AI agents are beginning to move from theory to practical use in securing physical security infrastructure. For systems integrators and consultants, the opportunity is significant.
AI-assisted tools can help identify exposed devices, maintain asset visibility, monitor vulnerabilities, detect anomalies, and support more consistent configuration reviews across video surveillance, access control, and other connected security systems.
But the same environments that make AI attractive also make unchecked automation risky. A mistaken action in a physical security deployment is not limited to an IT alert or a temporarily unavailable business application. It can affect video availability, door access, incident response, and operational continuity.
That distinction is shaping how experts view the near-term role of AI agents in physical security. The emerging consensus is that AI can be highly useful as an advisory and analytical layer, but full autonomous response remains difficult to justify in mission-critical environments where safety and security operations may be directly affected.
Evgeny Goncharov, Head of Kaspersky ICS CERT, said the risk increases when automated action becomes too aggressive.
“Aggressive automated responses increase the risk of false positives,” Goncharov said. “This could lead to unnecessary isolation of devices or interruption of services, affecting operational continuity and safety.”
For physical security professionals, that warning is especially relevant because many modern deployments now sit at the intersection of cyber and physical operations. Network cameras, access control panels, video management systems, intercoms, intrusion systems, and edge devices are increasingly IP-connected, software-driven, and integrated with broader enterprise networks.
This creates a wider attack surface. It also creates more operational dependencies. A cyber response that isolates a compromised endpoint may be appropriate in a conventional IT environment. In a physical security context, however, the same action could take a camera offline, prevent a door controller from communicating, or disrupt a security operations center’s visibility during an incident.
The problem with autonomous response
The core challenge is not whether AI can identify risks or recommend action. Experts suggest that it increasingly can. The bigger question is whether AI should be allowed to act independently when the consequences of a false positive may extend into the physical world.
Martin Zugec, Technical Solutions Director at Bitdefender, framed the issue around the inevitability of mistakes. “The starting point for this conversation should be an honest premise: AI agents will make mistakes. So will humans,” Zugec said. “We need to accept that failure is not a possibility to mitigate, it's a certainty to design for.”
That perspective is important for integrators because physical security systems are often expected to function continuously. Cameras must remain available. Doors must operate according to policy. Security teams must retain situational awareness. Any system designed to protect these environments must account not only for detection accuracy, but also for the operational impact of incorrect decisions.
“In physical security environments that framing matters more than most, because the blast radius of a mistake isn't just an IT inconvenience - it's a locked door, a blind camera, a gap in coverage during an active incident,” Zugec said.
This does not mean AI agents have no role in physical security. Rather, it suggests that their role must be carefully bounded. In many deployments, AI should support human decision-making rather than replace it. The practical architecture is one where AI identifies risk, explains the issue, prioritizes response, and recommends next steps, while a human operator, integrator, or security administrator makes the final decision.
Goncharov noted that this is already the model most organizations are adopting. “Most organizations adopt a model in which AI supports human decision-making rather than acting autonomously, ensuring that responses are context-aware and operationally safe,” he said.
For consultants and integrators, this has direct design implications. AI-enabled cybersecurity tools for physical security should be evaluated not only on detection capability, but also on workflow design. Important questions include how alerts are presented, whether recommendations are explainable, how confidence levels are shown, what approval steps exist before action is taken, and how easily operators can reverse or override a decision.
Near-term use cases for integrators
The most realistic opportunities for AI agents are currently on the advisory side of security operations. These are areas where AI can reduce manual effort, improve consistency, and help teams identify risk faster without giving the system unchecked control over the environment.
Goncharov identified several practical use cases. “The most realistic use cases for AI agents are exposure identification, continuous asset discovery, vulnerability monitoring, penetration testing, anomaly detection, suspicious artifacts analysis and processing traces of an intrusion,” he said.
For physical security integrators, continuous asset discovery may be among the most immediately valuable. Many end users operate mixed environments built over several years, sometimes with devices from multiple vendors, inconsistent documentation, and incomplete records of firmware versions, network locations, and ownership. This can make it difficult to know what is actually deployed, which devices are exposed, and which systems need attention.
AI-assisted discovery can help build and maintain a more accurate view of the environment. This matters because security weaknesses often begin with poor visibility. Untracked cameras, forgotten devices, outdated firmware, default credentials, and inconsistent network segmentation can all create risk.
Zugec also highlighted asset discovery and configuration review as realistic applications. “For both systems integrators and end users, the most realistic near-term applications are on the advisory side of the spectrum - AI agents that discover assets, surface misconfigurations, flag anomalies, and recommend actions, with a human making the final call,” he said.
This type of use case fits well with the integrator’s role. Integrators are often responsible for designing, deploying, maintaining, and upgrading systems across many customer sites. A tool that helps them identify misconfigurations or inconsistent device settings across multiple deployments could improve both security and service quality.
“Systems integrators are a particularly important leverage point here — they manage deployments at a scale that no single end user can match, and AI-assisted tooling for asset discovery and configuration audits could have outsized impact across the industry if adopted at that layer,” Zugec said.
The value is not limited to cybersecurity teams. Better asset data can support lifecycle planning, firmware management, compliance documentation, service contracts, and system refresh strategies. For consultants, it can also strengthen risk assessments by providing a more evidence-based view of what is deployed and how it is configured.
Why the basics still matter
A recurring theme in the expert comments is that AI does not remove the need for strong operational foundations. In fact, AI agents may depend on those foundations to function safely.
Zugec cautioned against moving too quickly without doing the underlying work. “What makes this harder is that AI adoption right now looks like a sprint. Everyone is moving fast, deployments are accelerating, and the results are visible — but so are the failures,” he said.
For physical security professionals, this means AI adoption should not be treated as a shortcut around documentation, system design, governance, and cyber hygiene. AI tools will be more effective when they operate in environments with clean asset inventories, reliable network maps, known device ownership, consistent configurations, and documented interfaces.
“Securing physical security infrastructure with AI agents requires the speed of a sprint and the consistency and methodical discipline of a marathon,” Zugec said. “Skipping the foundational work — clean asset inventories, reliable network maps, clear device ownership doesn't accelerate progress. It just means the mistakes, when they come, land harder.”
This is particularly relevant in video surveillance and access control, where systems are often expanded incrementally. New cameras may be added as sites grow. Access control may be integrated with visitor management, identity platforms, elevators, parking systems, or building management systems. Over time, the environment can become complex, with dependencies that are not always fully documented.
AI agents operating in such environments need accurate context. Without it, they may misinterpret normal operational patterns, fail to understand device criticality, or recommend actions that conflict with business or safety requirements.
What remains aspirational
The more ambitious vision for AI agents is full autonomous remediation, threat hunting, and predictive attack modeling across cyber-physical systems. Experts do not rule out these possibilities, but they remain cautious about their use in operational environments.
Goncharov said advanced applications are still not practical for most deployments. “More advanced applications, such as fully autonomous response and remediation or threat hunting and predictive attack modeling across cyber-physical systems, remain more aspirational than practical for now,” he said. “While technically possible, they require higher levels of creativity, trust, transparency and explainability.”
He added that safeguards remain necessary before such capabilities can be deployed in critical environments. “Operational safeguards are still needed before they can be safely deployed for mission-critical systems and in operational environments, especially where disruptions may directly impact safety and physical security,” Goncharov said.
Zugec made a similar point, linking the limits of autonomy to the condition of the operating environment rather than simply to AI capability.
“What remains aspirational is full autonomous remediation - not because the AI isn't capable, but because the foundational environment it needs to operate reliably in isn't ready,” he said. “Clean inventories, documented APIs, consistent configurations, clear ownership - these are the prerequisites. Until the marathon work is done, autonomous action at scale remains more ambition than practice.”
For systems integrators and consultants, this distinction is important when advising customers. AI should not be positioned as a replacement for sound system architecture or operational governance. Instead, it should be introduced as a tool that can strengthen visibility, accelerate analysis, and improve decision support when deployed within clear limits.
Implications for physical security professionals
The practical path for AI agents in physical security is likely to be incremental. Integrators can begin by using AI-assisted tools to improve asset discovery, identify exposure, monitor vulnerabilities, review configurations, and flag anomalies. These applications can provide measurable value without handing over control of critical functions.
At the same time, integrators should help customers define where automated action is acceptable and where human approval is required. A low-risk recommendation, such as flagging outdated firmware, may be suitable for automated ticket creation. A high-impact action, such as isolating an access control controller or disabling a camera connection, should require stronger review.
Consultants can also help customers build the foundations that make future AI use safer. This includes accurate system documentation, segmentation strategies, device ownership records, configuration baselines, escalation workflows, and incident response plans that reflect the operational importance of physical security systems.
The key lesson is that AI agents are not simply another layer of automation. In physical security environments, their deployment must be judged by how well they preserve safety, continuity, visibility, and control.
For now, the most useful role for AI is to help professionals see more, understand faster, and act with better context. Full autonomy may come later, but in video surveillance, access control, and integrated security environments, human oversight remains central to responsible deployment.