AI Video Analytics: What Enterprise Security Leaders Need to Know in 2026

Three years ago, AI video analytics was a technology that enterprise security leaders were watching with interest but deploying with caution. The capabilities were promising, the use cases were compelling, and the vendors were numerous — but the maturity of the technology and the clarity of the regulatory environment left many organizations in a wait-and-see posture.

That posture is no longer sustainable. AI video analytics has crossed the threshold from emerging technology to mainstream enterprise security tool. Organizations that have not yet developed a clear position on how they will use — and govern — AI video capabilities are falling behind.

This article provides a current-state assessment of AI video analytics for enterprise security leaders: what the technology can actually do, where it still falls short, and what organizations need to consider before deployment.

The term "AI video analytics" covers a wide range of capabilities. Understanding the distinctions matters for setting realistic expectations and selecting the right tools for specific use cases.

Modern AI video systems can reliably detect and classify objects — people, vehicles, packages, equipment — in real time across multiple camera feeds simultaneously. This capability underpins most other analytics functions and has reached a level of maturity where it performs reliably in production environments.

Practical applications: Perimeter intrusion detection, parking management, occupancy monitoring, package detection at loading docks.

Beyond detecting objects, AI systems can analyze behaviour — identifying patterns that may indicate a security concern. Loitering detection, crowd formation, object abandonment, and direction-of-travel analysis are all established capabilities.

Practical applications: Retail loss prevention, campus security, transportation hub monitoring.

Rather than looking for specific predefined behaviours, anomaly detection systems learn what "normal" looks like in a given environment and flag deviations. This is particularly valuable in environments where the threat profile is not well-defined.

Practical applications: Critical infrastructure protection, data centre security, after-hours monitoring.

Automated licence plate recognition has been a mature technology for years, but AI has significantly improved accuracy in challenging conditions — low light, partial occlusion, high vehicle speeds. Modern LPR systems integrated with access control can automate vehicle access management at scale.

Practical applications: Parking management, gate access control, fleet management.

Facial recognition remains the most capable and the most controversial AI video capability. The technology has improved significantly in accuracy and performance across diverse populations, but the regulatory and ethical landscape is complex and evolving.

Tawy's position: We deploy facial recognition only in applications where it is clearly appropriate, legally compliant, and subject to robust governance. We do not deploy it for general surveillance or in contexts where individuals have not been informed of its use.

Honest assessment of AI video analytics requires acknowledging its limitations:

False positive rates: Even the best AI video systems generate false positives. In high-traffic environments, a 1% false positive rate can generate hundreds of alerts per day. Effective deployment requires alert management workflows that prevent alert fatigue from undermining the system's value.

Edge cases and novel situations: AI systems trained on historical data can struggle with situations they have not encountered before. A new type of vehicle, an unusual weather condition, or a novel behaviour pattern may not be handled correctly.

Lighting and environmental conditions: Performance degrades in challenging lighting conditions — very low light, direct sunlight, fog, and rain all affect accuracy. Camera selection and placement are critical to managing these limitations.

Privacy and bias concerns: AI video systems can reflect biases present in their training data. Organizations deploying these systems have an obligation to evaluate and monitor for bias, particularly in applications that affect access decisions.

The most powerful enterprise security deployments integrate AI video analytics with access control systems — creating a unified security platform where video intelligence can trigger access control responses and access events can be correlated with video evidence. Tawy designs integrated platforms that treat video and access as complementary data sources rather than separate systems.

AI video systems generate large volumes of data. Organizations need clear policies on data retention, access controls for video footage, and procedures for responding to access requests and legal holds.

In Canada, AI video deployments must comply with PIPEDA and applicable provincial privacy legislation. Organizations deploying facial recognition or other biometric capabilities face additional obligations. Tawy works with clients to ensure deployments are designed for compliance from the outset.

The AI video analytics market includes dozens of vendors with varying levels of maturity, accuracy, and support capability. Tawy evaluates and integrates solutions from a curated set of vendors whose technology has been validated in enterprise environments.

AI video analytics is no longer a technology to watch — it is a technology to deploy, thoughtfully and with appropriate governance. Organizations that get this right will have a meaningful security advantage. Those that deploy without adequate planning will find themselves managing a system that generates noise rather than intelligence.

Tawy Systems helps enterprise organizations design, deploy, and govern AI video analytics programs that deliver real security value. If you are evaluating AI video capabilities for your environment, we would welcome the conversation.