4 Steps to Operationalizing AI in Physical Security
Key Highlights
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Disconnected security systems continue to limit AI's effectiveness and create operational and compliance challenges.
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A four-step framework outlines how organizations can build the data foundation needed for AI-enabled security operations.
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Connected security platforms can help organizations improve operational efficiency, scalability and incident defensibility.
The rush to bring AI into physical security has opened an operational gap, not because security leaders made poor decisions, but because of how the industry has historically shipped technology: as siloed, single-purpose tools. Security teams are deploying advanced, AI-driven capabilities that flag threats efficiently, yet those systems remain disconnected from the broader corporate data strategy. In fact, 40% of organizations are still running completely separate, siloed systems for video surveillance and access control, rather than unified or integrated platforms.
This operational void defines the “missing middle” — the gap between systems that can describe isolated events and systems that can explain incidents in context. It is the fragmented space between single-purpose security tools and a truly interconnected security operation. Allowing this gap to persist exposes enterprises to severe regulatory risks, as operators are flooded with disconnected alarms, causing massive alert fatigue.
A roadmap for operationalizing AI
Solving the “missing middle” requires security executives to shift their focus from simply acquiring tools that stand alone to building a truly unified infrastructure where systems can seamlessly share data. Here is a four-step guide to help enterprise organizations do this:
Step #1: Shift from reactive alerting to proactive operations
To move past isolated alerts and disparate systems, security leaders must first evaluate where their capabilities sit on an objective operational maturity curve. The Path to Intelligent Security, the industry benchmark Convergint published earlier this year, maps a clear progression across five distinct operational levels: Detect, Describe, Explain, Recommend and Act.
Currently, the majority of enterprise security organizations operate at the Detect or Describe levels: meaningful, foundational positions where real detection and documentation capability is being built. The question is not whether that progress counts. It is whether there is a plan to advance beyond the industry’s “treeline” — the point where systems begin validating one another and isolated data becomes connected intelligence. This integration gap mirrors a broader corporate trend: 37% of companies use AI only at a surface level, with little to no change in their underlying business processes. This underscores the security industry's largest untapped source of value: the distance between what is already deployed and what is operationally possible.
Step #2: Close the “missing middle” with proof
After moving systems from reactive to proactive, security leaders must look beyond simple threat detection to protect the enterprise from costly legal and compliance risks. Bridging the “missing middle” and connecting systems takes an organization from simply spotting problems to establishing and advancing a rigorous, defensible standard of care.
For example, when an incident occurs, an interconnected system links data from video, access control and intrusion systems to simultaneously validate the threat, clarify the cause and package the evidence. This defensibility is imperative in high-stakes environments where fragmented data is no longer just an operational headache, but a regulatory liability. In fact, this risk is fundamentally shifting how enterprises buy technology, with 40% of channel partners now citing compliance and governance requirements as the primary driver behind their clients' decisions to replace legacy systems entirely.
Step #3: Accelerate data readiness to close the operational void
To move past basic security and into automated, intelligent operations, security leaders must rewrite the rules of their underlying data layer. Teams cannot deploy advanced AI or automated response workflows if your video, access control and intrusion data are formatted differently and locked in separate databases.
Before an organization implements advanced automation such as forecasting and orchestrated response, it must establish strict data requirements for all physical security systems. This includes requiring documented, standards-based Application Programming Interfaces (APIs), normalized event taxonomies and structured data schemas. The test for any system is not whether it is proprietary or open. It is whether it can participate. This ensures systems speak the same language and allows automated tools to easily pull and parse information.
Fixing this data infrastructure is what allows security teams to transition from manual, disparate monitoring to automated monitoring and management. By shifting personnel from real-time execution to strategic governance, they are enabled to scale their security operations across expanding footprints without massive headcount growth. At its highest level, this model lets digital agents handle routine tasks at high speed allowing security leaders to stop managing people on a shift and start supervising a network of automated tools, stepping in only when a complex situation truly requires human judgment.
Step #4: Build a multi-stage AI roadmap
When mapping out an AI strategy, leaders must evaluate use cases on two criteria: business impact and data readiness. They must prioritize the specific problems where AI can deliver the highest immediate return, but only if your existing system data is clean enough to support it. Below is a summary of this multi-phase approach:
● Phase 1: Executives must focus on data quality and backend fixes, including documented, standards-based APIs and event normalization. Organizations should resist flashy AI features until the backend infrastructure can seamlessly communicate to detect and describe basic incidents within individual systems.
● Phase 2: Connecting those siloed systems to enable cross-functional, multi-sensor reasoning. This step allows tools to cross-reference data and explain complex incidents in real time.
● Phase 3 (or the final goal): automated, connected security operations. This is where advanced digital tools move from explaining what already happened to forecasting what might happen next, recommending predictive interventions and safely orchestrating automated incident-response runbooks at scale.
Fulfilling the executive duty of care
It is clear that the AI capabilities required to safeguard the enterprise already exist. The mandate for security leaders is no longer to acquire more technology, but instead to finally connect and integrate the tools they already have.
Ultimately, the forward-looking executives who successfully navigate this transition will do more than protect physical assets. They will fulfill their executive duty of care, strengthen operational defensibility and reposition the security function as a measurable driver of enterprise resilience.
About the Author
Eric Yunag
Executive Vice President, Products and Services
Eric Yunag is executive vice president of products and services at Convergint. With more than 25 years of experience in the security integration industry, he joined Convergint in 2016 through the acquisition of the company where he served as CEO. He now oversees product strategy, strategic technology partnerships and the development of the company's solution offerings.