heat traces, with dispatch centers given live feeds. APS reported in 2025 that a camera on a communication tower in the Prescott National Forest detected smoke from the Brady Fire when it was less than one acre, and that the fire did not affect its equipment or service. In May 2026 an Associated Press report said APS had nearly 40 active cameras and planned 71 by the end of summer, and quoted an APS meteorologist saying the cameras notify the utility about 45 minutes faster on average than the first 911 call.","production",2025,[25,26],[196],"en",[],true,[200,205,209],{"url":201,"title":202,"publisher":203,"date":204},"https:\u002F\u002Fwww.aps.com\u002Fen\u002FAbout\u002FOur-Company\u002FNewsroom\u002FArticles\u002FExtra_Eyes_APS_Firefighters_Team_Up_with_New_AI_Smoke-Detection_Cameras","Extra Eyes: APS, Firefighters Team Up with New AI Smoke-Detection Cameras","Arizona Public Service","2025-03-25",{"url":206,"title":207,"publisher":203,"date":208},"https:\u002F\u002Fwww.aps.com\u002Fen\u002FAbout\u002FOur-Company\u002FNewsroom\u002FArticles\u002FAPS_Lineworkers_and_Power_Plants_Ready_for_Strong_Summer_Run","APS Lineworkers and Power Plants Ready for Strong Summer Run","2025-06-26",{"url":55,"title":210,"publisher":211,"date":212},"States across the wildfire-prone Western US are using AI for early detection","Associated Press via Anchorage Daily News","2026-05-04",{"level":214,"checkedAt":173},"source-verified","B","arizona-public-service-pano-ai-wildfire-cameras",null,{"title":219,"useCases":220,"organization":221,"vendors":224,"summary":226,"stage":227,"year":193,"channels":228,"languages":229,"metrics":230,"outcomeDisclosed":198,"sources":231,"verification":242,"grade":215,"id":243,"organizationSlug":217},"Portland General Electric: Pano AI wildfire cameras shared with fire agencies",[177],{"name":222,"anonymized":184,"country":185,"region":148,"industry":17,"wikidata":223},"Portland General Electric (PGE)","Q7231939",[225],{"name":189,"role":190},"Portland General Electric began installing AI wildfire detection cameras in 2021 and by July 2025 ran almost 40 of them around Portland and surrounding areas. When the cameras detect smoke or fire activity, agency users get a notification with the location and a link to the live feed. More than 215 users from 50 agencies use the network, and PGE opened the feeds to the public in July 2025. PGE's wildfire planning and analytics manager wrote in 2023 that in summer 2022 the cameras detected a fire south of Hagg Lake 140 minutes before traditional 911 and satellite detection, so Oregon Department of Forestry crews could drive straight to it.","scaled",[25,26],[196],[],[232,237],{"url":233,"title":234,"publisher":235,"date":236},"https:\u002F\u002Fportlandgeneral.com\u002Fnews\u002F2025-07-public-can-now-access-wildfire-monitoring-camera-network","Public can now access wildfire monitoring camera network used by dozens of local first responder and safety agencies","Portland General Electric","2025-07-29",{"url":238,"title":239,"publisher":240,"date":241},"https:\u002F\u002Ftdworld.com\u002Fwildfire\u002Farticle\u002F21262313\u002Fportland-general-electric-deploys-ai-advanced-tech-to-reduce-wildfire-risk","Portland General Electric Deploys AI, Advanced Tech to Reduce Wildfire Risk","T&D World (written by PGE's wildfire planning and analytics manager)","2023-04-11",{"level":214,"checkedAt":173},"portland-general-electric-pano-ai-wildfire-cameras",{"title":245,"useCases":246,"organization":247,"vendors":250,"summary":252,"stage":192,"year":253,"channels":254,"languages":255,"metrics":256,"outcomeDisclosed":198,"sources":257,"verification":271,"grade":215,"id":272,"organizationSlug":217},"Xcel Energy: Pano AI wildfire detection cameras in Colorado, Texas and Minnesota",[177],{"name":248,"anonymized":184,"country":185,"region":148,"industry":17,"wikidata":249},"Xcel Energy","Q1486956",[251],{"name":189,"role":190},"Xcel Energy installs Pano AI camera stations on towers and other tall structures near its power lines. Each station sweeps 360 degrees every minute, AI looks for smoke, Pano analysts verify a possible fire, and the system triangulates the location and notifies local fire agencies and dispatch centers. In July 2024 Xcel and CORE Electric Cooperative described the Bear Creek Fire in Colorado: the Pano system and the US Forest Service detected the smoke from a lightning strike at the same time, a helicopter crew launched when the fire was 0.25 acres, and the fire was contained at three acres. At that date Xcel had 28 cameras working in Colorado. It has since announced programmes for more than 50 camera stations in the Texas Panhandle (December 2024) and 38 in Minnesota (October 2025). In May 2026 the Associated Press wrote that Xcel Energy in Colorado \"has installed 126\" cameras and aims to have cameras in seven of the eight states it serves by year's end.",2024,[25,26],[196],[],[258,262,266,270],{"url":259,"title":260,"publisher":248,"date":261},"https:\u002F\u002Fstories.xcelenergy.com\u002Fstories\u002FXcel-Energy-s-AI-powered-cameras-aid-in-Bear-Creek-Fire-containment","Xcel Energy’s AI-powered cameras aid in Bear Creek Fire containment","2024-07-17",{"url":263,"title":264,"publisher":248,"date":265},"https:\u002F\u002Fnewsroom.xcelenergy.com\u002Fnews\u002Fxcel-energy-using-ai-technology-to-detect-wildfires-in-texas-panhandle-MCJQJAEYRTBZEFBBTON334QSURGQ","Xcel Energy Using AI Technology to Detect Wildfires in Texas Panhandle","2024-12-03",{"url":267,"title":268,"publisher":248,"date":269},"https:\u002F\u002Fnewsroom.xcelenergy.com\u002Fnews\u002Fxcel-energy-brings-ai-driven-wildfire-detection-to-minnesota","Xcel Energy brings AI-driven wildfire detection to Minnesota","2025-10-09",{"url":55,"title":210,"publisher":211,"date":212},{"level":214,"checkedAt":173},"xcel-energy-pano-ai-wildfire-cameras",0,[],{"low":276,"high":277},1500000,3000000,[279,297,319,342],{"slug":170,"title":280,"shortTitle":281,"definition":282,"status":9,"industries":283,"functions":284,"patterns":286,"audience":27,"autonomy":28,"adoptionStage":29,"segment":30,"evidenceCount":288,"publicEvidenceCount":288,"organizations":289,"bestGrade":295,"headline":217,"lastVerified":296,"indexable":198},"AI vegetation management for power lines","Power line vegetation management","Power line vegetation management is AI that analyses imagery of the land along power lines to estimate where vegetation risks the lines, for utility crews. It uses satellite, aerial or lidar imagery to judge 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.",[17],[19,285],"field-service",[22,287],"prediction-and-scoring",5,[290,291,292,293,294],"Bear Valley Electric Service","Entergy","National Grid","PNM","Trinity Public Utilities District","C","2026-09-27",{"slug":171,"title":298,"shortTitle":299,"definition":300,"status":9,"industries":301,"functions":302,"patterns":303,"audience":27,"autonomy":28,"adoptionStage":29,"segment":30,"evidenceCount":304,"publicEvidenceCount":304,"organizations":305,"bestGrade":215,"headline":309,"lastVerified":318,"indexable":198},"AI storm outage prediction and restoration staging for power grids","Storm outage prediction and restoration","AI storm outage prediction is AI that predicts how many outages a coming storm will cause, where, and how severe restoration will be, for utility storm response teams. It combines weather forecasts, historical outage records and network data, so a utility can stage crews and mutual aid before the storm arrives instead of assessing damage after it hits.",[17],[19,285],[287],3,[306,307,308],"CenterPoint Energy","Hydro One","New Brunswick Power (NB Power)",{"kpi":310,"label":311,"unit":312,"n":313,"nUpTo":273,"kind":314,"value":315,"qualifier":316,"claimant":317,"organization":307,"vendorReported":198},"mttr-reduction","Time to repair reduction","percent",1,"reported",33,"approximately","vendor","2026-09-29",{"slug":172,"title":320,"shortTitle":321,"definition":322,"status":9,"industries":323,"functions":325,"patterns":327,"audience":27,"autonomy":328,"adoptionStage":29,"segment":329,"evidenceCount":330,"publicEvidenceCount":330,"organizations":331,"bestGrade":215,"headline":217,"lastVerified":296,"indexable":198},"AI predictive maintenance for industrial and energy assets","Industrial predictive maintenance","AI predictive maintenance is machine learning that learns the normal behaviour of industrial and energy equipment and flags early signs of degradation before a failure. It works from sensor and process data, flags issues weeks or months ahead, and turns them into prioritised maintenance work, so plants and utilities plan repairs instead of reacting to breakdowns.",[17,324],"manufacturing",[326,285],"operations",[23,287],"assist","asset-management",10,[332,333,334,335,336,337,338,339,340,341],"ADNOC","Colgate-Palmolive","Duke Energy","DuPont","Equinor","Georgia-Pacific","Holcim","Shell","Tenaga Nasional Berhad (TNB)","Tokyo Electric Power Grid (TEPCO Power Grid)",{"slug":343,"title":344,"shortTitle":345,"definition":346,"status":9,"industries":347,"functions":349,"patterns":351,"audience":353,"autonomy":328,"adoptionStage":354,"segment":355,"evidenceCount":356,"publicEvidenceCount":356,"organizations":357,"bestGrade":215,"headline":217,"lastVerified":362,"indexable":198},"catastrophe-exposure-assessment","AI for catastrophe and exposure assessment in insurance","Catastrophe exposure assessment","AI catastrophe exposure assessment is AI that turns satellite and geospatial data into a fast, portfolio wide view of which policies and properties are exposed to a catastrophe. It covers before and immediately after the event, so exposure managers and claims teams can quantify the loss, prioritize response and reach affected customers first, without waiting for ground surveys.",[348],"insurance",[350,20,326],"claims",[22,23,287,352],"agentic-workflow","back-office","emerging","catastrophe-and-exposure",4,[358,359,360,361],"Brit","Security First Insurance","Sompo Japan Insurance","Suncorp Group","2026-09-30",{"indexable":198,"reasons":364},[],[366,371,376,383,388,395,401,408,416,423,430,435,441,448,455,462,468,474,481,486,491,498,504,511,516,521,526,531,538,544,551,556,562,568,574,579],{"id":125,"label":367,"issuer":133,"region":134,"url":368,"description":369,"useCases":370,"indexable":198},"EU AI Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",275,{"id":127,"label":372,"issuer":133,"region":134,"url":373,"description":374,"useCases":375,"indexable":198},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",246,{"id":129,"label":377,"issuer":378,"region":379,"url":380,"description":381,"useCases":382,"indexable":198},"ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",143,{"id":128,"label":384,"issuer":385,"region":148,"url":149,"description":386,"useCases":387,"indexable":198},"NIST AI Risk Management Framework","NIST","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",110,{"id":389,"label":390,"issuer":391,"region":134,"url":392,"description":393,"useCases":394,"indexable":198},"uk-gdpr","UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",81,{"id":396,"label":397,"issuer":133,"region":134,"url":398,"description":399,"useCases":400,"indexable":198},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",72,{"id":402,"label":403,"issuer":404,"region":134,"url":405,"description":406,"useCases":407,"indexable":198},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",51,{"id":409,"label":410,"issuer":411,"region":412,"url":413,"description":414,"useCases":415,"indexable":198},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":417,"label":418,"issuer":419,"region":412,"url":420,"description":421,"useCases":422,"indexable":198},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":424,"label":425,"issuer":426,"region":148,"url":427,"description":428,"useCases":429,"indexable":198},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",23,{"id":126,"label":431,"issuer":133,"region":134,"url":432,"description":433,"useCases":434,"indexable":198},"NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",22,{"id":436,"label":437,"issuer":438,"region":379,"url":439,"description":440,"useCases":434,"indexable":198},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":442,"label":443,"issuer":444,"region":134,"url":445,"description":446,"useCases":447,"indexable":198},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":449,"label":450,"issuer":451,"region":148,"url":452,"description":453,"useCases":454,"indexable":198},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",17,{"id":456,"label":457,"issuer":458,"region":379,"url":459,"description":460,"useCases":461,"indexable":198},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":463,"label":464,"issuer":133,"region":134,"url":465,"description":466,"useCases":467,"indexable":198},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":469,"label":470,"issuer":471,"region":148,"url":472,"description":473,"useCases":467,"indexable":198},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-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":475,"label":476,"issuer":477,"region":148,"url":478,"description":479,"useCases":480,"indexable":198},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":482,"label":483,"issuer":133,"region":134,"url":484,"description":485,"useCases":480,"indexable":198},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":487,"label":488,"issuer":133,"region":134,"url":489,"description":490,"useCases":480,"indexable":198},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":492,"label":493,"issuer":494,"region":148,"url":495,"description":496,"useCases":497,"indexable":198},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",12,{"id":499,"label":500,"issuer":501,"region":379,"url":502,"description":503,"useCases":497,"indexable":198},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":505,"label":506,"issuer":507,"region":134,"url":508,"description":509,"useCases":510,"indexable":198},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":512,"label":513,"issuer":133,"region":134,"url":514,"description":515,"useCases":510,"indexable":198},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":517,"label":518,"issuer":133,"region":134,"url":519,"description":520,"useCases":510,"indexable":198},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":522,"label":523,"issuer":411,"region":412,"url":524,"description":525,"useCases":330,"indexable":198},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":527,"label":528,"issuer":133,"region":134,"url":529,"description":530,"useCases":330,"indexable":198},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":532,"label":533,"issuer":534,"region":148,"url":535,"description":536,"useCases":537,"indexable":198},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",8,{"id":539,"label":540,"issuer":133,"region":134,"url":541,"description":542,"useCases":543,"indexable":198},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",7,{"id":545,"label":546,"issuer":547,"region":548,"url":549,"description":550,"useCases":288,"indexable":198},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":552,"label":553,"issuer":133,"region":134,"url":554,"description":555,"useCases":356,"indexable":198},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":557,"label":558,"issuer":559,"region":134,"url":560,"description":561,"useCases":356,"indexable":198},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":563,"label":564,"issuer":565,"region":134,"url":566,"description":567,"useCases":356,"indexable":198},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":569,"label":570,"issuer":571,"region":412,"url":572,"description":573,"useCases":304,"indexable":198},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":575,"label":576,"issuer":133,"region":134,"url":577,"description":578,"useCases":304,"indexable":198},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":580,"label":581,"issuer":582,"region":148,"url":583,"description":584,"useCases":304,"indexable":198},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1791904362122]