[{"data":1,"prerenderedAt":630},["ShallowReactive",2],{"uc-physical-security-video-analytics":3,"uc-regulations":404},{"useCase":4,"evidence":179,"blitsAiDeployments":308,"benchmarks":309,"indicative":315,"related":318,"indexability":402,"includeUnpublished":185},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":21,"patterns":23,"channels":27,"audience":31,"autonomy":32,"adoptionStage":33,"problem":34,"problemStats":35,"howItWorks":36,"valueDrivers":37,"kpis":41,"indicativeValue":45,"macroEstimates":85,"feasibility":86,"implementation":98,"risk":136,"blitsAi":158,"faq":160,"related":173,"datePublished":174,"dateModified":174,"lastVerified":174,"changelog":175,"slug":178},"AI video analytics and screening for physical security","Physical security video analytics","AI video analytics for physical security","AI flags weapons or theft for a guard to check, never to decide alone. Verkada's Harry Rosen case study reports investigations in minutes instead of hours.","published","AI that watches camera feeds or walk through sensors at a site, flags a likely weapon, intrusion or theft in real time or on search, and leaves the verification and every response action to a human guard or investigator, rather than acting on its own.",[12,13,14,15,16],"AI video analytics","AI weapons detection","video security AI","AI powered video search","walk through weapons screening",[18,19,20],"retail-and-ecommerce","education","cross-industry",[22],"security-operations",[24,25,26],"computer-vision","anomaly-detection","classification-and-routing",[28,29,30],"internal-tools","microsoft-teams","api","employee-facing","assist","early-adopters","A guard cannot watch a hundred camera feeds at once, and a metal detector at a school or stadium\nentrance forces a line, a bag check and a pat down that slows everyone down. School board\nmembers weighing that tradeoff have raised concerns about how the process makes students feel,\nparticularly students of color. When something does go wrong, an investigator is left scrubbing\nhours of footage camera by camera to find the moment that matters.\n\nTwo kinds of AI have moved into this gap. Walk through scanners use sensor data, not just video,\nto flag a likely weapon shape without a full stop and search. Video platforms let an\ninvestigator search recorded footage by what something looked like, a logo, a bag, a colour of\nclothing, instead of by camera and timestamp. Both promise faster response and faster\ninvestigation, and both are the subject of real, public disputes about how well they actually\nwork, which is why every claim on this page carries its source.",[],"1. **Watch continuously, or index for search.** Cameras or walk through sensors feed the model\n   either live, to flag something as it happens, or continuously, to make the footage searchable\n   by content afterwards.\n2. **Score against known patterns.** A weapon shaped object, a restricted area entry, or a\n   described attribute such as clothing or a logo is matched or scored by the model.\n3. **Raise an alert or a hit, never a verdict.** A weapon scanner highlights where on a person's\n   body it thinks an object is; a search tool returns a list of matching clips.\n4. **A human verifies before anything happens.** A guard checks the highlighted area or reviews\n   the clip before anyone is stopped, searched or reported to the police.\n5. **Feed outcomes back.** Confirmed hits, false alarms and any miss found another way are\n   logged, so the team can see whether the alert is actually earning the attention it demands.",[38,39,40],"risk-reduction","cost-to-serve","speed",[42,43,44],"detection-rate-improvement","search-time-reduction","cost-savings",{"referenceOrg":46,"inputs":47,"formula":80,"currency":81,"period":82,"resultLabel":83,"caveat":84},"A retail chain with 20 stores, each running one to three loss prevention investigations a week",[48,53,60,67,73],{"key":49,"label":50,"low":51,"high":51,"unit":49,"note":52},"stores","Stores",20,"The reference chain.",{"key":54,"label":55,"low":56,"high":57,"unit":58,"note":59},"investigationsPerStoreWeek","Loss prevention investigations per store per week",1,3,"investigations per store per week","Editorial assumption, replace with your own case volume.",{"key":61,"label":62,"low":63,"high":56,"unit":64,"note":65,"sourceUrl":66},"hoursPerInvestigationBefore","Investigation time without content based search",0.5,"hours per investigation","Harry Rosen's loss prevention team describes a search without the tool as taking easily an hour with no guarantee of finding the footage; used here as an upper baseline.","https://www.verkada.com/customers/harry-rosen/",{"key":68,"label":69,"low":63,"high":70,"unit":71,"note":72,"sourceUrl":66},"timeSavedShare","Share of investigation time saved",0.8,"fraction of investigation time","Conservative against Verkada's Harry Rosen case study, which reports minutes instead of hours.",{"key":74,"label":75,"low":76,"high":77,"unit":78,"note":79},"costPerHour","Fully loaded cost of a loss prevention analyst",25,40,"USD per hour","Editorial assumption, replace with your own fully loaded cost.","stores * investigationsPerStoreWeek * 52 * hoursPerInvestigationBefore * timeSavedShare * costPerHour","USD","per year","Annual loss prevention investigation time cost avoided","Investigation time only. It leaves out the value of any loss actually prevented, the cost of the camera or sensor platform itself, and any extra staff time spent on false alarms.",[],{"complexity":87,"complexityNote":88,"dataPrerequisites":89,"integrations":93},"medium","The detection model, whether a walk through sensor or camera based search, is bought from a specialist vendor. The work for the security team is camera or sensor placement, wiring alerts into how guards already work, and writing a secondary screening procedure that holds up when the alert turns out to be a laptop rather than a weapon.",[90,91,92],"Camera or sensor coverage of the areas being screened, with clear sightlines","A written escalation and secondary screening procedure for every alert type","Historical incident and false alarm data to tune alert thresholds",[94,95,96,97],"Video management system or walk through sensor platform","Access control system for door and badge events","Incident and case management system for investigations","Alerting into the guard station or the security team's own communication channel",{"steps":99,"guardrails":115,"humanInTheLoop":120,"kpisToInstrument":121,"failureModes":126},[100,103,106,109,112],{"title":101,"detail":102},"Pilot on the highest risk entry points or aisles first","Start where the volume of people and the risk are both high enough to learn quickly, rather than rolling out to every site before the false alarm rate is known.",{"title":104,"detail":105},"Write the secondary screening procedure before go live","Decide who checks an alert, how, and with what training, before the first alert fires. A chromebook or a water bottle will set off a weapon scanner; know in advance how staff are meant to tell it apart from the real thing.",{"title":107,"detail":108},"Set a false alarm budget you can live with","Track how often staff develop workarounds, such as holding common items away from the body so they do not trigger an alarm, because the same workaround can also hide a real weapon.",{"title":110,"detail":111},"Ask for independently tested numbers, not only the vendor's own benchmark","A vendor funded, vendor scoped accuracy report is not the same as an independent test. Treat the vendor's own figures as a floor, and look for third party testing before a large rollout.",{"title":113,"detail":114},"Review alert quality on a schedule, not only after an incident","Sample confirmed hits and false alarms every month so the threshold gets tuned from ongoing data, not only relearned after something goes wrong.",[116,117,118,119],"A flagged weapon, intrusion or theft alert always goes to a trained human for secondary screening before any action is taken on a person","Every alert is logged with the frame or clip, the model's confidence and the human's verdict","Staff who conduct secondary screening are trained specifically for that role, not assigned informally on the day","Facial recognition or identity matching, if used at all, is a separate, explicitly approved capability, not a default of detection or search","A guard or investigator verifies every alert before anyone is stopped, searched or reported. The security team owns the false alarm rate and keeps it low enough that staff keep taking every alert seriously, and reviews it again whenever the vendor changes the underlying model.",[122,123,124,125],"Investigation time per case, before and after","Confirmed detections against false alarms, by alert type","Incidents found by the system against incidents found another way, such as a self report","Staff hours spent on secondary screening",[127,130,133],{"title":128,"detail":129},"A missed detection with a real weapon","Utica City Schools shelved its walk through system after it failed to detect a knife used in a stabbing, and separately missed a state trooper's service weapon twice when he went through the sensor with it on his hip. Treat any miss as an incident with a root cause review, and know which object types the technology is weak on before relying on it as the only layer.",{"title":131,"detail":132},"Alert fatigue from common false alarms","Reporting on Evolv's school deployments describes chromebooks, water bottles, umbrellas and binders setting off alerts. Staff workarounds, such as holding an item away from the body, can also hide a real weapon; measure and disclose the false alarm rate rather than letting an informal workaround become policy.",{"title":134,"detail":135},"Marketing claims outrun independent testing","Investigative reporting found that a vendor's own \"independent\" accuracy report was self funded, with the vendor choosing the scoring criteria and editing findings out across multiple drafts, including a finding that the system detected only about half of knives. Ask any vendor for a genuinely independent, unedited test result.",{"euAiAct":137,"regulations":140,"guidance":143,"controls":144,"incidents":149},{"tier":138,"basis":139},"context-dependent","Screening for a weapon shape, or searching footage by a described attribute such as clothing, is not itself the remote biometric identification or categorisation of a natural person, as long as it flags an object or an attribute rather than matching or categorising an individual. It becomes high risk under Annex III point 1(a) if the system performs remote biometric identification, whether real time or after the fact and in any setting, for example matching faces against a watchlist, and under Annex III point 1(b) if it performs biometric categorisation. It is high risk under Annex III point 6 if used by or on behalf of law enforcement for profiling a natural person. Real time remote biometric identification by or on behalf of law enforcement in a publicly accessible space is prohibited outright under Article 5(1)(h), subject only to its narrow, listed exceptions.",[141,142],"eu-ai-act","gdpr",[],[145,146,147,148],"Independent testing of detection accuracy before a large rollout, not only the vendor's own benchmark","A written, trained secondary screening procedure with defined roles","Logged alert and outcome history, reviewed on a schedule and after every miss","No facial recognition or watchlist matching without its own separate legal basis and impact assessment",[150,154],{"title":151,"url":152,"note":153},"FTC complaint and proposed settlement with Evolv Technologies over weapons detection accuracy claims","https://www.ftc.gov/news-events/news/press-releases/2024/11/ftc-takes-action-against-evolv-technologies-deceiving-users-about-its-ai-powered-security-screening","The FTC alleged Evolv had overstated its screening system's ability to detect all weapons and ignore harmless items. Under the proposed, stipulated settlement order, Evolv would be required to stop the unsupported claims and let certain K-12 school customers cancel their contracts.",{"title":155,"url":156,"note":157},"Incident 349: Evolv AI weapons detection system allegedly misrepresents accuracy, leading to school security gaps","https://incidentdatabase.ai/cite/349/","Independent record in the AI Incident Database of the reported gap between marketed and real world detection accuracy in schools.",{"howToBuild":159},"Blits.ai does not supply the weapon, intrusion or object detection model itself: that runs on\nthe camera, walk through sensor or video platform the security team already uses. Blits.ai\nbuilds the layer the guard and the investigator work in. An **agentic workflow**, triggered\nthrough an **API token** when the detection platform raises an alert, pulls the site's\nescalation procedure from a **knowledge base** and drafts an incident note with the alert's\ntime, location and confidence for a human to check. An **agentic task** with a scheduled\nrecheck, or a separate scheduled workflow that queries a **SQL knowledge base** of past\nalerts, reviews whether a given camera or sensor's confirmed versus false alarm ratio stays in\nthe range the team set.\n\n**Human in the loop confirmation** is central here, not optional: every drafted note stays open\nuntil a guard logs a verdict, and **guardrails** stop the workflow from proposing any action\nagainst a named person. Security staff receive the note in **Microsoft Teams** or through\n**internal tools**, with a **full audit trail** of every alert and verdict in the workflow's\nrun history. The platform is **model agnostic**, with **EU and UAE data residency** for the\nalert and case data.",[161,164,167,170],{"question":162,"answer":163},"Can AI reliably detect weapons on a camera or a walk through scanner?","Not on its own, and not without a trained human checking every alert. The FTC alleged that one major vendor's system missed weapons in schools while flagging harmless items, and a New York district shelved the same technology after it failed to detect a knife used in a stabbing. Treat the technology as one layer that raises alerts for a person to verify, not as a replacement for that person.",{"question":165,"answer":166},"What is AI powered video search, and how is it different from weapons detection?","Weapons detection screens people as they walk through, looking for an object's shape or signature. Video search, such as the tool Harry Rosen uses, works on recorded footage after the fact, letting an investigator search by a described attribute like clothing or a bag instead of scrubbing through camera by camera. They solve different problems and usually come from different vendors.",{"question":168,"answer":169},"Does this kind of system use facial recognition?","Not by default. The deployments on this page describe object detection and attribute based search, not matching a person's face to an identity. Facial recognition or watchlist matching is a separate, higher risk capability that needs its own legal basis and impact assessment.",{"question":171,"answer":172},"How much can AI video search reduce investigation time?","Verkada's own case study on Harry Rosen reports that investigations now take minutes instead of hours. Harry Rosen's director of loss prevention, in his own words, says a search without the tool \"would have taken easily an hour\" with no guarantee of finding the footage. That is a vendor case study naming one customer, so treat it as an indication rather than a guaranteed result for any other site.",[],"2026-09-29",[176],{"date":174,"note":177},"First published","physical-security-video-analytics",[180,209,226,247,266,292],{"title":181,"useCases":182,"organization":183,"vendors":189,"summary":193,"stage":194,"year":195,"channels":196,"languages":197,"metrics":198,"outcomeDisclosed":199,"sources":200,"verification":204,"grade":206,"id":207,"organizationSlug":208},"El Centro Regional Medical Center: camera coverage cuts workplace violence incidents",[178],{"name":184,"anonymized":185,"country":186,"region":187,"industry":188},"El Centro Regional Medical Center",false,"US","north-america","healthcare",[190],{"name":191,"role":192},"Verkada","platform","El Centro Regional Medical Center is the largest hospital in California's Imperial County. Under Emergency Preparedness Director Bill DuBois, it placed Verkada cameras in previously uncovered high risk areas as part of its workplace violence prevention, emergency preparedness and EMTALA compliance program, and uses the recorded footage as a training tool to teach staff safety protocols. DuBois reports the coverage has contributed to a sharp drop in workplace violence incidents at the hospital.","production",2024,[],[],[],true,[201],{"url":202,"title":203,"publisher":191},"https://www.verkada.com/customers/el-centro-regional-medical-center/","How the Largest Hospital in Imperial County Supports Workplace Violence Prevention, Emergency Preparedness, and EMTALA Compliance with Verkada",{"level":205,"checkedAt":174},"source-verified","C","el-centro-regional-medical-center-workplace-violence",null,{"title":210,"useCases":211,"organization":212,"vendors":215,"summary":217,"stage":194,"year":195,"channels":218,"languages":219,"metrics":220,"outcomeDisclosed":199,"sources":221,"verification":224,"grade":206,"id":225,"organizationSlug":208},"Harry Rosen: AI powered video search for loss prevention investigations",[178],{"name":213,"anonymized":185,"country":214,"region":187,"industry":18},"Harry Rosen","CA",[216],{"name":191,"role":192},"Harry Rosen is a Canadian luxury menswear retailer with 19 stores. Its loss prevention team replaced a fragmented set of camera, access control and alarm systems from different vendors with Verkada's cloud platform, then adopted Verkada's AI powered search to look up footage by what a suspect was wearing or carrying, such as a logo on a hat or a specific bag, instead of scrubbing through recordings camera by camera and hour by hour.",[],[],[],[222],{"url":66,"title":223,"publisher":191},"How Harry Rosen Advances Loss Prevention and Risk Management with Verkada",{"level":205,"checkedAt":174},"harry-rosen-ai-powered-search",{"title":227,"useCases":228,"organization":229,"vendors":231,"summary":234,"stage":194,"year":235,"channels":236,"languages":237,"metrics":238,"outcomeDisclosed":199,"sources":239,"verification":245,"grade":206,"id":246,"organizationSlug":208},"Charlotte-Mecklenburg Schools: Evolv weapons detection after two years",[178],{"name":230,"anonymized":185,"country":186,"region":187,"industry":19},"Charlotte-Mecklenburg Schools",[232],{"name":233,"role":192},"Evolv Technologies","Charlotte-Mecklenburg Schools, a North Carolina district, installed Evolv walk through weapons scanners. District officials told Louisville Public Media that the number of guns found on campus fell from 30 in the 2021 to 2022 school year to three in the 2022 to 2023 school year. The same reporting quotes a district spokesperson saying only one of those three guns was actually caught by the Evolv scanner; the other two were found before anyone tried to bring them into a building. The drop in guns found is therefore the district's own account of the deployment as a whole, not a confirmed detection rate for the scanner by itself.",2023,[],[],[],[240],{"url":241,"title":242,"publisher":243,"date":244},"https://www.lpm.org/news/2023-05-04/jcps-is-poised-to-spend-17-million-on-ai-weapons-detection-is-it-worth-it","JCPS is poised to spend $17 million on AI weapons detection. Is it worth it?","Louisville Public Media","2023-05-04",{"level":205,"checkedAt":174},"charlotte-mecklenburg-schools-weapons-detection",{"title":248,"useCases":249,"organization":250,"vendors":252,"summary":255,"stage":194,"year":235,"channels":256,"languages":257,"metrics":258,"outcomeDisclosed":185,"sources":259,"verification":264,"grade":206,"id":265,"organizationSlug":208},"Chelsea School District: AI visual gun detection added to existing cameras",[178],{"name":251,"anonymized":185,"country":186,"region":187,"industry":19},"Chelsea School District",[253],{"name":254,"role":192},"Omnilert","Chelsea School District is a Michigan public school district serving more than 2,400 students across multiple campuses. After upgrading to nearly 200 cameras district wide for a better field of view, Superintendent Michael Kapolka added Omnilert Gun Detect in early 2023, which scans the camera feeds for a visually detected weapon and sends an alert with images, a video clip and the camera's name and location to Omnilert's monitoring center for human verification before any response. Kapolka says the more the district can equip the people who are responding with accurate information, the more lives it can save in a crisis.",[],[],[],[260],{"url":261,"title":262,"publisher":254,"date":263},"https://www.omnilert.com/case-studies/chelsea-school-district-case-study","Chelsea School District Keeps Students & Staff Safe with AI Visual Gun Detection","2025-08-01",{"level":205,"checkedAt":174},"chelsea-school-district-gun-detection",{"title":267,"useCases":268,"organization":269,"vendors":273,"summary":275,"stage":194,"year":235,"channels":276,"languages":277,"metrics":278,"outcomeDisclosed":199,"sources":287,"verification":290,"grade":206,"id":291,"organizationSlug":208},"Kogan: AI powered video search cuts loss prevention investigation time",[178],{"name":270,"anonymized":185,"country":271,"region":272,"industry":18},"Kogan","AU","asia-pacific",[274],{"name":191,"role":192},"Kogan is an Australian retailer protecting people and assets across 21 warehouses and a head office. Its security team deployed Verkada cameras, access control and alarms across its facilities, so staff can find the footage behind an incident through People Analytics and motion based search instead of scrubbing through recordings camera by camera. Head of IT and Security Joshua Olds also credits the platform's mobile app with letting the team resolve incidents while away from a desk.",[],[],[279],{"kpi":43,"value":280,"unit":281,"qualifier":282,"baseline":283,"claimant":284,"quote":285,"sourceUrl":286},90,"percent","at-least","Time to find the relevant footage before Verkada's AI powered search","vendor","The ability to find key events through actionable insights – such as People Analytics and motion-based search – has reduced investigation time by over 90%.","https://www.verkada.com/customers/kogan/",[288],{"url":286,"title":289,"publisher":191},"How an Australian Retailer Reduced Investigation Time by Over 90% with Verkada",{"level":205,"checkedAt":174},"kogan-verkada-ai-search",{"title":293,"useCases":294,"organization":295,"vendors":297,"summary":299,"stage":300,"year":235,"channels":301,"languages":302,"metrics":303,"outcomeDisclosed":199,"sources":304,"verification":306,"grade":206,"id":307,"organizationSlug":208},"Utica City Schools: Evolv weapons detection shelved after a missed knife",[178],{"name":296,"anonymized":185,"country":186,"region":187,"industry":19},"Utica City School District",[298],{"name":233,"role":192},"Utica City Schools in upstate New York spent 3.7 million USD on an Evolv weapons detection system. The district's acting superintendent told Louisville Public Media that the district shelved the system after it failed to detect a knife that was used in a stabbing at a district school, and that the system also missed a state trooper's service weapon twice when the trooper went through the sensor with the weapon on his hip. Utica City Schools then spent 250,000 USD on metal detectors and bag X-ray machines, while continuing to pay off the Evolv contract.","paused",[],[],[],[305],{"url":241,"title":242,"publisher":243,"date":244},{"level":205,"checkedAt":174},"utica-city-schools-evolv-shelved",0,[310],{"kpi":43,"label":311,"unit":281,"aggregate":199,"higherIsBetter":199,"n":56,"nUpTo":308,"median":280,"min":280,"max":280,"byClaimant":312,"vendorOnly":199,"points":313},"Search time reduction",{"organization":308,"vendor":56,"regulator":308,"independent":308},[314],{"evidenceId":291,"organization":270,"value":280,"qualifier":282,"claimant":284,"grade":206,"pooled":199},{"low":316,"high":317},6500,99840,[319,336,362,386],{"slug":320,"title":321,"shortTitle":322,"definition":323,"status":9,"industries":324,"functions":325,"patterns":327,"audience":328,"autonomy":329,"adoptionStage":330,"evidenceCount":57,"publicEvidenceCount":57,"organizations":331,"bestGrade":335,"headline":208,"lastVerified":174,"indexable":199},"exam-and-assessment-integrity","AI assisted proctoring and integrity monitoring for remote exams","Exam and assessment integrity","AI that supports the integrity of a remote, high stakes exam by verifying a test taker's identity, analysing behaviour such as typing patterns, facial matching and session activity for signs of impersonation or unauthorized help, and flagging sessions for a trained human reviewer to decide, rather than letting a model issue an automated finding of misconduct on its own.",[19],[326,22],"operations",[25,24,26],"back-office","supervised-agent","mainstream",[332,333,334],"Duolingo","Educational Testing Service (ETS)","Pearson VUE","B",{"slug":337,"title":338,"shortTitle":339,"definition":340,"status":9,"industries":341,"functions":343,"patterns":346,"audience":31,"autonomy":32,"adoptionStage":33,"evidenceCount":348,"publicEvidenceCount":348,"organizations":349,"bestGrade":206,"headline":355,"lastVerified":361,"indexable":199},"data-quality-monitoring-agent","AI agent for data quality monitoring and observability","Data quality monitoring agent","An AI agent that watches data pipelines and tables continuously, uses machine learning to learn the normal pattern of freshness, volume, schema and distribution for each one, flags anomalies before they reach a dashboard or a downstream model, and traces the lineage back to the change that caused them so an engineer can fix the source, not just the symptom.",[20,342,18],"technology",[344,345],"it-and-engineering","analytics-and-reporting",[25,26,347],"summarization",5,[350,351,352,353,354],"Backcountry","Choozle","Contentsquare","Resident","SeatGeek",{"kpi":356,"label":357,"unit":281,"n":358,"nUpTo":308,"kind":359,"value":280,"qualifier":360,"claimant":284,"organization":353,"vendorReported":199},"error-reduction","Error reduction",2,"reported","exact","2026-09-28",{"slug":363,"title":364,"shortTitle":365,"definition":366,"status":9,"industries":367,"functions":370,"patterns":372,"audience":328,"autonomy":329,"adoptionStage":33,"segment":328,"evidenceCount":375,"publicEvidenceCount":375,"organizations":376,"bestGrade":206,"headline":381,"lastVerified":361,"indexable":199},"cash-application-and-remittance-matching","AI for cash application and remittance matching","Cash application and remittance matching","AI that reads remittance advices in many formats, matches incoming customer payments to open receivable invoices, proposes deduction and short pay reason codes from prior resolutions, and routes only the genuine exceptions to a cash application analyst, so the accounts receivable sub ledger clears itself for the clean majority of payments.",[20,368,18,188,369],"manufacturing","logistics-and-transportation",[371],"finance-and-accounting",[373,374,25,26],"document-processing","agentic-workflow",4,[377,378,379,380],"Keurig Dr Pepper","L'Oréal","ResMed","Sysco",{"kpi":382,"label":383,"unit":281,"n":375,"nUpTo":308,"kind":384,"value":385,"qualifier":360,"claimant":284,"organization":208,"vendorReported":199},"automation-rate","Automation rate","median",96,{"slug":387,"title":388,"shortTitle":389,"definition":390,"status":9,"industries":391,"functions":392,"patterns":394,"audience":328,"autonomy":329,"adoptionStage":33,"evidenceCount":57,"publicEvidenceCount":57,"organizations":395,"bestGrade":335,"headline":399,"lastVerified":401,"indexable":199},"customs-classification-and-declaration","AI for customs classification and declaration preparation","Customs classification and declarations","AI that reads what is being shipped (the commercial invoice, the product data and sometimes a photo), proposes the tariff classification code with its reasoning and a confidence score, drafts the customs declaration with value, origin and parties, and sends only uncertain or high risk entries to a licensed customs specialist before filing.",[369,18,20],[326,393],"regulatory-compliance",[26,373,374,24],[396,397,398],"DHL Express","United Parcel Service","ZLS Zoll und Logistikservice GmbH",{"kpi":382,"label":383,"unit":281,"n":56,"nUpTo":308,"kind":359,"value":280,"qualifier":360,"claimant":400,"organization":397,"vendorReported":185},"organization","2026-09-27",{"indexable":199,"reasons":403},[],[405,412,417,425,432,439,445,452,459,465,472,479,485,491,498,505,511,518,524,530,536,543,548,555,560,565,570,576,583,589,596,602,608,614,619,624],{"id":141,"label":406,"issuer":407,"region":408,"url":409,"description":410,"useCases":411,"indexable":199},"EU AI Act","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",230,{"id":142,"label":413,"issuer":407,"region":408,"url":414,"description":415,"useCases":416,"indexable":199},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",207,{"id":418,"label":419,"issuer":420,"region":421,"url":422,"description":423,"useCases":424,"indexable":199},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":426,"label":427,"issuer":428,"region":187,"url":429,"description":430,"useCases":431,"indexable":199},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",92,{"id":433,"label":434,"issuer":435,"region":408,"url":436,"description":437,"useCases":438,"indexable":199},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",71,{"id":440,"label":441,"issuer":407,"region":408,"url":442,"description":443,"useCases":444,"indexable":199},"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":446,"label":447,"issuer":448,"region":408,"url":449,"description":450,"useCases":451,"indexable":199},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",50,{"id":453,"label":454,"issuer":455,"region":272,"url":456,"description":457,"useCases":458,"indexable":199},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":460,"label":461,"issuer":462,"region":272,"url":463,"description":464,"useCases":76,"indexable":199},"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.",{"id":466,"label":467,"issuer":468,"region":187,"url":469,"description":470,"useCases":471,"indexable":199},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",22,{"id":473,"label":474,"issuer":475,"region":421,"url":476,"description":477,"useCases":478,"indexable":199},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",21,{"id":480,"label":481,"issuer":407,"region":408,"url":482,"description":483,"useCases":484,"indexable":199},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",17,{"id":486,"label":487,"issuer":488,"region":408,"url":489,"description":490,"useCases":484,"indexable":199},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",{"id":492,"label":493,"issuer":494,"region":187,"url":495,"description":496,"useCases":497,"indexable":199},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",16,{"id":499,"label":500,"issuer":501,"region":421,"url":502,"description":503,"useCases":504,"indexable":199},"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":506,"label":507,"issuer":407,"region":408,"url":508,"description":509,"useCases":510,"indexable":199},"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":512,"label":513,"issuer":514,"region":187,"url":515,"description":516,"useCases":517,"indexable":199},"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":519,"label":520,"issuer":521,"region":187,"url":522,"description":523,"useCases":517,"indexable":199},"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":525,"label":526,"issuer":407,"region":408,"url":527,"description":528,"useCases":529,"indexable":199},"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":531,"label":532,"issuer":533,"region":421,"url":534,"description":535,"useCases":529,"indexable":199},"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":537,"label":538,"issuer":539,"region":187,"url":540,"description":541,"useCases":542,"indexable":199},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",11,{"id":544,"label":545,"issuer":407,"region":408,"url":546,"description":547,"useCases":542,"indexable":199},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":549,"label":550,"issuer":551,"region":408,"url":552,"description":553,"useCases":554,"indexable":199},"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.",10,{"id":556,"label":557,"issuer":455,"region":272,"url":558,"description":559,"useCases":554,"indexable":199},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":561,"label":562,"issuer":407,"region":408,"url":563,"description":564,"useCases":554,"indexable":199},"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":566,"label":567,"issuer":407,"region":408,"url":568,"description":569,"useCases":554,"indexable":199},"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":571,"label":572,"issuer":407,"region":408,"url":573,"description":574,"useCases":575,"indexable":199},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",9,{"id":577,"label":578,"issuer":579,"region":187,"url":580,"description":581,"useCases":582,"indexable":199},"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.",7,{"id":584,"label":585,"issuer":407,"region":408,"url":586,"description":587,"useCases":588,"indexable":199},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",6,{"id":590,"label":591,"issuer":592,"region":593,"url":594,"description":595,"useCases":348,"indexable":199},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":597,"label":598,"issuer":599,"region":408,"url":600,"description":601,"useCases":375,"indexable":199},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":603,"label":604,"issuer":605,"region":408,"url":606,"description":607,"useCases":375,"indexable":199},"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":609,"label":610,"issuer":611,"region":272,"url":612,"description":613,"useCases":57,"indexable":199},"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":615,"label":616,"issuer":407,"region":408,"url":617,"description":618,"useCases":57,"indexable":199},"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":620,"label":621,"issuer":407,"region":408,"url":622,"description":623,"useCases":57,"indexable":199},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":625,"label":626,"issuer":627,"region":187,"url":628,"description":629,"useCases":57,"indexable":199},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790699629230]