Weighing the Trade-Offs While Staying on Course
What AI Actually Changes
Video surveillance has quietly gone through one of the biggest shifts in its history. For decades, a camera was a recording device: it captured footage, stored it, and waited for a human to either watch it live or retrieve it after something went wrong. That model is disappearing. By 2025, nearly 80 percent of shipped security cameras included some form of analytics, now known as AI. Roughly two-thirds of those relied on deep learning rather than the older rules-based motion detection that dominated the industry for years. Cameras are no longer just eyes; they are becoming interpreters, and that shift is worth examining—both where it delivers real value and where it still falls short.
The clearest win is the collapse of false alarms. Traditional intrusion and motion-based systems have lived with high nuisance-alarm rates for fifty years, largely because they cannot distinguish a wind-blown trash bag from an intruder. AI-driven analytics, paired with remote video monitoring, have pushed false alarm rates down dramatically in fielded deployments, with some monitoring providers reporting escalated false alarms reduced by more than half. For anyone who has staffed a monitoring center or fielded a 2 a.m. dispatch call over a raccoon, that is not a marginal improvement. It changes staffing models, guard-hour allocation, and how seriously a customer takes an alert. Here at TriCorps, in the month of September alone, we received 11,788 AI-configured alarms; however, only 8,061 were escalated to an operator, resulting in a 93 percent reduction in traffic requiring operator review.
The second shift is from reactive to proactive. Object classification, behavioral anomaly detection, license plate recognition, and now visual gun detection allow a system to flag a problem as it develops rather than after the fact. Visual weapon detection has become a serious differentiator in K-12 schools, healthcare facilities, and public venues because it covers a wider field of view than acoustic gunshot sensors and can, in principle, surface a threat before the first shot. Natural-language searches across archived footage—such as, “Show me every red pickup that entered the north lot after 9 p.m. this week”—are also becoming standard rather than a novelty. It is the kind of feature that can turn hours of forensic review into minutes.
Where the Cracks Show
None of this comes without real costs and risks, and it is worth being direct about both.
Accuracy is not uniform across populations or conditions. Facial recognition systems, in particular, have documented histories of higher error rates for people with darker skin tones, and the consequences are not hypothetical. A wrongful arrest in Michigan in 2020 traced directly to a facial recognition misidentification is the case most often cited, and it serves as a useful reminder that a false positive in this domain is not just an inconvenience; it can cost someone their liberty. Any organization deploying identity-matching analytics needs to treat accuracy claims skeptically until they are validated against the actual population the system will encounter, not just a vendor’s data.
Privacy and governance have not caught up with capability. Cameras increasingly capture people in genuinely vulnerable moments—medical emergencies, domestic disputes, and activities on private property—and when that footage feeds into always-on analytics or is shared with third-party platforms, questions about who can see it, for how long, and under what authority often lag well behind the technical deployment. Multi-camera facial recognition networks operating without public disclosure have already generated real controversy in at least one major city, and that is the kind of story that erodes public trust in the entire industry, not just the operator involved.
Then there is the operational and cybersecurity layer that rarely makes it into the sales pitch. The overwhelming majority of cyberattack attempts target known, unpatched vulnerabilities, which means every AI-enabled camera added to a network is another endpoint that requires firmware management and lifecycle planning. Integrators who treat analytics cameras as “install and forget” hardware are building liability into every deployment. Cloud dependency is also increasingly the default because the model sizes needed for high-quality inference are impractical to run at the edge on much of the existing camera hardware. This introduces bandwidth, latency, and vendor lock-in considerations that were not part of the conversation five years ago.
Finally, there is an operational hesitation that is easy to underestimate: interfaces that surface too much data, alert fatigue from poorly tuned models, and operators who do not trust a system they do not understand. A monitoring center flooded with “events” it cannot act on is arguably worse off than one running simple motion detection because it trains people to ignore the screen.
Why the Answer Is Still to Push Forward
None of these problems are arguments for slowing down. They are arguments for deploying deliberately.
The false-alarm reduction alone justifies continued investment. An industry that has lived with nuisance dispatch rates for half a century now has a credible path toward near-zero false alerts, and that changes the economics of monitoring at scale. The shift from forensic review to real-time prevention is the difference between explaining what happened and stopping it while it is happening, which is the entire point of a security program.
The bias and accuracy failures that receive attention are, in almost every documented case, failures of governance and validation, not proof that the underlying technology is unfixable. Accuracy thresholds, mandatory audits, human decision-making, and retention limits are all known, implementable controls. These analyses are not calling for abandoning the technology; they are calling for the same kind of professional discipline the industry already applies to access control credentialing or alarm-response verification. Treating governance as a prerequisite for deployment, rather than an afterthought, is how this technology earns the trust it needs to keep expanding.
Competitively, camera manufacturers, video management system providers, and monitoring platforms are converging on AI as the baseline expectation, not a premium add-on. An integrator or end user who waits for the technology to become “perfect” before adopting it will simply be selling or buying yesterday’s product while the false-alarm rates, labor costs, and response times of AI-native systems continue improving around them.
The right approach is aggressive adoption paired with real accountability: validate accuracy, secure the endpoints, write policies before the cameras go live, and keep a human in the decision loop wherever the stakes are high. That combination is what turns a genuinely powerful tool into a trustworthy one, and it is why continuing to push AI forward in camera analytics is the right call—not a risk to be managed away.
