AI in Physical Security: What Is Actually Changing

The conversation around artificial intelligence (AI) and physical security has shifted. It is no longer about whether organizations should pay attention—it is about whether they are moving fast enough to keep up with what is already on the market.

After more than two decades working across enterprise security programs, I have watched a lot of technology cycles come and go. This one feels different, and I think it is worth being honest about both the opportunity and the complexity.

AI is being deployed across three distinct layers in physical security today. It is embedded within major platform ecosystems. It is delivered as purpose-built analytics engines that sit on top of existing infrastructure. And it is increasingly running at the edge, processing intelligence directly on the device before data ever reaches a server. Each layer serves a different function, and most enterprise environments will interact with all three, whether they planned to or not.

The practical impact on operations is real. AI-driven triage is filtering alarm volume before a human operator ever sees an event. Behavioral analytics are surfacing patterns that manual review would miss entirely. Access control decisions are becoming contextually richer, moving beyond a static credential match toward a more dynamic picture of who is presenting, when and whether that pattern is consistent with known behavior.

What I am seeing in enterprise conversations is that technology is often ahead of organizational readiness to absorb it. Procurement criteria have evolved. Buyers now ask about AI model transparency, data residency, bias testing, and integration with IT security stacks alongside traditional specs. That is a meaningful shift in a relatively short period.

Workforce challenges are equally real. The operator role is changing. The integrator model is under pressure. And the regulatory environment around video analytics, behavioral scoring, and biometric data is moving unevenly across jurisdictions in ways that require security architects to think ahead of current requirements.

AI in physical security is genuinely capable of improving outcomes through faster threat detection, fewer false positives and better use of human attention. But the organizations seeing results are those that approach AI as a strategic capability requiring governance, training and clear use case definition. The ones struggling are those that bought AI as a feature and assumed the system would manage itself.

The window for deliberate, well governed AI adoption is open right now. Those who use it well will be in a meaningfully better position in three to five years. Those who wait for clarity will be trying to catch up.

To get a better understanding of AI’s role in your security deployment, consider how you would answer the following questions:

• If you had to defend your current AI deployment to a regulator tomorrow, could you?
• Who in your organization actually owns AI accountability, and is that role senior enough to matter?
• Are your operators being trained to work with AI, or are they being expected to absorb it?
• What does your integrator’s AI roadmap look like beyond their current product sheet?
• If AI disappeared from your security stack tomorrow, what would actually break?

Answer these honestly and you will know where to focus.

The views and opinions expressed in guest posts and/or profiles are those of the authors or sources and do not necessarily reflect the official policy or position of the Security Industry Association.

This article originally appeared in All Things AI, a newsletter presented by the SIA AI Advisory Board.