By Stuart Kerr, Technology Correspondent, LiveAIWire
AI border surveillance now scans faces, analyses gait patterns, assesses behavioural indicators, and cross-references biometric data against international watchlists, all in the seconds it takes a traveller to approach a border officer. Airports and land border crossings around the world are quietly deploying these systems at scale, with profound consequences for privacy, civil liberties, and the treatment of migrants and asylum seekers who have little power to resist its application.
The drivers are familiar: governments seeking to process growing volumes of legitimate travel more efficiently while identifying security threats, irregular migrants, and fraudulent documentation. AI promises to do both simultaneously, offering faster throughput for low-risk travellers and more rigorous scrutiny for those triggering risk signals. Whether it delivers on either promise reliably is a more complicated question, and the communities bearing the costs of the technology’s failures are rarely those benefiting from its efficiencies.
Biometric AI Border Surveillance at Scale
The most widespread AI border technology is automated biometric matching. Over 70 countries now use AI-assisted facial recognition at airports, according to data compiled by the International Air Transport Association. The United States has deployed facial recognition at most major airports through the Department of Homeland Security’s Biometric Entry-Exit program. The EU’s Entry/Exit System, which became operational in phases from 2024, captures fingerprints, facial images, and biographical data from all non-EU visitors at external borders.
These systems vary significantly in accuracy, and accuracy gaps follow predictable demographic lines. Academic research and government audits have consistently found higher false match and false rejection rates for people with darker skin tones, older faces, and non-binary gender expressions, a pattern LiveAIWire has documented in more detail in our coverage of facial recognition and law enforcement. At a border, a false positive can mean detention, missed flights, or referral for enhanced screening based on algorithmically generated suspicion, and the administrative burden of correcting these errors falls entirely on the person wrongly flagged.
Several European national data protection authorities have raised concerns about the compatibility of AI border surveillance systems with GDPR requirements, particularly around the proportionality of mass biometric data collection. The European Data Protection Board has issued guidance on biometric processing at borders that member states are implementing with varying degrees of rigour.
Behavioural Detection and Prediction Systems
Beyond biometrics, some border agencies have deployed AI systems designed to detect deception or irregular intent through behavioural analysis. The EU’s iBorderCtrl project, which trialled an AI-powered lie detection system at land borders, was suspended following concerns about its scientific validity and discriminatory potential. This same reliance on aerial and behavioural monitoring runs through LiveAIWire’s coverage of AI drone surveillance, where border agencies increasingly deploy autonomous systems that raise identical accountability questions in a different physical form.
Impact on Asylum Seekers and Vulnerable Populations
The populations most affected by AI border surveillance are frequently those with the least access to legal recourse. Asylum seekers fleeing persecution, stateless persons without recognised documentation, and undocumented migrants are subject to AI-assisted processing in conditions of significant vulnerability. UNHCR has published guidance on the use of AI in refugee and migration contexts that emphasises the need for human oversight of all consequential decisions and the right to challenge algorithmic determinations. The gap between UNHCR guidance and the operational practices of member states deploying AI border systems is substantial and largely unmonitored.
The Accountability Deficit
The accountability deficit in AI border surveillance is compounded by the lack of independent audit. Most national border AI systems are developed and operated under contracts that include confidentiality provisions preventing external researchers or civil society organisations from assessing their accuracy or fairness. The United Nations Special Rapporteur on the Right to Privacy has called for mandatory independent auditing of AI systems used in border control and immigration enforcement, a recommendation that has been acknowledged but not implemented by the states operating the most extensive AI border systems.
If you travel internationally, your biometric data is almost certainly being processed by AI border surveillance systems, whether or not you are aware of it. In most jurisdictions, your ability to opt out of biometric processing is limited, and for non-citizens it may not exist at all. The governance frameworks governing AI at borders lag significantly behind deployment. The EU AI Act classifies remote biometric identification as high-risk and imposes transparency and accuracy requirements, but implementation is phased and enforcement mechanisms are still being developed.
Data Retention and the Case for International Standards
The data retention practices of AI border surveillance raise additional concerns beyond the accuracy issues. Biometric data collected at borders is typically stored for extended periods, in some cases indefinitely, and shared across law enforcement and intelligence agencies under bilateral and multilateral agreements that operate with limited transparency. The aggregation of travel data, biometric data, and risk scores creates detailed profiles of individuals’ movements and associations that go substantially beyond what is needed for border security purposes.
The case for stronger, more consistent international standards for AI border surveillance, grounded in human rights law and subject to independent audit, is compelling and largely unaddressed by current international governance frameworks, a gap that mirrors what LiveAIWire has traced in our coverage of AI diplomacy and how nations use code as a form of soft power. The speed at which these systems are being deployed significantly exceeds the speed at which international human rights and data protection frameworks are being updated to govern them.
About the Author
Stuart Kerr is Technology Correspondent at LiveAIWire, covering artificial intelligence, cybersecurity, and the social impact of emerging technology. He publishes daily at LiveAIWire.com.