Category: Featured

Measuring SOC performance: Why your team’s confidence might hide the real gap

Posted on July 8, 2026 by - Featured, Industry Expertise

Ninety-three percent of physical security leaders say they’re confident their program would detect a coordinated threat.

That number sounds great. It also happens to be nearly meaningless.

We surveyed 300 security professionals at U.S. organizations with 500+ employees and an established SOC (or concrete plans to build one). We asked them how they’d rate their programs. We asked how confident they felt. And then we asked something harder: How often do you actually meet your own SLAs?

The answer: 19%.

Only 1 in 5 organizations always meets their service level agreements for incident response time, alarm processing rate, and time to resolution. The other 81% fall short of their own commitments at least some of the time. More than 40% miss them often enough that “sometimes” or “occasionally” was the most honest answer they could give.

Confidence runs 74 points ahead of actual performance.

You can’t effectively manage physical security operations without measuring them

Most security teams don’t have reliable mechanisms to track SLA performance.

When teams can’t measure consistently, they fill the gap with confidence. The program feels mature because the team is experienced, the technology is in place and the SOC is running. Everything seems fine because there’s no clear signal that it isn’t.

That’s not negligence. It’s a systems problem. And it’s the most important finding in our research on physical security operations management.

The same teams processing 342 alarms a day – roughly 1 in 3 of which is false, meaning more than 100 noise events arrive before a real alarm – still report near-universal confidence in catching a true incident.

Larger organizations hit 421+ daily alarms, with a self-reported false alarm rate approaching 44%. (Within our customer base we find this to be closer to 80-90%.) That volume alone should create doubt. But without consistent SOC measurement against defined standards, it’s difficult to tell what’s accurate.

Structure amplifies this gap between confidence and reality. Among organizations with centralized or consolidated SOCs, 98% report confidence. That sounds like a good sign. Until you remember that program structure is one of the strongest predictors of confidence regardless of how the program actually performs. Feeling organized isn’t the same as being effective.

Five questions to ask to properly measure SOC performance

If you want to move toward data-driven security operations — where performance is measured, not assumed — start here.

  1. What are our SLAs, and are they written down? Not “roughly” or “in someone’s head.” Create specific, documented targets for incident response time, alarm processing rate, and time to resolution. SLAs in security operations centers only work when they’re defined well enough to measure against; make sure everyone knows what they are.
  2. How often do we actually hit them? Remove the guesswork by pulling the data. If you can’t answer this question from a report, that’s your answer. A data-driven security program starts with tracking, not gut checks.
  3. What percentage of our daily alarms are false positives? According to our research, the industry average is 32.5% (and in our experience, can jump to as much as 90% false). At enterprise scale, it climbs to 44%. If your SOC doesn’t know its false alarm rate, operators are triaging noise without knowing how much of their shift is real work.
  4. What are our operators doing that automation could do instead? In our study, the top two tasks operators wanted to eliminate were manually triaging alarms (22%) and sending routine notifications and escalation emails (28%). Half your team’s bandwidth is going to work that shouldn’t require a human. Is yours?
  5. Where does our self-assessed maturity diverge from our SLA data? If you rate your physical security operations management at Level 3 or Level 4 but you’re only hitting SLAs occasionally, that’s the gap worth investigating. Maturity and performance aren’t the same thing, and this research proves it.

These five questions won’t fix anything by themselves. But they’ll tell you whether your confidence is grounded or whether you’re operating with the same blind spot we found across most SOCs in the industry.

The most effective programs aren’t working harder. They’re measuring what matters, supporting operators by automating what doesn’t need a human, and building data-driven security operations that keep pace with their scale.

That’s what closing the confidence gap actually looks like.

Where does your security program stand?

Download the full State of Physical Security Operations in 2026 report, including benchmark data on false alarm rates, AI adoption, SOC maturity levels, and SLA attainment across 300 security programs.

9 key takeaways from ‘Beyond compliance: Driving business value through integrated security operations’

Posted on May 8, 2026 by - Featured, Industry Expertise

Security used to live in the back office. Compliance checkbox. Cost center. Door alarms and badge readers. That era is ending (albeit not as quickly as we’d like) and the security leaders still operating in it are getting passed over for the ones who can connect security to revenue, resilience, and customer trust.

The shift isn’t simple. It means breaking down silos that exist for political reasons, not technical ones. It means translating risk into language the C-suite already cares about. And it means being honest about the gap between the integrated security operations most organizations claim to have and the ones they actually have.

In a recent panel discussion, “Beyond compliance: Driving business value through integrated security operations,” hosted by Steve Lasky from SecurityInfoWatch, panelists dug into that gap: What’s broken, what’s working, and what it actually takes to evolve. The conversation featured insights from:

Here are nine takeaways from the conversation and what each one means for organizations trying to build security programs that earn their seat at the table.

1. Speak the business’s language (or get ignored)

Mark Kelly opened with a fundamental truth. Making the case for integrated security operations starts with cross-functional alignment. Security leaders need to step out of their silo, understand how other teams operate, and quantify the value they’re delivering in terms the business actually cares about.

Mark used supply chain logistics as an example. If your integrated security approach can address pain points around shipment arrival times or in-transit risk, you’re solving a business goal, not just a security one. His advice was clear: find a pain point you can fix, fix it, and quantify the impact.

What it takes: Security leaders fluent in the language of business outcomes. Controls and compliance frameworks aren’t enough on their own.

2. Stop leading with tools. Lead with outcomes.

Don Hough communicated that when you’re talking to the C-suite or the board, the metrics that land are the ones executives already track: reduced downtime, business continuity, operational resilience.

His advice was direct. Stop leading with tools. The conversation isn’t whether you bought the right technology. It’s whether the business can operate effectively with what you have. That reframe changes everything.

What it takes: Security narratives anchored in continuity, resilience, and operational performance. Not product names.

3. “Beyond compliance” means surfacing risks the business didn’t know it had

Ben Hopkins offered a useful reframe of what “beyond compliance” actually means: expanding the scope of risk management itself. Every vertical has different risk profiles, and the technology already deployed, including video, is sitting on insights nobody is using.

Most surveillance footage is never reviewed. But buried in it are tripping hazards, OSHA violations, customer experience issues, HR concerns, and operational inefficiencies. With AI-powered analytics, organizations can surface and act on those risks, as well as generate ROI from systems originally bought for active threat assessment.

What it takes: A willingness to treat security technology as a multi-purpose source of business intelligence, not just a defensive control.

4. Security has earned a seat at the table, and now it has to keep it

Ryan Schonfeld described how security has shifted from back-office function to business enabler and force multiplier. A series of high-profile events, including the UnitedHealthcare CEO shooting, recent civil unrest and looting, the rise in organized retail crime, and the 345 Park Avenue shooting that brought forth the complexities of multi-tenant occupancy, have reshaped how organizations think about security. CSOs and security leaders finally have a seat at the executive table.

But that seat comes with a tab. Boards aren’t asking whether security feels better. They’re asking what the spend produced. Investments need to generate return, and that return needs to be expressed in language business leaders already understand.

What it takes: Security leaders who can demonstrate ROI in the same terms used by finance and operations. The bar for “business-relevant” has moved.

5. Convergence is a people and governance problem, not a technology problem

This was the most consistent theme of the panel. Mark put it plainly: organizations treat convergence as a tech integration problem, but the real failure points are ownership and decision-making. Nobody is responsible for assembling the full picture.

Insider risk is the textbook example. The signals are rarely in one place. HR sees conduct or performance issues. A manager notices behavior changes. Cyber sees unusual logins. Physical security teams see badge anomalies in places people shouldn’t be. Each team escalates differently. Without alignment, the lack of integration becomes the risk.

Ryan was even more direct. He’s been part of fusion center conversations where the technology turned out to be the easiest part. The hard part is getting the right people to the table and getting them to agree. He said, “The real fights are over escalation paths, data ownership, egos, and empire building. Most teams don’t lose to a missing API. They lose to politics.”

Don added the structural piece. Organizations that perform well in a crisis have a clear governance and decision-making model, real-time situational awareness, and the discipline to exercise their plans against real-world risks, not static documents on a shelf.

What it takes: Defined ownership, governance models, escalation paths, and the political will to put the picture above the territory.

6. Compliance is the stick. Culture is the carrot.

Don drew a useful distinction between compliance-driven security and culture-driven security. Compliance is the stick… you have to meet it. But the organizations that thrive create incentives that make security a cultural priority, not just a regulatory one.

Given the speed and intensity of today’s risks, figuring out the right carrots; that is, what makes business units actually want to prioritize security, is critical to building programs that scale.

What it takes: Programs that reward proactive security behavior across business units, not just penalize the ones that fall behind.

7. AI is powerful. It’s also not magic.

The panel agreed that AI is changing how security operations work. Ben highlighted the most obvious win: cutting the time spent reviewing footage. AI-powered video search lets investigators find what they need quickly, ramps up newer operators faster (he pointed out that the next generation isn’t going to put up with clunky software), and turns video into an auditing tool for broader business questions.

Ryan pushed back on the silver-bullet narrative. He said that yes, AI is good at taking data and surfacing outcomes (that’s the magical part). But garbage in, garbage out is a cliché because it’s true. If your systems aren’t talking to each other and your data isn’t normalized, AI can’t deliver on its promise at scale or in a real-time crisis. He flagged a second pitfall too: organizations rushing to buy AI tools before they know what problem they’re solving.

Ryan also pointed out that larger enterprises are now standing up dedicated AI compliance teams. They’re evaluating what’s happening with the data, navigating global privacy laws, and figuring out which tools actually scale across markets.

What it takes: Clear use cases, normalized data, and governance frameworks that account for privacy, scalability, and what the technology can’t do yet.

8. Insider risk is bigger, broader, and more connected than most organizations realize

Mark made a strong case for insider risk as one of the most pressing risk categories (and one where integration matters). Insider risk shows up across the entire employee lifecycle, including onboarding, in sensitive role assignments, elevated access, as well as HR conduct and performance issues. It can be elevated when employees start pulling away, when there are resignations to competitors and reductions in force that needs to trigger secure offboarding.

He also pointed to a less obvious dimension. As manufacturing repatriates, with chip manufacturing returning to the U.S., for example, organizations are bringing in outside expertise to fill domestic SME gaps. There are new insider risk profiles that come with that. Addressing the full picture means bringing data streams together across HR, IT, physical, and management. No single silo can solve it.

What it takes: Cross-functional insider risk programs that span the full employee lifecycle and connect signals from HR, cyber, and physical security.

9. More technology doesn’t equal more security

A thread that ran across the panel: the assumption that more tools means more safety is dangerous. Don called it the “more tech equals more safety” trap. Over-investing in tools at the expense of training, people, policy, and operational integration. Technology, he noted, is only as effective as it is simple.

Ryan mentioned that there’s a point of diminishing returns where alerts and data pile up faster than teams can act on them. The result is the worst of both worlds: critical signals get missed because organizations are drowning in data they don’t know how to use. Ben raised the same concern from the analytics side. Real-time alerts need to be reserved for genuine risks to safety, security, and the business. Other data needs to be analyzed and used to focus resources where they matter most, not pushed at humans hoping someone will sort it out.

Ryan named the structural problem nobody wants to talk about. The security industry has had bad APIs for a long time. Even when systems can technically share data, the lack of standardization across manufacturers makes normalization brutal. A door-forced alarm from one system looks nothing like the same event from another. End users have the leverage to push manufacturers to change that. Most haven’t used it.

What it takes: Discipline to evaluate whether new technology actually improves operations. Investment in training and integration. And pressure on the industry to standardize what should’ve been standardized years ago.

The bigger picture: what successful integrated security operations look like

Pulling these takeaways together, a clear profile emerges of what it takes.

It starts with business fluency at the leadership level, including security leaders who can quantify pain points, align to outcomes, and communicate in the language of the C-suite. It requires governance before technology: clear ownership, decision-making models, and escalation paths in place before systems get integrated. It depends on a culture-first mindset, where incentives make security part of how the business operates rather than a checkbox to satisfy auditors.

Underneath all of that sits the data layer. Integrated, normalized data, which is accessible across HR, cyber, physical, and operational systems, is the connective tissue that lets AI, analytics, and decision-making actually deliver value. Without it, more tools just generate more noise.

And finally, it takes disciplined technology investment and cross-functional alignment. The biggest risks today for organizations are insider threats, organized retail crime, supply chain disruption, and crisis response, which all span silos. No single team, system, or vendor solves them alone.

The shift from compliance to business value isn’t about doing more. It’s about doing the right things, together. The organizations getting this right don’t just protect themselves better. They unlock new sources of operational value, customer trust, and competitive advantage.

That’s the bar. The work is figuring out how to clear it.

Let’s chat about how you can integrate security operations into a unified platform.

AI in physical security: Where to start and what actually works

Posted on April 30, 2026 by - Featured, Industry Expertise

The conversation around AI in physical security has shifted. Two years ago, it was hype, pilot programs, and vendor promises. Today, enterprise security teams are deploying AI in production across access control, video surveillance, and incident management, and the gap between early adopters and everyone else is widening fast.

But for many security leaders, the question isn’t whether AI works. It’s where to start, what to realistically expect, and how to avoid the mistakes that derail implementation before it ever gets off the ground.

How are people using AI in physical security today?

AI in physical security is no longer experimental. Modern video analytics can distinguish between a person, a vehicle, and an animal with meaningful accuracy, which is a far cry from the motion-triggered false alarm machines that gave earlier-generation systems a bad reputation.

The market is also moving quickly. Major players in the security software space are embedding AI natively into their technology, which means buyers increasingly get AI as a feature rather than an additional product.

That said, integrating AI is the biggest challenge. Connecting modern AI tools to legacy physical security infrastructure can be tough because those legacy systems weren’t built to “play nice” with other systems. 

One thing hasn’t changed in the last few years: human oversight remains essential. The best implementations today keep humans in the decision loop while AI handles volume and pattern recognition.

What does AI actually do well for physical security?

Across security programs, AI is delivering real, measurable value in four areas:

Automated alert triage is where most teams see the fastest ROI. AI filters false alarms from real threats, prioritizes alarms by risk context, and dramatically reduces the manual review burden on operators (often reducing it by 60 to 80%).

Intelligent video moves beyond single-camera monitoring. Cross-camera object tracking, behavioral anomaly detection, and automatic incident timeline reconstruction give operators and investigators tools that used to require hours of manual footage review.

Device health & maintenance is a great place to gain huge value but is often overlooked by security programs. AI can give you device health in real time and predict failures before they cause coverage gaps. Security operations can get a view of the status of every sensor, camera, and access point, with automatic alerts when devices go offline or degrade. This is something really difficult for humans to monitor manually and it’s a great way to ensure you’re getting the most from integrator contracts.

Where is the best place to start using AI? 

The smartest security teams don’t try to automate everything at once. They start by trying to solve one problem or paint point at a time.

For most organizations, the starting point is to reduce alarm fatigue and manage false alarms. It’s where AI can show measurable results quickly without requiring a full system and technology overhaul.

Once you’ve been able to show success, you can build on that. Start with a second use case, and again measure what actually happened. Share the results, even the ugly ones, as it indicates you’re learning. Through this process, AI will earn the trust of the team. And from there, you can expand further.

A practical 90-day framework looks like this:

  • Spend the first two weeks understanding the types of alarms you’re getting, and how many per month. Use this to decide which type of alarm volume you’ll try to reduce first. 
  • Use weeks three and four to select one focused use case, or alarm to manage with AI, and research AI technologies. Vendors with genuine security domain expertise are recommended. 
  • Deploy AI for the single use case during weeks five thru nine. Determine what your success goals are before go-live. For example if you’re targeting false DHO alarm reduction, set a specific benchmark: A 30% decrease in false alarms. 
  • Spend weeks 10-12 fine tuning the outcomes. Keep track of metrics and present results to leadership.

What success with AI looks like

The financial savings impact AI can offer is compelling. Lower cost-per-incident can be achieved through automation: often SOCs see reduced guard costs, fewer emergency dispatch calls, and operators who are paid to only handle true incidents (not click buttons to resolve false alarms). Many companies are also seeing reduced insurance premiums tied to these improved risk controls. These financial savings are board-reportable outcomes alongside improvements in the usually-tracked metrics like TTR, false alarm rate, system uptime. All of which translate monies spent in security into business language.

The mistakes that derail it

Most AI implementations don’t fail because of the technology. They fail in the execution and use. The most common pitfalls include trying to automate too much too fast and skipping change management with SOC operators. Partnering with IT can make or break the improvements, as AI requires a feedback loop to make sure the AI model is getting better and better at understanding the specifics about your program and your SOPs. And finally, many security leaders misunderstand how complex integration can be; it’s critical to the success, but many vendors do not offer an easy way to integrate across systems like ACS and VMS.

Starting small and showing wins generates trust from leadership, from operators, and from the organization. It’s good program management and you can do it, regardless of your experience with AI.

Ready to learn more about how to begin implementing AI in your security program? Let’s chat. 

HiveWatch 2025 Wrapped: Our Year in Physical Security

Posted on December 23, 2025 by - Company News, Featured

You know that satisfying moment when Spotify tells you exactly how many minutes you spent listening to the same three songs on repeat? Consider this our version of that.

Our Top Number: $65 Million

We closed a $33 million Series B funding round in September, led by Anthos Capital. Total funding raised? More than $65 million.

Our CEO, Ryan Schonfeld, is calling this “Phase 2: Scale.” Translation: we’re hiring across engineering, product, customer success, marketing, and sales in El Segundo. Things are about to get loud.

Most Played Feature: The AI Operator

If 2025 had a top track, it was the AI Operator.

Powered by Anthropic, this is the feature that handles alarm triage at scale so human operators can stop drowning in false positives. It launched to customers early this year and the response was… a lot.

90%+ of new customer contracts now include it.

One CSO from a $300 billion tech company said their team is shifting “from alarm processors to strategic analysts.” That’s the energy we’re going for.

Your Listening Personality: Security That Actually Scales

Remember when we kept saying security doesn’t scale? We meant it. And then we fixed it.

The AI Operator processes high volumes of initial alarms so your team focuses on real threats. Not noise. Not busywork. Actual security incidents that need human judgment.

New Releases This Year

We dropped some features based on what you actually asked for.

QuickReport: Employees report incidents from their phones via QR code. Photos, video, descriptions, and anonymous options. Operators filter by location, time, and type. Simple.

Guard Post Orders: Site-specific instructions now live directly in the Guard Mobile App. Field teams responding to incidents have what they need without digging for it.

Dashboard Upgrades: More customization, better filtering, streamlined workflows. The HiveWatch® GSOC OS now bends to how your team actually works.

The Stat That Still Gets Us

95% of Americans have interacted with a product, service, or brand protected by HiveWatch.

We don’t have a clever way to say this. That’s just wild. And we don’t take it lightly.

Your 2026 Preview

More AI Operator capabilities. More platform updates. And a continued push toward what we’re calling the “GSOC of the future.”

Physical security is having a moment. The teams that figure out how to pair human expertise with AI-powered tools are the ones who’ll actually scale protection across their entire footprint.

That’s the playlist we’re building.

Thanks for Streaming With Us

Seriously. Thanks for being part of this year. If you want to see any of these features in action, connect with our team. We’ll walk you through all of it.

See you in 2026.

‘Start Using AI’ – What That Actually Means for Security Teams

Posted on December 9, 2025 by - Featured, Industry Expertise

The meeting that changed nothing

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.

What does “start using AI” actually mean in physical security?

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:

Automating repetitive security tasks

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.

Improving threat detection and response times

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).

Making better use of existing data

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?

Connecting siloed security systems

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.

AI video analytics and real-time object detection

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.

Intelligent alarm management: reducing false alarms by more than 90%

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%.)

Predictive maintenance for security hardware

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.

Access control pattern recognition and anomaly detection

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.

Incident report analysis and automated trend identification

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.

How to assess your current security operations for AI readiness

Before you start shopping for AI solutions, you need to get honest about where you are. Here’s a practical self-assessment framework:

What manual processes are consuming your team’s time?

Be specific. “Reviewing footage” isn’t specific enough. Is it:

  • Searching for a specific person across 50 cameras?
  • Verifying alarm activations manually?
  • Creating incident reports from scratch?
  • Correlating events across multiple systems?

Track how much time these tasks actually take. You need baseline metrics to prove ROI later.

Where do you have data you’re not using?

You’re probably collecting way more data than you’re analyzing:

  • Access logs
  • Alarm patterns and timestamps
  • Environmental sensors
  • Badge swipe histories
  • Camera health diagnostics

What’s just sitting there? What insights are you missing?

What alerts or incidents are you missing or catching too late?

This is hard to answer because by definition, you don’t know what you’re missing. But you can look at:

  • Near-misses or incidents caught by chance
  • Situations where “if only we had known sooner”
  • Threats identified through investigation rather than real-time detection
Where are your systems not talking to each other?

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.

Data security and privacy considerations you can’t ignore

Here’s the catch: you need to think about data security from day one. Questions to answer:

  • Where is your AI processing happening? On-device? Cloud? Hybrid?
  • What data are you feeding it, and who has access to that data?
  • How long are you retaining video and biometric data?
  • What’s your data breach response plan?

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.

How to start small with AI in security operations (without looking like you’re doing nothing)

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:

Begin with one high-impact use case

Pick something that’s:

  • Painful (it bothers everyone)
  • Measurable (you can track before/after metrics)
  • Clear (success looks obvious)

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.

Pilot with your most time-consuming manual process

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.”

Focus on measurable outcomes, not technology buzzwords

Not “we’re using AI” but:

  • “We reduced false positives by 85%”
  • “We cut investigation time from 4 hours to 30 minutes”
  • “We caught 3 incidents we would have missed”
  • “We reallocated 20 hours per week from alarm verification to strategic analysis”
Build internal advocates before scaling

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.”

Critical questions to ask AI security vendors (so you don’t get sold vaporware)

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:

Essential questions for every AI security vendor

“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:

  • Edge processing: Lower latency, better privacy, limited by device compute power
  • Cloud processing: More powerful analysis, requires bandwidth, raises data sovereignty questions
  • Hybrid: Best of both, but more complex to implement

“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:

  • Timeline (be suspicious of “instant deployment”)
  • Resources needed (IT staff, network changes, hardware requirements)
  • Dependencies (what needs to be in place first)
  • Training requirements for your team

Red flags that indicate poor solutions

  • “AI” that’s just basic rules-based analytics (if-then statements aren’t AI)
  • Solutions that require replacing your entire infrastructure
  • Vendors who can’t clearly explain what the AI is actually doing
  • Promises of 100% accuracy (it doesn’t exist in the real world)
  • No clear path to integration with your existing systems
  • No customer references willing to talk specifics
  • Reluctance to do a paid pilot before full commitment

Integration requirements to clarify upfront

Get documentation on:

  • Video format requirements (H.264, H.265, resolution requirements)
  • Metadata needs (timestamps, GPS, sensor data)
  • Camera brand compatibility (works with your existing hardware?)
  • Network bandwidth requirements (especially for cloud processing)
  • On-premise vs. cloud processing options
  • API availability for custom integrations
  • VMS compatibility and plugin availability

ROI metrics that actually matter

Measure what matters:

Time savings:

  • Hours saved per analyst per day
  • Reduction in average investigation time
  • Decrease in alarm verification time

Detection improvements:

  • False positive reduction percentage (with before/after baseline)
  • Incidents caught that would have been missed
  • Response time improvement (measured in minutes/seconds)

Cost impacts:

  • Operational cost reduction (fewer false alarm fees, staff reallocation)
  • Equipment utilization improvement
  • Overtime reduction

Not “better security” or “improved awareness” – those are outcomes, but they’re too vague to measure and too easy to manipulate.

Turning “start using AI” from directive to direction

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.

Your first step: Pick one problem AI could solve this quarter

Not this year. This quarter.

Make it specific:

  • Don’t: “Improve our security posture”
  • Do: “Reduce false motion alarms in our warehouse by 75%”

Make it measurable:

  • Don’t: “Better threat detection”
  • Do: “Cut incident investigation time from 3 hours to 45 minutes”

Make it matter to your team’s daily work:

  • Don’t: “Deploy cutting-edge AI”
  • Do: “Eliminate the 2 hours daily spent manually reviewing overnight footage”

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.