AI agents are rapidly transitioning from theoretical concepts to practical tools within physical security infrastructure, presenting significant opportunities for systems integrators and consultants. These intelligent tools can revolutionize how we identify exposed devices, maintain asset visibility, monitor vulnerabilities, detect anomalies, and ensure consistent configuration reviews across video surveillance, access control, and other interconnected security systems.
However, the very environments that make AI so appealing also introduce substantial risks if automation remains unchecked. A mistaken action in a physical security deployment isn’t merely an IT alert or a temporary service outage; it can directly impact video availability, door access, incident response, and overall operational continuity. This crucial distinction is shaping expert perspectives on the near-term role of AI agents in physical security. The emerging consensus suggests that AI can be an invaluable advisory and analytical layer, but full autonomous response is difficult to justify in mission-critical environments where safety and security operations are directly at stake.
Evgeny Goncharov, Head of Kaspersky ICS CERT, highlights the escalating risk when automated actions become overly aggressive. “Aggressive automated responses increase the risk of false positives,” Goncharov warns. “This could lead to unnecessary isolation of devices or interruption of services, affecting operational continuity and safety.”
This caution is particularly pertinent for physical security professionals, as many modern deployments now straddle the line between 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 deeply integrated with broader enterprise networks. This interconnectedness not only expands the attack surface but also creates intricate operational dependencies. While isolating a compromised endpoint might be standard practice in a conventional IT environment, the same action in a physical security context could disable a camera, block a door controller, or blind a security operations center during a critical incident.
The Peril of Autonomous Response
The fundamental challenge isn’t whether AI can identify risks or suggest actions—experts agree it increasingly can. The more profound question is whether AI should be permitted to act independently when the ramifications of a false positive can ripple into the physical world. Martin Zugec, Technical Solutions Director at Bitdefender, frames the issue around the undeniable reality of mistakes. “The starting point for this conversation should be an honest premise: AI agents will make mistakes. So will humans,” Zugec asserts. “We need to accept that failure is not a possibility to mitigate, it’s a certainty to design for.”
This perspective is vital for integrators, as physical security systems are typically expected to maintain continuous operation. Cameras must remain online, doors must function according to policy, and 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 emphasizes.
This doesn’t negate the role of AI agents in physical security; rather, it underscores the need for their roles to be meticulously defined. In many deployments, AI should augment human decision-making rather than supplant it. The practical architecture involves AI identifying risks, explaining issues, prioritizing responses, and recommending next steps, with a human operator, integrator, or security administrator making the final decision. Goncharov notes that this advisory model is already widely adopted: “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.”
For consultants and integrators, this has direct design implications. AI-enabled cybersecurity tools for physical security must be evaluated not only on their detection capabilities but also on their workflow design. Critical questions include how alerts are presented, whether recommendations are explainable, how confidence levels are displayed, what approval steps precede action, and how easily operators can reverse or override a decision.
Near-Term Opportunities for Integrators
The most realistic opportunities for AI agents currently lie in the advisory aspects of security operations. These are areas where AI can significantly reduce manual effort, enhance consistency, and help teams identify risks more rapidly without granting the system unchecked control over the environment.
Goncharov outlines 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.”
For physical security integrators, continuous asset discovery may prove to be one of the most immediately valuable applications. Many end-users manage complex, heterogeneous environments built over years, often featuring devices from multiple vendors, inconsistent documentation, and incomplete records of firmware versions, network locations, and ownership. This complexity makes it challenging to ascertain what is truly deployed, which devices are exposed, and which systems require attention. AI-assisted discovery can help build and maintain a more accurate, up-to-date view of the environment. This is critical because security vulnerabilities frequently originate from poor visibility—untracked cameras, forgotten devices, outdated firmware, default credentials, and inconsistent network segmentation can all introduce significant risk.
Zugec also highlights asset discovery and configuration review as highly 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 explains.
This type of use case perfectly aligns with the integrator’s role. Integrators are frequently tasked with designing, deploying, maintaining, and upgrading systems across numerous client sites. A tool that assists them in identifying misconfigurations or inconsistent device settings across multiple deployments could dramatically enhance 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 states.
The value extends beyond cybersecurity teams. Enhanced asset data can bolster 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 deployed assets and their configurations.
Why the Fundamentals Still Matter
A consistent theme among experts is that AI does not negate the necessity for robust operational foundations. In fact, AI agents often depend on these very foundations to function safely and effectively.
Zugec cautions against rushing AI adoption without first laying the groundwork. “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 observes.
For physical security professionals, this means AI adoption should not be viewed as a shortcut around meticulous documentation, sound system design, stringent governance, and diligent cyber hygiene. AI tools will be far more effective when operating in environments characterized by clean asset inventories, reliable network maps, clear device ownership, consistent configurations, and well-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 asserts. “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 especially pertinent in video surveillance and access control, where systems are often expanded incrementally. New cameras are added as sites grow, and access control systems may integrate with visitor management, identity platforms, elevators, parking systems, or building management systems. Over time, these environments can become incredibly complex, with dependencies that are not always fully documented. AI agents operating in such environments require accurate context. Without it, they may misinterpret normal operational patterns, fail to understand device criticality, or recommend actions that conflict with crucial business or safety requirements.
What Remains Aspirational
The more ambitious vision for AI agents encompasses full autonomous remediation, sophisticated threat hunting, and predictive attack modeling across complex cyber-physical systems. While experts don’t dismiss these possibilities entirely, they remain cautious about their immediate deployment in operational environments.
Goncharov notes that advanced applications are not yet 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 says. “While technically possible, they require higher levels of creativity, trust, transparency and explainability.” He further emphasizes the need for safeguards before such capabilities are introduced into 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 adds.
Zugec echoes this sentiment, linking the limitations of autonomy more to the state of the operating environment than solely to AI’s capabilities. “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 explains. “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, understanding this distinction is paramount when advising clients. AI should not be pitched as a substitute for sound system architecture or robust operational governance. Instead, it should be introduced as a powerful tool that can enhance visibility, accelerate analysis, and improve decision support when deployed within clearly defined limits.
Implications for Physical Security Professionals
The practical trajectory for AI agents in physical security will likely be incremental. Integrators can begin by leveraging AI-assisted tools to refine asset discovery, pinpoint exposures, monitor vulnerabilities, review configurations, and flag anomalies. These applications offer tangible value without ceding control over critical functions.
Simultaneously, integrators should assist clients in defining where automated actions are acceptable and where human approval is indispensable. A low-risk recommendation, such as flagging outdated firmware, might be suitable for automated ticket creation. However, a high-impact action, like isolating an access control controller or disabling a camera connection, demands rigorous human review.
Consultants can also guide clients in building the foundational infrastructure that will enable safer future AI utilization. This includes accurate system documentation, strategic segmentation, meticulous device ownership records, established configuration baselines, clear escalation workflows, and comprehensive incident response plans that reflect the critical operational importance of physical security systems.
The core lesson is that AI agents are not merely another layer of automation. In physical security environments, their deployment must be evaluated by how effectively they safeguard safety, continuity, visibility, and control. For now, AI’s most valuable role is to empower professionals to see more, understand faster, and act with enhanced context. While full autonomy may eventually arrive, in the critical domains of video surveillance, access control, and integrated security environments, human oversight remains indispensable for responsible deployment.