Physical security is evolving from crisis response to threat anticipation. Relying solely on alerts after an event has breached the perimeter leaves organizations vulnerable. The objective now is to predict and mitigate risk early, a shift made possible by advancing AI capabilities.

Yet many teams are still relying on older technology, manually piecing together data, or working in silos. A collection of disconnected systems can’t provide the level of awareness needed to mitigate risks that can disrupt operations, impact revenue, or threaten people. 

From power outages and supply chain disruptions to executive travel risks and severe weather, security leaders must anticipate these events and their business impacts before they hit the headlines.

Imagine this: after seeing a news report, a CEO calls the security director to ask whether the security team is aware of a geopolitical disruption that could pose a security risk to an important supply shipment. The security director could be aware of that incident before it was in the news and already be executing a mitigation strategy before hearing from the CEO. 

Siloed systems can’t support these types of anticipatory and comprehensive responses. Open, unified architectures integrated with advanced AI models enable this proactive stance. By incorporating new internal and external data sources, enterprise organizations can understand their environment locally and abroad before an issue escalates. This shift is changing what’s expected of security teams.

The Changing Role of Physical Security

Physical security teams used to focus on responding to incidents and protecting their facilities from on-premises threats. The department was considered a cost of doing business, a must-have for large-scale organizations. 

Today, proactive security teams actively drive operational resilience and safeguard business continuity. To achieve these goals, security teams look beyond their physical footprint.

Video evidence, access control logs, sensors, alarms, and incident reports are still important, but they’re limited to what’s happening on the facility or campus grounds. 

What about external threats, such as a security issue within the city where the HQ is located, or a mounting conflict near a remote office? Spotting, interpreting, and combining external signals with internal alerts elevate a company’s security posture.

Nearby public safety events and instability are not only security threats but also business issues that can affect workforce retention, daily operations, and the customer experience. If there’s a major fire at a supplier’s distribution center or a shutdown at an important port, this could affect the supply chain. Geopolitical instability can disrupt critical vendor relationships or executive travel plans. Even an unplanned mass demonstration might require immediate, alternate arrangements for on-site employees. Early detection of these disruptions is an invaluable asset to a modern security team.

These aren’t traditional security events, but they can have just as much impact on the business. An advanced warning could be the difference between a close call and a multi-million-dollar loss. 

Predicting Threats Through Synthesized Data

Some of the most critical risk signals aren’t coming from your security systems at all. There are millions of public data sources that can indicate emerging security threats.

Security teams have been gathering data for years: license plate numbers, access times and locations, entry points, video surveillance data, and more. Combining these internal metrics with external sources improves resilience and supports broader organizational goals.

The challenge isn’t a lack of data; it’s the sheer volume. No team of operators could possibly manually process it all.

Whenever there’s a public incident, one of the first places it’s posted is on social media. The volume of text coming from even one popular social media platform each day would take a thousand years for a human to read. Other sources include weather data, electricity outage maps, police scanners, and traffic cameras, and the noise becomes overwhelming. 

This is the specific problem AI solves. By rapidly analyzing vast amounts of input, AI identifies patterns and surfaces potential issues far earlier than human operators could on their own.

With the right technology to collect and process data sources, security teams can identify patterns or unusual behavior. By combining these insights with internal security systems, operators gain greater situational awareness and can respond more effectively. 

Partnerships between innovative companies at the intersection of AI and security provide tools to help operators filter out the noise and isolate the critical insights, both internal and external. Early awareness of potential threats buys the security team time to assess options and activate contingency plans.

While AI tools are incredibly valuable, many security organizations have yet to make this shift, continuing to operate in reactive environments. They’re managing alarms, reviewing incidents, and piecing together information after something has already happened. As the volume of data and complexity of risks grow, that approach becomes harder to sustain.

Turning Intelligence Into Action — With a Human in the Loop

According to the Genetec 2026 State of Physical Security Report, 46% of respondents plan to integrate AI or large language model (LLM) applications in their security environment over the next few years.

But there’s still a gap between interest and trust. Seventy percent of end-user respondents are concerned about how AI systems are designed and implemented.

This hesitancy comes from a valid concern. Many mass-market LLM tools are prone to ‘hallucinations,’ producing answers that are confidently wrong. Because these tools rely on statistical guesses rather than factual verification, they often generate outputs that seem entirely plausible but are fundamentally incorrect.

In a security context, this can’t happen. Accuracy matters. And it’s the human security team that’s responsible and accountable for any decisions.

The best security-specific applications of AI don’t use off-the-shelf technology. The most effective security deployments use models that are carefully configured and trained to improve reliability. These models are designed with robust governance and security frameworks to ensure operational integrity and reliability. In fact, the ISO 42001 certification is the first international standard for AI systems, establishing a comprehensive framework to ensure responsible governance of AI technologies.

Even with these certifications, a human must always remain in control. AI can reduce manual work, support faster processing, and trigger standardized workflows, but humans must retain final oversight. 

Security environments are unpredictable. They require context, experience, and the ability to interpret ambiguity. Machine learning models are effective at recognizing patterns and surfacing information, but they can’t grasp the intricacies of real-life events as a security operator can.

AI tools can support operators, not replace them.

Avoid ‘Walled Gardens’

To get the most out of AI technology, the AI software needs to work with the rest of your security environment.

Getting locked into a single vendor, or a ‘walled garden,’ limits access to new innovations. A closed, single-source solution may appear simpler at first, but it restricts an organization to one provider’s roadmap and one approach to development.

Vendors attempting to build everything in a vertically integrated ‘stack’ often deliver solutions that look impressive on paper but fail to adapt over time. A closed system inherently struggles to incorporate new devices, sensors, external intelligence sources, or custom workflows. Over time, these limitations compound.

Open architecture offers a different approach. It allows you to combine specialized technologies from the vendors you select. This is where partnerships between companies like Genetec and Dataminr demonstrate their value. By combining rigorously tested AI technology with a unified, open-architecture security system, departments access all the data and tools they need within a single interface. 

Layering AI tools over fragmented systems only adds complexity. Without a unified foundation, organizations end up with a collection of disconnected data. Security operators should not have to toggle between screens and cross-reference information to ensure nothing is missed. Unified platforms eliminate this friction, enabling organizations to manage complex physical environments from one cohesive system.

The Next Frontier

End users already see the value in systems that leverage AI to improve security by helping operators automate tasks, speed up investigations, reduce noise, and prioritize the events that matter. 

However, that’s the beginning of what’s possible.

AI agents are now scouring billions of data signals from more than one million public data sources to provide even more threat intelligence. These agents operate at a scale and speed that a human team can’t accomplish.

A few examples of the possibilities

• Campus police: Eyewitness accounts of an incident on social media are immediately fed to the operations team, triggering lockdown procedures and a rapid response.
• Public safety: Online chatter indicates a large public event is drawing unexpected crowds, allowing safety teams to consolidate data and pre-emptively adjust staffing levels.
• Manufacturing: Weather tracking combined with supply chain mapping predicts emerging disruptions, enabling operational teams to reroute shipments.
• Executive security: Local news alerts indicate infrastructure disruption near a traveling executive, prompting protection teams to adjust routes immediately.

These capabilities continue to elevate security departments from a cost center to an ROI driver. But the real advantage is clarity. Security teams struggle to make decisions with incomplete information. When internal data, external signals, and workflows are connected, those decisions become clearer and easier to act on.

We’re already at the point where leading technologies can predict the likely path of a wildfire based on where it starts and on wildfire patterns over the last two decades. In the near future, platforms will not only anticipate risk but also deliver highly personalized daily risk assessments that detail exactly how an emerging trend will impact specific organizational assets. 

As security teams gain access to richer intelligence and connected workflows, their mandate is expanding from basic protection to ensuring complete operational resilience.