[{"data":1,"prerenderedAt":586},["ShallowReactive",2],{"uc-autonomous-plant-process-control":3,"uc-regulations":366},{"useCase":4,"evidence":178,"blitsAiDeployments":253,"benchmarks":254,"indicative":263,"related":266,"indexability":364,"includeUnpublished":184},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":18,"functions":21,"patterns":23,"channels":26,"audience":28,"autonomy":29,"adoptionStage":30,"problem":31,"problemStats":32,"howItWorks":33,"valueDrivers":34,"kpis":38,"indicativeValue":41,"macroEstimates":69,"feasibility":70,"implementation":83,"risk":122,"blitsAi":156,"faq":158,"related":171,"datePublished":173,"dateModified":173,"lastVerified":173,"changelog":174,"slug":177},"AI for autonomous control of industrial plant processes","Autonomous plant process control","Autonomous AI control of industrial plants","AI that sets valves in a running plant. Aramco reports 10% to 15% less amine and steam at Fadhili; Yokogawa reports 40% less steam at ENEOS Materials.","published","Autonomous plant process control is reinforcement learning AI that sets valves and setpoints in a running industrial plant, within the limits of its existing control and safety systems. It takes over loops that operators adjust by hand because PID control and advanced process control cannot hold them, so a process unit can use less steam, chemicals and power and need fewer manual interventions.",[12,13,14,15,16,17],"autonomous control AI","reinforcement learning process control","AI closed loop process optimization","industrial autonomy","AI advanced process control","autonomous plant operations",[19,20],"energy-and-utilities","manufacturing",[22],"operations",[24,25],"agentic-workflow","prediction-and-scoring",[27],"api","back-office","autonomous","emerging","Process plants run on control loops that hold levels, temperatures and flows near a setpoint.\nMost loops are handled well by the plant's distributed control system and by PID control and\nadvanced process control. A few are not. At the ENEOS Materials Yokkaichi Plant, the liquid level\nin a butadiene distillation column and the supply of waste heat and steam had to be held tightly to\nkeep the product pure, and changes in outside temperature made it hard for the control system and\nadvanced process control to adjust two key valves. Operators set those valves by hand and\nstepped in often when conditions shifted. Using large amounts of steam wastes energy and is\ncostly.\n\nGas treating has the same pattern. Aramco's Fadhili Gas Plant removes acid gases from sour gas in\nan acid gas removal unit that uses amine, steam and power, and Aramco reports that its AI agents\ncut all three and reduced the manual interventions operators had to make there. In both cases the\ntarget is the way an existing unit is operated, hour by hour, rather than building a new unit.",[],"1. **Pick a loop that conventional control holds badly.** Typical candidates are valves that\n   operators adjust by hand and units where quality and energy use pull in opposite directions.\n2. **Train on a simulator.** A model of the plant is built first and the AI agents learn to\n   control it there, so no learning happens on the live process. Yokogawa describes this step at\n   both ENEOS Materials and Aramco.\n3. **Test against the plant's own knowledge and data.** The control model is evaluated and refined\n   with data from the unit. At ENEOS Materials, operators first entered the values the AI proposed\n   into the control system by hand.\n4. **Connect it to the control system.** The agents are integrated with the plant's production\n   control system so the existing safety functions stay in place. At Fadhili this was Yokogawa's\n   CENTUM VP.\n5. **Widen in phases.** Aramco introduced the agents in three phases, optimizing one section at a\n   time until the core of the acid gas removal unit ran under autonomous control.\n6. **Keep the way back.** At ENEOS Materials the plant has safety instrumentation, and staff say\n   they can simply stop the AI and go back to normal control system operation.",[35,36,37],"cost-to-serve","employee-productivity","risk-reduction",[39,40],"energy-savings","cost-reduction",{"referenceOrg":42,"inputs":43,"formula":64,"currency":65,"period":66,"resultLabel":67,"caveat":68},"A gas processing or chemical site with one to three process units that use steam, chemicals and power",[44,50,57],{"key":45,"label":46,"low":47,"high":48,"unit":45,"note":49},"units","Process units put under autonomous control",1,3,"Editorial assumption, replace with the number of units that have a loop operators still adjust by hand.",{"key":51,"label":52,"low":53,"high":54,"unit":55,"note":56},"unitEnergyCost","Annual steam, chemical and power cost of one unit",2000000,6000000,"USD per unit per year","Editorial assumption, replace with the unit's own utility and consumables cost from your plant accounts.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"savingShare","Reduction in that cost",0.05,0.1,"fraction","Aramco reports around 5% less power and 10% to 15% less amine and steam at the Fadhili Gas Plant. The range runs from the power figure to the bottom of the amine and steam range, because a unit's whole utility bill falls by less than its steam use alone. It stays well under the 40% steam reduction Yokogawa reports for ENEOS Materials, which has no stated baseline.","units * unitEnergyCost * savingShare","USD","per year","Annual steam, chemical and power cost avoided","Counts only the reduction in steam, chemical and power use. It leaves out the cost of building and validating the plant simulator, control system and instrument work, software licenses, the effort to maintain the model, and the value of steadier quality and fewer manual interventions.",[],{"complexity":71,"complexityNote":72,"dataPrerequisites":73,"integrations":78},"high","The build needs a plant simulator that reproduces the unit, a connection to the distributed control system with the existing safety functions kept in the loop, and agreement from operations, process safety and maintenance. Aramco's rollout at Fadhili ran in three phases over section after section of the unit before the core process was under autonomous control.",[74,75,76,77],"Historian data on the loop's process variables, valve positions and disturbances such as ambient temperature","Engineering data to build and validate a plant simulator of the unit","Control loop configuration, operating limits and product quality specifications","A record of the manual valve moves operators make today and why",[79,80,81,82],"The plant's distributed control system, for example Yokogawa's CENTUM VP at Fadhili","Plant simulator used to train and test the agents before live use","Safety instrumented systems and alarm management, which stay independent of the AI","Process historian for monitoring and later retraining",{"steps":84,"guardrails":100,"humanInTheLoop":105,"kpisToInstrument":106,"failureModes":112},[85,88,91,94,97],{"title":86,"detail":87},"Choose one loop that operators still run by hand","ENEOS Materials started with two valves on one distillation column that its control system and advanced process control had not managed to automate. A single loop with a measurable energy cost is easier to judge than a whole plant.",{"title":89,"detail":90},"Train and test on a simulator before the live unit","Build a plant model and let the agents learn on it, then check the control model against plant data and site knowledge. Reliability and validity checks belong here, before any live connection.",{"title":92,"detail":93},"Start with operators in the loop","At ENEOS Materials the AI proposed valve positions and operators entered them into the control system. Running that way for a period lets operators compare the AI with their own judgement and gives the first evidence for safe autonomous operation.",{"title":95,"detail":96},"Keep the plant's safety functions in force","Integrate the agents with the production control system so existing alarms, interlocks and safety instrumented functions act on whatever the AI does, as Aramco did at Fadhili.",{"title":98,"detail":99},"Widen by section, then by site","Move from one section to the core process in phases, run a detailed evaluation, and only then assess other units. Aramco says other facilities are being assessed after Fadhili.",[101,102,103,104],"Operating limits for every controlled variable set in the control system, outside the AI's authority to change","Existing safety instrumented functions and alarms independent of the AI and unchanged by it","A tested route back to normal control system operation that an operator can trigger at once","Model changes released through the plant's management of change process","Operators supervise the unit and can stop the AI and return to normal control. In the ENEOS Materials trial phase they entered the AI's proposed values themselves. As a matter of practice, process safety and operations engineers should own the operating limits, approve every new model version and review the AI's valve moves after upsets; no source on this page describes that split.",[107,108,109,110,111],"Steam, chemical and power use per tonne of product or per unit of throughput, against a matched baseline","Time the loop runs under AI control, and the number of times an operator takes it back","Variation of the controlled level, temperature or flow around its target","Off specification product and the fuel and labor lost making it","Manual valve interventions per shift",[113,116,119],{"title":114,"detail":115},"The simulator does not match the plant","Agents trained on a model that misses a disturbance, such as weather driven changes in ambient temperature, act badly on the live unit. Refine the control model with plant data before going live, as ENEOS Materials did.",{"title":117,"detail":118},"Operators do not trust moves they would not make","ENEOS Materials staff report that operators called to ask whether it was OK to stop the AI during the 35 day test, and that the AI sometimes makes valve moves people would not think of. Show operators what the AI is doing and keep stopping it easy.",{"title":120,"detail":121},"A result that rests on one unit","Aramco says detailed evaluations are still ongoing, and Yokogawa's release calls the Fadhili figures initial results. Treat one unit's savings as a range, and measure the next unit before counting on the same.",{"euAiAct":123,"regulations":126,"guidance":131,"controls":150,"incidents":155},{"tier":124,"basis":125},"context-dependent","Annex III point 2 lists AI systems used as safety components in the management and operation of critical digital infrastructure, road traffic, or in the supply of water, gas, heating or electricity as high risk. An agent that only optimizes a process unit inside operating limits and safety systems that act independently is not obviously a safety component, while one whose failure would endanger the supply of gas, heating or electricity could be. Which side a given plant falls on depends on its design and use. The deployments recorded here are in Saudi Arabia and Japan, outside the EU market.",[127,128,129,130],"eu-ai-act","nist-ai-rmf","iso-42001","nis2",[132,138,144],{"title":133,"issuer":134,"region":135,"url":136,"note":137},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https:\u002F\u002Fartificialintelligenceact.eu\u002Fannex\u002F3\u002F","Point 2 covers AI used as a safety component in the supply of water, gas, heating or electricity and in critical digital infrastructure.",{"title":139,"issuer":140,"region":141,"url":142,"note":143},"AI Risk Management Framework","NIST","north-america","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary framework for mapping, measuring and managing AI risk, including reliability and safety.",{"title":145,"issuer":146,"region":147,"url":148,"note":149},"ISA\u002FIEC 62443 series of standards","ISA","global","https:\u002F\u002Fwww.isa.org\u002Fstandards-and-publications\u002Fisa-standards\u002Fisa-iec-62443-series-of-standards","Security standards for industrial automation and control systems, relevant when AI agents are connected to a plant's control network.",[151,152,153,154],"Plant simulator validation record for every model version before live use","Operating limits and safety functions documented as independent of the AI","Logging of every setpoint or valve change the AI makes, with the plant state at the time","Network separation and access control between the AI and the plant control network",[],{"howToBuild":157},"Blits.ai does not run the control loop. A reinforcement learning controller sits with the plant's\ncontrol system vendor, and that is where operating limits and safety functions belong. What\nBlits.ai builds is the layer around it for operations teams. An **agentic workflow** reads the\ncontroller's status, recommendations and alarms through **custom functions** that call the\nplant historian or a **SQL knowledge base** over its exports, and drafts a shift summary or a\nchange request. **Human in the loop approval** keeps a person in charge of anything that would\nchange a limit or reach back to the plant.\n\nFor operators and process engineers, an **AI agent** over a **knowledge base** of operating\nprocedures, limit tables and past upset reports explains why the controller moved a valve and\nwhere the procedure says to intervene. **Guardrails** keep answers inside approved documents,\n**human handover** passes open questions to the responsible engineer, **test suites** replay\nreal questions on every change, and **analytics** and **monitors** show which questions go\nunanswered. The platform is model agnostic. Run Blits.ai in our cloud in the EU, Qatar,\nSingapore, the UAE, the US or Australia, in your private cloud, or fully on premises.",[159,162,165,168],{"question":160,"answer":161},"Who runs AI that controls a plant directly?","Aramco has autonomous control AI agents running the acid gas removal unit at its Fadhili Gas Plant in Saudi Arabia, built with Yokogawa, and ENEOS Materials has used Yokogawa's AI to control two valves on a distillation column at its Yokkaichi Plant in Japan since 2022. Both are reported by the vendor and the operator, not by an outside auditor.",{"question":163,"answer":164},"How much energy does it save?","Aramco reports a 10% to 15% reduction in amine and steam use and around 5% less power at Fadhili, and says detailed evaluations are still ongoing. Yokogawa reports a 40% reduction in steam use at ENEOS Materials. The two figures come from different processes and baselines, so measure your own unit against a matched baseline before using either.",{"question":166,"answer":167},"Is it safe to let AI set valves?","Both deployments built safeguards around the AI. Yokogawa trained the agents on a plant simulator first and connected them to the control system so its existing safety functions apply, and ENEOS Materials staff say the plant has safety instrumentation and that the AI can be stopped to return to normal control. Those are the operators' and vendor's descriptions of their own setups.",{"question":169,"answer":170},"How is this different from advanced process control?","The ENEOS Materials case describes two valves that PID control and advanced process control had not managed to automate, so operators adjusted them by hand. Yokogawa says its AI can handle conflicting targets such as high quality and energy savings, and in that case it learned its control model on a simulator, was refined with plant data, and then ran the two valves.",[172],"industrial-asset-predictive-maintenance","2026-10-14",[175],{"date":173,"note":176},"First published","autonomous-plant-process-control",[179,221],{"title":180,"useCases":181,"organization":182,"vendors":188,"summary":192,"stage":193,"year":194,"channels":195,"languages":197,"metrics":198,"outcomeDisclosed":207,"sources":208,"verification":216,"grade":218,"id":219,"organizationSlug":220},"Aramco: reinforcement learning AI agents controlling acid gas removal at the Fadhili Gas Plant",[177],{"name":183,"anonymized":184,"country":185,"region":186,"industry":19,"wikidata":187},"Aramco",false,"SA","middle-east","Q679322",[189],{"name":190,"role":191},"Yokogawa","platform","Between October 2024 and April 2025 Aramco and Yokogawa deployed multiple coordinated reinforcement learning agents (Yokogawa's FKDPP algorithm) that directly and autonomously control the acid gas removal unit at Aramco's Fadhili Gas Plant, the process that turns sour gas into sweet gas. The agents were trained on a simulator of the plant first, tested for reliability and validity, and then connected to Yokogawa's CENTUM VP control system so the plant's existing safety functions stay in place. Aramco reports a 10% to 15% reduction in amine and steam use and around 5% less power, and says the technology is now permanently adopted at Fadhili, with other Aramco facilities assessed for further deployments. Aramco says detailed evaluations are still ongoing.","production",2025,[196],"internal-tools",[],[199],{"kpi":39,"value":200,"unit":201,"qualifier":202,"period":203,"claimant":204,"quote":205,"sourceUrl":206},5,"percent","approximately","power usage at the acid gas removal unit, first results of the rollout from October 2024 to April 2025","organization","a 10% to 15% reduction in amine and steam usage, around a 5% reduction in power usage, improved process stability, and a decrease in required operator manual interventions","https:\u002F\u002Fwww.aramco.com\u002Fen\u002Fwhat-we-do\u002Fenergy-innovation\u002Fdigitalization\u002Fai-and-big-data",true,[209,211],{"url":206,"title":210,"publisher":183},"AI and Big Data",{"url":212,"title":213,"publisher":214,"date":215},"https:\u002F\u002Fwww.yokogawa.com\u002Fnews\u002Fpress-releases\u002F2025\u002F2025-10-29\u002F","Aramco and Yokogawa Achieve a Major Milestone with Commissioning of Multiple Autonomous Control AI Agents at Major Gas Facility","Yokogawa Electric Corporation","2025-10-29",{"level":217,"checkedAt":173},"source-verified","B","aramco-yokogawa-fadhili-autonomous-acid-gas-removal",null,{"title":222,"useCases":223,"organization":224,"vendors":229,"summary":231,"stage":193,"year":232,"channels":233,"languages":234,"metrics":235,"outcomeDisclosed":207,"sources":242,"verification":250,"grade":251,"id":252,"organizationSlug":220},"ENEOS Materials: reinforcement learning AI controlling a butadiene distillation column at Yokkaichi",[177],{"name":225,"anonymized":184,"country":226,"region":227,"industry":20,"wikidata":228},"ENEOS Materials Corporation","JP","asia-pacific","Q119658225",[230],{"name":190,"role":191},"Yokogawa and JSR (whose elastomers business is now ENEOS Materials) used Yokogawa's FKDPP reinforcement learning AI to control two valves on the butadiene distillation column at the Yokkaichi Plant in Japan, work that the plant's distributed control system and advanced process control could not do and that operators had done by hand. A field test ran for 35 consecutive days in January and February 2022; the AI first learned on a plant simulator, then was refined with plant data, and its recommended values were entered into the control system by operators before it controlled the plant. A revised model has run since April 2022 and the case study says it remains in routine use, with Yokogawa reporting 40% less steam use.",2023,[196],[],[236],{"kpi":39,"value":237,"unit":201,"qualifier":238,"claimant":239,"quote":240,"sourceUrl":241},40,"exact","vendor","Reduction of steam usage by 40%, saving energy and reducing costs","https:\u002F\u002Fwww.yokogawa.com\u002Flibrary\u002Fresources\u002Freferences\u002Fsuccessstory-eneos-materials\u002F",[243,245],{"url":241,"title":244,"publisher":214},"\"A world's first\" is now routine, ENEOS Materials Successfully Achieves Autonomous Control at a Plant Using AI",{"url":246,"title":247,"publisher":248,"date":249},"https:\u002F\u002Fwww.yokogawa.com\u002Fus\u002Fnews\u002Fpress-releases\u002F2022\u002F2022-03-22\u002F","In a World First, Yokogawa and JSR Use AI to Autonomously Control a Chemical Plant for 35 Consecutive Days","Yokogawa Electric Corporation and JSR Corporation","2022-03-22",{"level":217,"checkedAt":173},"C","eneos-materials-yokogawa-fkdpp-distillation-control",0,[255],{"kpi":39,"label":256,"unit":201,"aggregate":207,"higherIsBetter":207,"n":257,"nUpTo":253,"median":258,"min":200,"max":237,"byClaimant":259,"vendorOnly":184,"points":260},"Energy savings",2,22.5,{"organization":47,"vendor":47,"regulator":253,"independent":253},[261,262],{"evidenceId":252,"organization":225,"value":237,"qualifier":238,"claimant":239,"grade":251,"pooled":207},{"evidenceId":219,"organization":183,"value":200,"qualifier":202,"claimant":204,"grade":218,"pooled":207},{"low":264,"high":265},100000,1800000,[267,294,314,340],{"slug":172,"title":268,"shortTitle":269,"definition":270,"status":9,"industries":271,"functions":272,"patterns":274,"audience":276,"autonomy":277,"adoptionStage":278,"segment":279,"evidenceCount":280,"publicEvidenceCount":280,"organizations":281,"bestGrade":218,"headline":220,"lastVerified":293,"indexable":207},"AI predictive maintenance for industrial and energy assets","Industrial predictive maintenance","AI predictive maintenance is machine learning that learns the normal behavior 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 prioritized maintenance work, so plants and utilities plan repairs instead of reacting to breakdowns.",[19,20],[22,273],"field-service",[275,25],"anomaly-detection","employee-facing","assist","early-adopters","asset-management",12,[282,283,284,285,286,287,288,289,290,291,292],"ADNOC","Colgate-Palmolive","Duke Energy","DuPont","Equinor","Georgia-Pacific","Holcim","OCP Maintenance Solutions (OCP Group)","Shell","Tenaga Nasional Berhad (TNB)","Tokyo Electric Power Grid (TEPCO Power Grid)","2026-09-27",{"slug":295,"title":296,"shortTitle":297,"definition":298,"status":9,"industries":299,"functions":301,"patterns":302,"audience":276,"autonomy":303,"adoptionStage":278,"segment":304,"evidenceCount":48,"publicEvidenceCount":257,"organizations":305,"bestGrade":218,"headline":308,"lastVerified":313,"indexable":207},"production-scheduling-and-sequencing","AI copilots for production scheduling and sequencing","Production scheduling and sequencing","AI production scheduling and sequencing is AI that continuously resequences production orders and material flow between lines as conditions change, flagging conflicts and proposing changes for a human to approve. This includes routing parts and transfer carts and resequencing the order list at a line, triggered by events such as a machine going down, a rush order or a late part. A planner or operator no longer works out the sequence by hand.",[300,20],"automotive",[22],[24,25],"copilot","production-scheduling",[306,307],"Hyundai Motor Group","Mercedes-Benz Group AG",{"kpi":309,"label":310,"unit":201,"n":47,"nUpTo":253,"kind":311,"value":312,"qualifier":202,"claimant":204,"organization":306,"vendorReported":184},"productivity-gain","Productivity gain","reported",86,"2026-09-29",{"slug":315,"title":316,"shortTitle":317,"definition":318,"status":9,"industries":319,"functions":323,"patterns":325,"audience":28,"autonomy":328,"adoptionStage":278,"evidenceCount":329,"publicEvidenceCount":329,"organizations":330,"bestGrade":251,"headline":335,"lastVerified":339,"indexable":207},"autonomous-rfq-and-tail-spend-sourcing","AI agent for autonomous RFQ and tail spend sourcing","RFQ and tail spend sourcing","Autonomous RFQ sourcing is AI that sends requests for quotation to suppliers, compares the bids and recommends or makes the award for small purchases, within rules a buyer sets. It covers the tail of spend, the many low value or repeat requests that buyers rarely have time to compete, and hands complex or high value events to a person.",[320,19,321,322],"cross-industry","technology","logistics-and-transportation",[324,22],"procurement",[24,326,327],"document-processing","content-generation","supervised-agent",4,[331,332,333,334],"bp","G42","Massachusetts Bay Transportation Authority (MBTA)","Snowflake",{"kpi":336,"label":337,"unit":201,"n":257,"nUpTo":253,"kind":311,"value":338,"qualifier":238,"claimant":239,"organization":332,"vendorReported":207},"processing-time-reduction","Cycle time reduction",75,"2026-10-13",{"slug":341,"title":342,"shortTitle":343,"definition":344,"status":9,"industries":345,"functions":347,"patterns":350,"audience":28,"autonomy":328,"adoptionStage":278,"segment":352,"evidenceCount":353,"publicEvidenceCount":353,"organizations":354,"bestGrade":251,"headline":361,"lastVerified":173,"indexable":207},"b2b-sales-order-capture-agent","AI agent for B2B sales order capture from email, WhatsApp and PDF","B2B order capture","A B2B sales order capture agent is AI that reads customer orders arriving by email, WhatsApp, voicemail, PDF or fax and turns them into sales orders in the ERP. Staff review the exceptions it flags instead of retyping every line.",[320,322,346,20],"retail-and-ecommerce",[348,22,349],"sales","customer-service",[326,24,351],"classification-and-routing","wholesale and distribution",6,[355,356,357,358,359,360],"Colony Foods","The Howard Elliott Collection","Krystal Produce","North West Meat Company","Reach Food Group","Swaledale Butchers",{"kpi":336,"label":337,"unit":201,"n":329,"nUpTo":253,"kind":362,"value":363,"qualifier":238,"claimant":239,"organization":220,"vendorReported":207},"median",90,{"indexable":207,"reasons":365},[],[367,372,378,384,388,395,401,408,415,422,427,433,439,446,453,459,465,471,477,484,489,494,500,506,513,518,523,529,536,541,547,553,558,564,570,575,580],{"id":127,"label":368,"issuer":134,"region":135,"url":369,"description":370,"useCases":371,"indexable":207},"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.",295,{"id":373,"label":374,"issuer":134,"region":135,"url":375,"description":376,"useCases":377,"indexable":207},"gdpr","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.",265,{"id":129,"label":379,"issuer":380,"region":147,"url":381,"description":382,"useCases":383,"indexable":207},"ISO\u002FIEC 42001","ISO and IEC","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",148,{"id":128,"label":385,"issuer":140,"region":141,"url":142,"description":386,"useCases":387,"indexable":207},"NIST AI Risk Management Framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",115,{"id":389,"label":390,"issuer":391,"region":135,"url":392,"description":393,"useCases":394,"indexable":207},"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.",84,{"id":396,"label":397,"issuer":134,"region":135,"url":398,"description":399,"useCases":400,"indexable":207},"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.",73,{"id":402,"label":403,"issuer":404,"region":135,"url":405,"description":406,"useCases":407,"indexable":207},"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.",53,{"id":409,"label":410,"issuer":411,"region":227,"url":412,"description":413,"useCases":414,"indexable":207},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","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":416,"label":417,"issuer":418,"region":227,"url":419,"description":420,"useCases":421,"indexable":207},"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":130,"label":423,"issuer":134,"region":135,"url":424,"description":425,"useCases":426,"indexable":207},"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.",23,{"id":428,"label":429,"issuer":430,"region":147,"url":431,"description":432,"useCases":426,"indexable":207},"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":434,"label":435,"issuer":436,"region":141,"url":437,"description":438,"useCases":426,"indexable":207},"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.",{"id":440,"label":441,"issuer":442,"region":141,"url":443,"description":444,"useCases":445,"indexable":207},"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.",20,{"id":447,"label":448,"issuer":449,"region":135,"url":450,"description":451,"useCases":452,"indexable":207},"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":454,"label":455,"issuer":134,"region":135,"url":456,"description":457,"useCases":458,"indexable":207},"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.",15,{"id":460,"label":461,"issuer":462,"region":147,"url":463,"description":464,"useCases":458,"indexable":207},"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.",{"id":466,"label":467,"issuer":468,"region":141,"url":469,"description":470,"useCases":458,"indexable":207},"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":472,"label":473,"issuer":134,"region":135,"url":474,"description":475,"useCases":476,"indexable":207},"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":478,"label":479,"issuer":480,"region":141,"url":481,"description":482,"useCases":483,"indexable":207},"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":485,"label":486,"issuer":134,"region":135,"url":487,"description":488,"useCases":483,"indexable":207},"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":490,"label":491,"issuer":134,"region":135,"url":492,"description":493,"useCases":483,"indexable":207},"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":495,"label":496,"issuer":497,"region":147,"url":498,"description":499,"useCases":483,"indexable":207},"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":501,"label":502,"issuer":503,"region":141,"url":504,"description":505,"useCases":280,"indexable":207},"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.",{"id":507,"label":508,"issuer":509,"region":135,"url":510,"description":511,"useCases":512,"indexable":207},"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":514,"label":515,"issuer":134,"region":135,"url":516,"description":517,"useCases":512,"indexable":207},"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":519,"label":520,"issuer":134,"region":135,"url":521,"description":522,"useCases":512,"indexable":207},"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":524,"label":525,"issuer":411,"region":227,"url":526,"description":527,"useCases":528,"indexable":207},"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.",10,{"id":530,"label":531,"issuer":532,"region":141,"url":533,"description":534,"useCases":535,"indexable":207},"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":537,"label":538,"issuer":134,"region":135,"url":539,"description":540,"useCases":535,"indexable":207},"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.",{"id":542,"label":543,"issuer":544,"region":186,"url":545,"description":546,"useCases":353,"indexable":207},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":548,"label":549,"issuer":550,"region":135,"url":551,"description":552,"useCases":200,"indexable":207},"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":554,"label":555,"issuer":134,"region":135,"url":556,"description":557,"useCases":329,"indexable":207},"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":559,"label":560,"issuer":561,"region":135,"url":562,"description":563,"useCases":329,"indexable":207},"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":565,"label":566,"issuer":567,"region":227,"url":568,"description":569,"useCases":48,"indexable":207},"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":571,"label":572,"issuer":134,"region":135,"url":573,"description":574,"useCases":48,"indexable":207},"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":576,"label":577,"issuer":411,"region":227,"url":578,"description":579,"useCases":48,"indexable":207},"mas-shared-responsibility-framework","MAS Shared Responsibility Framework","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fguidelines\u002Fguidelines-on-shared-responsibility-framework","Singapore's framework that assigns banks and telcos duties and loss sharing for phishing scams.",{"id":581,"label":582,"issuer":583,"region":141,"url":584,"description":585,"useCases":48,"indexable":207},"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.",1792049861532]