Data and AI: Disruption – and Opportunity – is Happening Now

This article originally appeared in City Security Magazine’s 2025 Summer Issue.

 

Security operations are changing. Over the past few years, the conversation around AI and how it will disrupt the industry has mostly been limited to buzzwords and conference keynotes. Now, the industry is at a point where talk is being turned into action and business intelligence, data analytics and artificial intelligence are working together to solve real problems in ways that matter to security businesses.

Look at how security firms work today. Managers juggle complex challenges every day. Staff scheduling often feels like a never-ending puzzle. Decisions happen too late. Profit margins stay thin. Clients want more, but companies struggle to deliver – or to prove they have delivered.

The problem isn’t lack of data. Its lack of meaningful clean and curated data. Security guards who are using workforce management technology and ERP systems are already capturing guard movements, client interactions, scheduling details and performance metrics. Still, much of this data sits unused, locked in a myriad of tables in your source ERP systems. 

Powerful data analytic and AI systems will change how we use this information, become more agile and make truly data-driven strategic choices.

 

What’s Shifting in Security Ops

Security teams aren’t new to digital tools. Many already use software for scheduling, payroll, patrol logs and incident tracking. But just using systems isn’t the same as getting full value from them. Right now, the gap is in how data is handled.

Even companies that rely on ERP or workforce management systems often find themselves flying blind when they need fast answers. That’s because most analytical and AI tools and platforms aren’t built for deep analysis or real-time insights that are industry specific. As a result, many companies operate reactively: schedules are fixed after a problem happens, rather than ahead of it. Staffing shortages catch teams off guard. Service lapses are discovered only after a client complaint.

And none of this is due to a lack of data. Security firms are collecting a huge amount already – from clock-in data and patrol check-ins to site notes and guard certifications. The challenge is organising and analysing that data in a near real-time way that helps managers make better decisions, faster.

 

Why Better Data Matters More Than More Data

A large volume of information doesn’t help if it’s hard to access, slow to process, or siloed across platforms. That’s where analytics and AI come in – not as flashy features, but as tools that connect the dots.

The real opportunity isn’t in replacing your existing systems. It’s in making them smarter.

By layering intelligence onto the platforms you already use, you can take advantage of data that’s just been sitting there. Think of it like turning on the lights in a room you’ve always used in the dark. Same room, different visibility.

For example, instead of manually adjusting schedules every week, you can forecast staffing needs based on actual patterns and fast-moving scheduling changes. Instead of reviewing weekly reports to spot problems, you can see problems and solve them before guards ever leave shift. Instead of being reactive, you’re being proactive.

 

What Happens When You Add Intelligence

When these tools support true business intelligence, you start gaining real insights. Things you can gain include: 

• A clearer insight into real-time job performance against budget. Instead of waiting for end-of-month reports to understand whether a job ran over budget, you can see that data in near real time. That helps you spot overspending earlier and make changes before it hits your margins. 
• Track turnover trends and retention risk. Identify patterns that often lead to attrition—like last-minute shift changes, excessive travel between sites or inconsistent hours. When you know which guards are at higher risk of leaving, you can intervene earlier with support or reassignments to improve retention. 
• Exception reporting. With analytics layered into your security software platform, you can more easily detect anomalies when something is operating outside the norm. This reduces surprises and makes compliance easier to manage. 
• Employee schedules align better with actual needs. Predictive analytics can use past data—seasonal trends, site-specific risk levels contract terms—to recommend schedules that match expected demand. That avoids both under-coverage and unnecessary labour costs. 
• KPIs are not disparate. Things like average cost per post, revenue per hour worked and client satisfaction tied to guard performance can be pulled from your system automatically – no matter who is pulling them. And because the data is live, you’re not relying on backward-looking reports to guide next month’s decisions. 

All of this adds up to a better grip on operations and stronger client delivery.

 

The Financial Side of Smarter Ops

For a lot of security companies, labour is the biggest expense. So small improvements in scheduling or coverage can create large savings. 

And when guard turnover is high (as it is for much of the industry), anything that helps make the job more stable – like more consistent shifts or fewer last-minute changes – helps reduce churn. That translates into lower hiring and training costs.

There’s also value in what you can prove. If your systems help you track exactly how SLAs were met, or how quickly an issue was handled, that improves client trust. It can also help in competitive contract bids.

Security’s Next Phase Isn’t in the Future – It’s Now

This isn’t a five-year plan. Many companies are already making these changes. The tech exists and in many cases, the data is already being collected. What’s different now is how that data is being pushed for maximum value to your bottom line. 

For firms willing to take this next step – by recognizing this technology is no longer future-state, but happening necessary now – the payoff is clear.

 

Author: David Libesman, SVP & GM, AI & Data Analytics