Why AI Adoption in Security Screening Is Stalling

Over the past decade, screening technologies have achieved levels of performance that would have seemed unrealistic only a few years ago. Detection capabilities have improved, computing power has increased dramatically and new approaches to automated threat recognition continue to emerge.

At the same time, adoption has progressed at very different speeds across regions, sectors and use cases.

The reason may have less to do with technology than with the systems surrounding it. Security screening operates within an ecosystem of regulations, procedures, certifications, liability frameworks and operational habits that have evolved over decades. Technology can change quickly. Institutions tend to change much more slowly.

Understanding that distinction is essential to understanding the current state of AI adoption in security screening.

More Than Fifty Years of the Same Operational Model

Modern security screening still relies on an operational framework established more than half a century ago.

While detection technologies have evolved significantly, the underlying process remains remarkably familiar. Passengers stop at checkpoints, present belongings for inspection and move through systems overseen by trained operators. Human decision-making remains central to the process.

This is not simply a story about hardware. Entire ecosystems have been built around this model, including training programmes, certification requirements, service contracts, audit procedures and regulatory frameworks. Together, they reinforce a structure in which the human operator remains the primary decision-maker.

In recent years, certain segments have seen meaningful progress. AI-driven walkthrough metal detection has gained traction in parts of the US market, and higher-throughput AI baggage X-ray solutions continue to evolve. These developments represent real engineering achievement and tangible steps forward. They do not, however, indicate broad institutional acceptance of AI-driven replacement of the operator. They are improvements within the established model, not a transition beyond it. 

Two Very Different Uses of AI

Discussions about AI often become confusing because the term is used to describe fundamentally different approaches.

The first is assistive AI, where AI supports operators by analysing data, highlighting areas of concern and helping identify potential threats. The operator remains responsible for the final decision.

The second is autonomous AI, where AI performs the primary detection task itself while operators focus on exceptions or secondary review.

The distinction matters. Assistive AI can improve consistency and reduce fatigue, but the underlying operating model remains largely unchanged. Autonomous AI, by contrast, has the potential to reshape throughput, staffing models, operational costs and the overall screening experience.

It is also where adoption becomes considerably more challenging.

Why Expectations Change

AI systems are often evaluated differently from the processes they aim to replace.

Human error is recognised as part of operational reality. Training, procedures and oversight are designed to reduce mistakes, but few organisations expect perfection.

Automated systems, however, are frequently expected to perform at exceptionally high levels before they are considered acceptable.

As a result, new technologies can end up being measured against an idealised version of existing processes rather than against actual operational performance. Questions such as “What if the AI misses a threat?” naturally emerge, even when comparable questions may not be asked of established processes.

This does not necessarily reflect resistance to innovation. It reflects how organisations evaluate risk when introducing new technologies into critical environments.

A Useful Parallel: Autonomous Driving

A similar dynamic can be seen in autonomous vehicles.

Discussions around self-driving technology rarely focus solely on technical capability. They also focus on responsibility, liability, regulation and public acceptance.

Security screening faces many of the same questions. The technology may evolve rapidly, but adoption ultimately depends on the surrounding frameworks evolving as well.

What This Means for the Industry

Discussions around AI adoption in security screening often focus on technology: detection rates, false alarms, throughput and operational efficiency.

These factors matter. But they are only part of the picture.

As AI takes on a greater role in screening operations, questions of accountability, trust and institutional readiness become equally important. The challenge is no longer simply building systems that can perform. It is creating the frameworks that allow organisations to adopt those systems with confidence.

This helps explain why adoption can progress at different speeds across regions, sectors and applications, even when the underlying technology is available.

The future of security screening will be shaped not only by advances in AI, but also by how effectively the industry develops the standards, certification frameworks and operational models needed to support those advances.

Technology may be the catalyst for change. The pace of adoption, however, will depend on how quickly the surrounding ecosystem evolves alongside it.

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