Tag: Physical Security

Why physical security teams struggle with multiple systems in SOC management platforms

Posted on August 4, 2026 by - Featured, Industry Expertise

Picture your security operations center (SOC).

Your SOC gets an incoming alarm through your access control platform. An operator receives it and switches to the VMS to pull video, navigating to the right cameras and time stamp. Then flips back to the access control system to check the badge log. Then opens a third platform to log the incident and check SOPs for a phone number. Then a fourth to notify the on-call guard. Then a spreadsheet to track response time.

By the time the full picture is assembled, several minutes have passed. In security operations, where moments matter, this can be an eternity. And when alerts are coming in that are false, consuming an operator’s time, these critical incidents might even get missed among the noise (which is a real problem).

This is the reality for most physical security teams today; and it’s not because they haven’t invested in good technology, but because that technology was never designed to work in combination with one another.

Why are physical security teams managing so many disparate systems?

The average SOC operator uses 4.7 different applications in a single shift. At organizations with more than 10 locations, that climbs to 5.6. That’s according to our 2026 State of Physical Security Operations benchmark study, which surveyed 300 security professionals across mid-to-large U.S. enterprises.

Every time an operator switches applications, they are trying to get more context on the security incident. They manually reconstruct timelines that should be automatically compiled for them. They copy data between tools (a task that 19% of respondents said they’d eliminate first). The work of connecting the dots falls on the operators, not the systems.

Here’s the uncomfortable truth: most physical security stacks weren’t built. They accumulated.

An organization deploys access control from one vendor. Cameras from another. They add a visitor management system. A guard tour platform. An incident reporting tool. Each of these decisions made sense in isolation. Each vendor had the best product in its category at the time.

But nobody asked how the data would flow between them.

Security operations management didn’t create this problem, but they are tasked with managing it. 

Why disparate systems are so hard to connect

Legacy physical security systems weren’t designed for integration with other products. They were designed to do one thing well: control access to a door, record video, identify water intrusion. The idea that all of this data might need to live in one place, talk to each other in real time, and surface actionable intelligence for an operator was, frankly, an afterthought.

Proprietary products that don’t “play well with others” (i.e. integrate easily) can mean that operators are forced to tab back and forth between multiple systems without being able to gain a full view of what’s happening. 

The result is what security teams deal with every day: systems that technically work but operationally don’t. Data that exists but isn’t connected. Insights that are possible in theory but impossible to extract in practice without a team of people manually bridging the gap.

The disparate systems problem is bigger than it looks

As organizations grow, the problem compounds fast.

HiveWatch’s research found that as an organization roughly doubles in headcount, the number of connected devices it monitors more than quadruples: from an average of 447 devices to 1,798. Daily alarm volume climbs from 293 to 421+. And the false alarm rate jumps from 29% to 44%.

The cost of this can skyrocket if not addressed. For example, the average hourly rate for an operator is between $19 and $25 per hour. Spending 50% of their time can mean losing $19k to $26k a year on looking at things that don’t matter. Multiply by multiple people per shift and you have quite the resource drain on your hands. 

That means larger organizations are processing over 100 false alarms per day before finding a single real incident. Operators are managing thousands of devices, sifting through hundreds of alarms, across multiple platforms, and expected to not miss anything.

The tools haven’t kept pace with the growth. The humans are absorbing the difference.

This is how false alarms become a massive problem for security operations. It’s not just about the cost of chasing bad alerts – though that’s real, with industrywide false alarms costing the U.S. emergency response system an estimated $3.1 billion annually. It’s about what false alarms prevent: the ability of operators to respond quickly and confidently to actual problems.

When your GSOC is juggling five screens to triage and respond to a single alarm, a real incident is waiting in the queue . The real incident doesn’t announce itself as real, you have to wade through many to find it.

Why finding the right technology that brings systems together is tough

What a modern SOC needs is a true operational layer: something that doesn’t just collect data from disparate systems, but normalizes it, connects it in the right way, and surfaces it in a single place that an operator can easily see and act on.

That’s a fundamental engineering challenge that requires deep knowledge of all of the systems and platforms. The challenge can be amplified by vendors that aren’t truly open or willing to integrate with other systems. Solving this challenge requires that someone have the ability to handle different data schemas, alarm or alert structures, and formats across vendors that have been in the market for decades. 

It requires, frankly, a lot of unglamorous work that most companies find hard to fix. Most physcial security teams don’t have IT resources dedicated to help optimize their tools, and contracting with the people who deployed the systems can be cost prohibitive.

The promise of “unified security management” has been made many times. Usually what gets delivered is a dashboard that aggregates information but doesn’t actually allow operators to see everything they need to see and ACT within a single platform. Operators still have to context-switch. They’re just doing it in slightly fewer tabs. 

There’s also the loss of critical data being collected from these systems, where it can help paint the picture of a security program and how it works from day to day. 

The 56% of security programs stuck at maturity Level 2 or Level 3 in our benchmark research, where processes exist but aren’t consistently followed and technology is deployed but not optimized, aren’t stuck there because they haven’t tried. They’re stuck because they’ve paid for systems that look connected on paper, but fall apart when things get busy. 

There’s a better way to run this

You bought good physical security tools. The problem isn’t the tools. The problem is that they aren’t connected.

You don’t need to replace your ACS, your VMS, or your guard management platform. HiveWatch sits on top of the systems you already have: your cameras, access control, alarm systems, and pulls everything into one screen. Instead of jumping between six tools, an operator sees a single feed of what’s happening right now, already sorted by what matters most. When a door forced-open alarm comes in, they don’t just get the alert, they get the nearby camera footage, who badged in last, and how to respond for that site, all in one place. Routine false alarms get resolved automatically before anyone has to look at them, so the alerts that reach a human are the ones that actually need one.

One incident queue. No more tab switching.

If your security operations team is still stitching together a picture of what happened from five different screens at 2 a.m., the problem isn’t their effort. 

It’s a problem that can be easily solved.

Want to see how leading security programs have unified their systems and improved  operations? See the HiveWatch platform →

Or download the full 2026 State of Physical Security Operations benchmark study to see how your program stacks up.

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. 

How to Talk Threat Intel to Your C-Suite (Without Losing Them)

Posted on December 16, 2025 by - Industry Expertise

Security leaders know the drill: You’ve got critical threat data, limited resources, and an executive team that needs to understand why it all matters fast. But bridging the gap between operational security and boardroom priorities? That’s where things get tricky.

We recently sat down with Ryan Schonfeld (HiveWatch), Cory Siskind (Base Operations), and Bill Schieder (Labcorp) to talk about what actually works when you’re trying to get executive buy-in for security initiatives.

Start with the 10K, Not the Product Demo

Billr’s advice is refreshingly simple: Figure out your problem before you go shopping for solutions.

“Look at your company’s 10K report,” he said. “Identify the risks that your security organization can mitigate, and use that as the foundation for building your business cases.”

That 10K isn’t just a compliance document; it’s a cheat sheet for what your leadership already considers material risks. When your security pitch ties directly to those documented concerns, you’re not asking executives to care about something new. You’re showing them you can help with something they’re already worried about.

Security as a Business Facilitator (Not Just a Line Item)

Cory pushed back on the idea that security is purely a cost center. Her take: Security is a business facilitator.

Think about what good threat intelligence actually enables: optimized supply chain routes, smarter due diligence on acquisitions, and better decisions about where to deploy your workforce. That’s not just risk mitigation. That’s a competitive advantage.

Bill added the concept of security as a “revenue preserver,” and shared a story from his time at Flexport. By getting TAPA Level A certifications for their warehouses, they unlocked an entire tier of high-value clients they couldn’t previously pursue. Security investment became revenue growth.

The Data Problem Has Flipped

Bill put it simply: “When I first started in global security in 2008-09, our challenge was getting information. Now we have to decipher between what’s intelligence and what’s noise.”

The answer isn’t more data. It’s the right data, presented in ways executives can act on: visualizations, baselines that let you spot real changes versus normal fluctuation, or trend analysis that tells you whether an incident is a one-off or part of a pattern.

Cory emphasized granularity, as city-wide crime stats don’t tell you much about the specific blocks where your people actually work. “When you take a blanket approach to an entire city or region, you’re missing out on opportunities and failing to properly assess risk at the locations where you actually operate.”

Guard Force: The Obvious Place to Start

Ryan pointed to guard deployment as immediate low-hanging fruit. It’s usually the biggest security expense, but deployment decisions are rarely based on actual risk data. Most organizations default to uniform coverage; every site gets the same, regardless of whether it needs it.

Data changes that. You’re not necessarily spending more. You’re putting resources where they actually matter.

Beyond “Nothing Bad Happened”

The hardest part of security leadership might be proving value when your job is preventing things from happening. The panel offered some concrete alternatives: supply chain disruptions caught early, reduction in false alarms, time saved through automation, and business opportunities unlocked by certifications.

Bill’s vision for AI is practical; not replacing analysts, but giving them leverage. “Can we have AI take all the geospatial analytics data from our locations globally and give me a daily intel report in 10 minutes that would take an analyst half a day to put together?”

Building Executive Trust

Bill was direct about what it takes: “You can’t just come in with buzzwords. You have to have business cases and real-life solutions. It doesn’t take long for leadership to figure out if you have business acumen and can be a viable business partner.”

Fear-mongering doesn’t build lasting credibility. Consistent, quantifiable wins do.

Missed the live session? Watch the full recording here. And if you want to see how HiveWatch helps security teams turn threat intelligence into executive-ready insights, request a demo.

What Happens When Physical Security Systems Get Hacked?

Posted on October 14, 2025 by - Industry Expertise

Most people think about hackers going after credit cards or customer databases. But your access control system? Your camera network? Those are targets too. And when they get compromised, the consequences look different than a typical data breach; they’re often worse.

The short answer: Attackers can unlock doors remotely, disable cameras during break-ins, or use your security infrastructure as a backdoor into your corporate network. Physical security systems are increasingly IP-connected, which means they carry the same vulnerabilities as any other networked device, except they control access to your actual buildings.

Why Would Anyone Hack a Door Lock?

Because it’s easier than you think, and the payoff is real.

Remember the casino that got breached through a connected fish tank thermometer? Once attackers were inside the network, they moved laterally until they found what they wanted. Physical security devices work the same way. An IP camera with default credentials or an access control system running outdated firmware becomes the entry point.

The thing is, most organizations treat physical security systems like appliances. You install them, they work, and then you forget about them. Meanwhile, your IT team is patching servers and rotating credentials monthly. That disconnect is exactly what attackers count on.

What Can Actually Go Wrong

Camera Systems

Video management systems run on networks, often with remote access enabled for monitoring. When these get compromised, attackers can manipulate footage, disable recording, or simply watch your operations in real-time to plan their next move. The problem? Most VMS platforms aren’t monitored the same way your servers are. No one’s checking system logs daily or running vulnerability scans on the camera network.

Access Control

Once someone gains access to your access control platform, they can create credentials, modify access rights, or pull reports showing movement patterns throughout your facility. The system logs everything as legitimate activity because technically, it is – just initiated by the wrong person. Some systems integrate directly with HR databases for automatic provisioning, which means a compromise isn’t limited to just doors opening.

The Network Pivot

This is the one IT teams actually worry about. Your physical security devices are connected to your network. Sometimes, on the same network as everything else because segmentation is expensive and nobody budgeted for it when the system was installed.

Attackers don’t always care about your cameras. They care that your cameras are an easy way into your network. One compromised device with weak credentials becomes the foothold for lateral movement. From there, it’s a straight shot to servers, databases, or anything else connected.

The Part Nobody Talks About: Insider Threats

External hackers are one problem. Insider threats such as disgruntled employees or contractors with system access, are another.

Someone with admin rights to your access control platform can do significant damage before anyone notices. They can export databases. They can create phantom credentials. They can pull detailed reports on executive movements.

Most organizations audit their cybersecurity privileges regularly. How often are you auditing who has admin rights to your physical security systems?

What Actually Needs to Happen

Here’s where things get uncomfortable: fixing this requires physical and cyber security teams to work together, and most organizations aren’t structured for that.

Start with the basics:

  • Treat physical security systems like IT assets. Patch them. Update firmware. Rotate credentials.
  • Segment your networks. Cameras and card readers shouldn’t be on the same network as your accounting system.
  • Monitor your physical security infrastructure the same way you monitor servers. Log analysis, alert on anomalies, and investigate configuration changes.

Then get serious about convergence:

Your security operations center (SOC) team needs visibility into your physical security systems. Not just “the alarm went off,” but actual system health, failed login attempts, and configuration changes. When someone attempts unauthorized access to your VMS, it should trigger an alert just like suspicious network activity does.

This is why HiveWatch built the GSOC OS with SOC 2 compliance and network security as core requirements, not afterthoughts. Physical security platforms need to meet the same standards as any other enterprise software, including regular penetration testing, encrypted data transmission, role-based access control, and the whole package.

Why This Matters

Physical security systems getting hacked isn’t some distant, theoretical risk. It’s happening, and it’s usually the result of treating these systems differently than you’d treat any other part of your infrastructure.

The fix isn’t complicated, but it does require acknowledging that physical and cyber security aren’t separate anymore. They’re two sides of the same problem. The organizations that figure this out early are going to be in much better shape than the ones still treating their access control system like a box on the wall.

Want to see how your physical security infrastructure stacks up from a cybersecurity perspective?Request a demo to see how unified security operations actually work.

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