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In August 2024, three sold-out Taylor Swift concerts at Vienna's Ernst Happel Stadium were canceled hours before the first show. Austrian authorities, acting on intelligence passed to them by the CIA, had arrested a 19-year-old who had been radicalized online and had assembled chemicals and detonators at his home. Roughly 200,000 people held tickets. No camera at the gate found that plot, and no scanner would have. It surfaced in intelligence work that happened weeks earlier, far from the venue perimeter.
That case explains why event security programs are pairing two different capabilities. Artificial intelligence handles the perimeter and the crowd, watching feeds and sensors at a scale no operator can match. Open source intelligence handles everything outside the fence line, where grievance, planning, and targeting form in public. The programs that hold up under scrutiny run both, with people making the decisions in between.
How to Use AI for Event Security
Four applications dominate current deployments.
Computer vision on video feeds.
Algorithms analyze live camera streams for predefined conditions: crowd density and flow, movement against the prevailing direction, abandoned objects, intrusion into restricted zones, people on the ground, smoke, and flames. France passed Law No. 2023-380 ahead of the Paris 2024 Olympics to permit exactly this on an experimental basis, with facial recognition and biometric matching explicitly excluded from the scope.
Screening and detection at entry.
Sensor-fusion scanners use machine learning to separate weapon signatures from laptops, keys, and water bottles, with the goal of moving crowds through entry points faster than traditional walk-through metal detectors.
Drone detection and mitigation.
NFL chief security officer Cathy Lanier told Congress that rogue drone flights into restricted airspace above stadiums during NFL games reached 2,845 in 2023, compared with about a dozen in the 2017 season. Detection systems classify radio frequency signatures to distinguish a hobbyist quadcopter from something worth interrupting an event over.
Language models applied to open source collection.
Classification, translation, summarization, and semantic search let a small analyst team work through volumes of public posts that would otherwise go unread.
What Are the Advantages of Using AI in Event Security
Attention is where the gain is largest. A stadium may run several hundred cameras, and human operators lose reliability watching static feeds within roughly 20 minutes. Software applies the same criteria to every feed for the full duration of an event, at 2 a.m. during load-in as readily as during the headline set.
Detection also happens faster. Crowd density crossing a dangerous threshold, a vehicle entering a pedestrian corridor, or smoke in a concourse gets flagged in seconds, ahead of a radio call from someone who happened to be standing nearby.
Documentation improves as a side effect. Detection rules produce a record of what was monitored and when, which matters for insurers, licensing authorities, and any post-incident inquiry.
On the intelligence side, the main benefit is triage. Thousands of posts mentioning an artist, a team, or a venue arrive in the weeks before doors open. Automated classification and summarization reduce that to a reviewable set, which separates an analyst team that reads everything relevant from one that samples.
Problems with Implementing AI
Vendor claims deserve scrutiny. In November 2024 the Federal Trade Commission took action against Evolv Technologies, alleging the company deceptively advertised that its AI-powered scanners would detect all weapons and ignore harmless personal items, and made unsupported claims about accuracy and cost savings against traditional metal detectors. The proposed settlement barred those representations and let certain school customers exit contracts. Evolv Express units are installed in stadiums and hospitals as well as schools.
Performance varies by use case. The French evaluation committee reviewing the Olympic video surveillance experiment produced mixed findings, with some use cases such as abandoned baggage detection performing poorly, and the heavy police presence during the Games making the test difficult to generalize from. The experiment was extended rather than declared a success.
Regulation is tightening. Under the EU AI Act, a set of practices including real-time remote biometric identification in publicly accessible spaces for law enforcement, biometric categorization by sensitive characteristics, and emotion recognition in workplaces and schools became prohibited on February 2, 2025, subject to narrow exceptions. Most other biometric identification systems fall into the high-risk tier, and following the Digital Omnibus that entered into force in July 2026, full obligations for those standalone systems apply from December 2, 2027. In the United States, state biometric privacy laws such as Illinois BIPA carry private rights of action, which has driven litigation against venues using facial recognition.
Sensitivity settings create a tradeoff that no model removes. Higher sensitivity catches more and alarms more, and an entry line that alarms constantly trains staff to wave people through.
Utilizing AI and OSINT Together for Events
The two capabilities cover different time horizons. Sensors and cameras see what arrives at the venue. Open source collection sees what forms before anyone travels.
For a major event, useful collection is anchored to specific terms: the venue name and address, the performer or team, the event name and dates, sponsor brands, and the names of executives or officials associated with the event. Analysts watch for direct threats, protest and counter-protest mobilization, ticket fraud and impersonation accounts, doxing of talent or staff, and geolocated content posted from the site during load-in. Continuous threat monitoring and alerting against that term set is what turns scattered posts into something an advance team can act on, and deeper searching across deep and dark web sources covers spaces where planning discussions move once they leave mainstream platforms.
AI does the heavy lifting inside that workflow. Semantic search finds posts that never use the obvious keyword. Classifiers separate a fan complaining about ticket prices from someone describing a plan. Summarization compresses a day of chatter into something a security manager can read before a production meeting. Analysts still validate, because a model's confidence score carries no legal weight when a decision affects someone's access to an event.
Findings then need somewhere to go. Mapping geolocated activity against the venue footprint in a situational awareness view puts online signals on the same picture as gates, medical posts, and camera positions. When a post warrants knowing who is behind it, investigations and identity resolution work connects the account to a real person, which is what allows a credibility assessment instead of a guess.
Moving From Reactive Security to Proactive Security
Proactive event security runs on a timeline rather than an alarm.
Four to six weeks out, teams build a baseline: who has publicly objected to the event, which groups have called for action, what happened at comparable events at the same venue, and which principals attending have existing threat histories. Liferaft's analysis of what past attacks reveal about World Cup security walks through how historical incident patterns shape that baseline.
In the final week, collection narrows to travel logistics, last-minute mobilization calls, and accommodation or transport disruption near the site. Anything credible routes to a named decision maker with an agreed threshold, and the file supporting it lives in case management so the same subject is recognizable at the next event.
On event day, AI detection carries the live load while analysts stay on collection, because online reaction to something happening inside a venue often outpaces the incident reporting chain.
Regulation is pushing programs in the same direction. The UK's Terrorism (Protection of Premises) Act 2025, known as Martyn's Law, received Royal Assent in April 2025 and is expected to come into force in spring 2027, with the Security Industry Authority as regulator. Premises expecting 200 to 799 people fall into a standard tier with training and procedure requirements, and premises and qualifying events expecting 800 or more fall into an enhanced tier that must assess vulnerability and document public protection measures.
How Does AI Change Event Security Moving Forward?
Counter-drone capability is expanding fastest. The SAFER SKIES Act, signed as part of the FY26 National Defense Authorization Act on December 18, 2025, gives state, local, tribal, and territorial law enforcement a path to detect, track, and mitigate drones that pose a credible threat to large public gatherings, an authority previously held by a handful of federal agencies. FEMA moved quickly behind it, awarding an initial $250 million to the 11 states hosting FIFA World Cup 2026 matches and to the National Capital Region.
Procurement is changing alongside it. After the FTC action, buyers are asking for test data, false alarm rates at operational sensitivity settings, and references from venues of similar size, rather than accepting capability claims at face value.
The division of labor is settling as well. Detection models handle volume. People handle judgment, credibility, proportionality, and the decision to deny entry, cancel, or call law enforcement. Regulators in both the EU and UK frameworks assume a documented human decision sits behind consequential action, which makes the analyst layer a compliance requirement in addition to an operational one.
Improving Event Security with Liferaft
Liferaft, a Securitas company, supports more than 200 organizations worldwide with continuous monitoring across social media, forums, alternative networks, and deep and dark web sources. Queries are built around venues, events, principals, and brands, so alerts arrive tied to something a security team actually owns.
Liferaft iQ, the platform's AI layer, condenses large volumes of posts into concise summaries, supports semantic and context-aware search, flags emerging discussions through live insights, and generates reports against a team's own data. Identity resolution connects online personas to real actors, geospatial mapping places activity against a venue footprint, and dossiers keep evidence and case history together for briefings, licensing authorities, or law enforcement referral. Teams protecting performers, executives, or officials attending events can read more in our guide to OSINT for executive protection, and anyone planning an event calendar can book a discovery call to see how the workflow fits an existing security operation.
FAQs
Is AI surveillance more effective than traditional security?
AI surveillance and traditional security measure different things, so effectiveness depends on the task. Video analytics outperform human operators at sustained monitoring of many feeds, detecting crowd density changes, and flagging predefined events in seconds. Trained staff outperform software at assessing intent, de-escalating, and making judgment calls under ambiguity. Evidence on AI systems is also uneven: the FTC found that one prominent AI screening vendor made unsupported accuracy claims, and the French evaluation of Olympic video analytics rated some use cases poorly. Treat AI as an attention multiplier for a staffed operation and verify vendor claims with test data from venues of comparable size.
What ethical considerations arise with the use of AI in event security?
Privacy sits at the center. Attendees rarely have a meaningful choice about being analyzed, which raises questions about notice, retention, and secondary use of footage. Bias in detection models can produce uneven false alarm rates across demographic groups, concentrating secondary screening on some attendees. Accuracy claims carry ethical weight, since overstated capability leads venues to thin staffing. Legal frameworks encode several of these concerns already: the EU AI Act prohibits real-time remote biometric identification in public spaces for law enforcement outside narrow exceptions, and Illinois BIPA requires consent for biometric collection. Documented human review before any consequential decision remains the practical safeguard.
What AI technologies are most utilized in securing events?
Computer vision leads, covering crowd analytics, abandoned object detection, perimeter intrusion, and fire and smoke detection. Sensor-fusion screening at entry points is second. Counter-drone detection is growing quickly following the SAFER SKIES Act and federal grant funding tied to the 2026 World Cup. On the intelligence side, natural language processing handles classification, translation, summarization, and semantic search across open sources. Facial recognition appears in some venues, though its use is restricted or contested in the EU, in parts of the US, and by several sports leagues.