The eagerness to adopt AI in physical security is increasing as teams want to implement technology solutions for faster, smarter operations. At the same time, the conversations surrounding AI have changed. The focus now includes not only efficiency gains but also concerns about accountability and trust.
According to the Genetec 2026 State of Physical Security report, AI ranked alongside access control and video surveillance as a key priority. However, approximately 40% of the over 7,000 physical security leaders surveyed expressed concern about how AI systems use their data. The potential for malicious purposes followed close behind at 36%.
You may recognize this tension in your own organization. There is an expectation that AI can help operations and an understanding that AI entails risks. Balancing the two includes careful deployment and policies.
Higher stakes demand a higher standard
AI can quickly spot the difference and elevate the event to the security team
Most AI applications in physical security revolve around automation. Basic examples include triggering events, filtering and classifying alarms, and enabling faster searches across video and event data. These applications tackle the persistent security challenges of too much noise and too little context. AI takes on some of the lifting, cutting the false positives that overwhelm operators and adding context to the events that remain.
For example, someone loitering near an entrance may not be a notable event. While someone loitering and trying door handles is a priority. AI can quickly spot the difference and elevate the event to the security team. From there, the human operator can confirm the issue and next steps. AI narrows the operator's focus rather than replacing their judgment.
Higher standard of supervision
While these uses improve operator efficiency, they need to be implemented responsibly. Irresponsible AI usage in physical security is more than a simple inconvenience. The outputs affect safety and security. AI applications within physical security demand a higher standard of supervision.
This oversight is often referred to as “human in the loop,” but perhaps a better phrase is "expert in the loop." The person validating an output needs the knowledge to question it, contextualise it, and overrule it if needed.
How to deploy responsible AI
Deploying responsible AI starts by defining where AI can add value to your specific organization
Deploying responsible AI starts by defining where AI can add value to your specific organization. AI works best as a tool with a defined job. What is the problem that needs to be solved? What challenges are holding back the team? Start by identifying the problems, then work backward to a solution. Otherwise, you may be implementing technology that doesn’t fully meet your needs.
Next, define parameters around AI usage within your team. Document exactly what AI is permitted to do and not allowed to do within your operations. Information should include which use cases you've approved, what data the system can access, and how issues escalate. By defining these elements, your team has clear guidelines on when AI tools are part of your security procedures and for what purposes.
Cross-functional monthly meetings
Because AI can behave unpredictably, it’s good to prepare for when something does go wrong. Even a well-built model will eventually drift from its thresholds. This is where governance comes in. Governance is what keeps responsible AI holding up over time. It sets the review cycles that catch an issue before an incident does.
Governance related to AI entails data privacy, liability, and regulation. Input from legal, compliance, cybersecurity, and IT teams is important. Getting these groups together starts with something as simple as cross-functional monthly meetings to review use cases and emerging challenges. Consider also working with other manufacturers, integrators, and end users across your ecosystem. In doing so, you create shared standards that make issues diagnosable.
Responsible AI as a practice, not a project
The goal is to protect individual rights and build trust without hampering technological advancement
Responsible AI usage in security is not a box to check or a project to complete. It's an ongoing practice, especially as the compliance bar rises.
As more AI security risks surface, governments are drafting legislation to regulate how organizations can develop and implement AI-enabled technology. The goal is to protect individual rights and build trust without hampering technological advancement.
Identified risk category
For example, the 2024 AI Act in the European Union (EU) sets obligations for various AI applications based on their identified risk category. These requirements include creating adequate risk assessments and mitigation practices, using high-quality training datasets to reduce bias, and sharing detailed documentation on models with governing authorities as needed. In the US, multiple states have implemented legislation on AI use, and national legislation has been discussed.
Organizations can begin by implementing documented processes for guidelines and governance:
- Conduct risk assessments: Evaluate how automating a specific process may impact critical systems or safety protocols
- Identify non-critical applications: Start by implementing AI into processes that aren’t central to your most critical operations to curb major business disruptions
- Prioritise human-centred design: Ensure that AI applications always empower humans with the information they need to make the best decisions
- Design for explainability: Ensure AI decisions can be explained and traced, so the expert in the loop can question, verify, or overrule them when needed.
- Broaden data protection strategies: Apply cybersecurity measures and best practices to AI-enabled solutions, including regular audits and system updates
- Choose trusted vendors: Work with vendors who follow responsible AI principles, considering biases, data protection, and cybersecurity
Core pillar of physical security
The industry is slowly converging on shared standards for AI governance. This is good news for early adopters who wish to shape the industry rather than react to it. The potential applications of AI in security are exciting. But as this technology evolves, so do the risks.
Strong governance builds trust, a core pillar of physical security. Responsible AI protects that trust and earns more of it. Start by defining your outcomes, building guardrails, and creating accountability structures to support your organization’s responsible use of AI technology.
