You've probably been in this meeting. Leadership announces the company needs to “start using AI” across the organization. There is some talk about it. The meeting ends. And then... nothing. Because nobody actually knows what that means in practice – especially in security operations.
According to Pro-Vigil's "The State of Physical Security Entering 2024" report, 71% of businesses aren't currently using AI for security, and 57% aren't even sure if it can help. There's a massive gap between the executive mandate and the practical reality of running a security operation.
But there’s hope for security teams looking to use their data in an intelligent way to make better decisions, maximize resources, and prove security’s value to the C-suite.
Here’s how to translate "start using AI" into concrete, actionable steps for physical security teams.
Here's the thing: AI in physical security isn't about replacing human judgment. It's about giving security professionals better tools so they can focus on what humans do best, making nuanced decisions about complex situations.
When leadership says "start using AI," they're rarely asking you to build a neural network from scratch. What they're actually asking for, whether they realize it or not, is for you to solve problems faster and smarter.
In physical security, that typically translates to:
The stuff that eats up your team's time: alarm classification, footage review, and report generation. The work that keeps you stuck at your desk instead of thinking strategically. AI-powered security systems were identified as the top physical security trend in 2024, transforming video surveillance with real-time detection and analysis capabilities.
Because the difference between a missed threat and a caught one is often measured in seconds, not minutes, AI can process multiple video feeds simultaneously and flag anomalies faster than any human operator (then it can send alerts to its human supervisor for verification).
You've got terabytes of video footage and thousands of alarm records sitting in your systems doing nothing. What story are they telling? What patterns are you missing?
Your access control system can actually communicate with your video management system without manual intervention or complex workarounds. Breaking down these silos is essential for creating a unified security operation. (Read more about breaking down security silos in connected ecosystems.)
Here's the thing: AI in physical security isn't about replacing human judgment. It's about giving security professionals better tools so they can focus on what humans do best, making nuanced decisions about complex situations.
Modern AI can distinguish between a person, a vehicle, an animal, and a plastic bag blowing in the wind. That sounds basic, but it's revolutionary when you're dealing with hundreds of cameras and the incoming feeds they create. The technology has matured to the point where it's not just detecting objects; it's understanding context and behavior patterns. According to market analysis, video surveillance comprises 52.1% of total physical security revenue in 2024, with AI adoption accelerating rapidly.
This is where AI is having the biggest immediate impact. Traditional motion detection systems are notoriously noisy. SecurityInfoWatch reports that a typical central station operator is exposed to at least three alarms per minute, with up to 95% of those alarms being false positives.
Modern AI systems are now reducing false alarms by 90-95%, according to multiple industry sources. This means your team can actually focus on real threats instead of chasing shadows, or moths, weather changes, or that one tree branch that triggers motion detection every single day. (For a deeper dive into this topic, read our guide on how to reduce physical security false alarms by 90%.)
AI can analyze patterns in your camera feeds and system logs to predict when equipment is likely to fail. It's like having a maintenance schedule that actually reflects reality instead of arbitrary time intervals. This reduces downtime and extends the life of expensive security equipment.
AI can learn normal access patterns and flag anomalies, such as someone badging into areas they don't usually access or unusual after-hours activity. It's not about Big Brother; it's about surfacing signals that would otherwise be invisible in the noise.
Instead of manually reviewing hundreds of incident reports to spot patterns, AI can analyze them in seconds and tell you, "Hey, we're seeing a spike in tailgating attempts in Building C on Friday afternoons." This type of insight allows you to allocate resources proactively rather than reactively.
Before you start shopping for AI solutions, you need to get honest about where you are. Here's a practical self-assessment framework:
Be specific. "Reviewing footage" isn't specific enough. Is it:
Track how much time these tasks actually take. You need baseline metrics to prove ROI later.
You're probably collecting way more data than you're analyzing:
What's just sitting there? What insights are you missing?
This is hard to answer because by definition, you don't know what you're missing. But you can look at:
If you're manually correlating information from different systems, that's a strong AI candidate. Humans shouldn't be the middleware between your VMS, access control, and alarm systems.
Whatever comes up as your biggest pain point – that's your AI priority list.
Here's the catch: you need to think about data security from day one. Questions to answer:
These aren't afterthoughts - they're core requirements. You also can't ignore compliance and privacy concerns. If you're deploying facial recognition, you need to know the laws in your jurisdiction. If you're analyzing employee movement patterns, you need clear policies and consent mechanisms.
The worst thing you can do is try to implement AI everywhere at once. You'll burn budget, exhaust your team, and probably end up with a bunch of shelfware.
Instead, follow this approach:
Pick something that's:
For most teams, that's false alarm reduction or footage review - not because they're the most exciting initiatives, but because they deliver immediate, measurable results.
Track how long your team spends on it now. Document specific examples. After you implement AI, track again. The time savings are your proof point. Be specific: "Investigation time reduced from 4 hours to 30 minutes per incident" is better than "things got faster."
Not "we're using AI" but:
Get your operators and analysts actually using the tool. Listen to their feedback. When they start telling other teams about it unprompted, you know you're onto something. Their testimonials will be worth more than any vendor pitch when you're ready to scale.
You don't need to transform your entire operation overnight. You need one solid win that makes people say, "Okay, this actually helps."
The security AI market is crowded, and unfortunately, it's full of rebranded video analytics being called "AI." Here's how to separate signal from noise:
"What specific problem does this solve?" If they can't give you a concrete answer beyond "AI-powered security," run. You need specifics: "Reduces time spent verifying motion alarms from 2 hours daily to 15 minutes."
"What's your false positive rate, and how was it measured?" Anyone can claim 95% accuracy. Ask for the methodology. What dataset? What conditions? Independent testing or self-reported?
"How does your AI actually work?" You don't need a PhD to understand the basics. If they can't explain it without buzzwords, they either don't know or are hiding something. Red flag phrases: "proprietary AI magic" or "advanced algorithms."
"What data do you need, and where is it processed?" On-device processing is different from cloud processing. Each has tradeoffs:
"How do you handle model drift and retraining?" AI models degrade over time as conditions change (new lighting, seasonal changes, facility modifications). How do they handle this? Automatic retraining? Manual updates? This is critical for long-term performance.
"What does implementation actually look like?" Get specifics on:
Get documentation on:
Measure what matters:
Time savings:
Detection improvements:
Cost impacts:
Not "better security" or "improved awareness" – those are outcomes, but they're too vague to measure and too easy to manipulate.
Here's what I want you to take away from this: "Start using AI" isn't a directive to become an AI expert. It's a directive to start solving problems differently.
You don't need to understand transformer architectures or neural network optimization. You need to stay a security expert who uses better tools. That's it.
The reason is that the security landscape is changing quickly. According to industry analysis, video surveillance represents 52.1% of physical security revenue, and the services segment is growing faster than hardware. AI is becoming table stakes, not a differentiator. The question isn't whether to adopt it, but how to do it strategically.
Not this year. This quarter.
Make it specific:
Make it measurable:
Make it matter to your team's daily work:
Start there. Prove the value with real numbers. Document the before and after. Get testimonials from your operators about how it changed their day.
Then scale.
That's how you turn "start using AI" from a vague mandate into a concrete improvement in how your team operates.
Want to talk more about AI in physical security operations? Check out what we're building at HiveWatch.