1) Can AI really monitor CCTV feeds today?

Yes. Modern video analytics detect people, vehicles, behaviours (e.g., loitering, crowding), number plates, and safety breaches in real time. These systems flag anomalies and push alerts to operators or trigger responses in other systems (access control, alarms). Accuracy still depends on camera quality, angles and lighting, but the capability is live and widely deployed.

2) Will it work with the cameras and VMS I already have?

If you use entrustIT's Cloud CCTV product, the answer is yes. However, this is typically true for most cloud CCTV vendors these days. Look for ONVIF support, especially Profile M for analytics metadata; it defines how to configure analytics and stream events such as people/vehicle detections, plates, faces and geolocation. This makes multi-vendor deployments far less painful.

3) How accurate is it—and how do I measure that?

Face recognition benchmarks from NIST show continued year-on-year gains, but real-world performance depends on your scenes and use cases. For operations teams, measure: false alarm rate, missed detection rate, mean time to detect (MTTD) and mean time to respond (MTTR)—per site and per camera class. Validate with a structured pilot and ground-truth samples before scaling. 

4) Can AI replace operators or manned guarding?

In the future - yes. Dean Drako, CEO of Eagle Eye Networks, a global leader in AI Video Management Systems, predicts that in the future all cameras will be monitored by AI. For now, though, treat AI as force-multiplication: it watches everything, all the time; humans make contextual decisions, engage staff, and close incidents. This is the prevailing direction of travel in professional guidance and market practice.

5) What business intelligence can I get from CCTV beyond security?

Plenty—without new sensors. Typical wins for multi-site retail and hospitality:

• Footfall by site/daypart

• Dwell and hotspots for merchandising and layout

• Paths and conversion-adjacent metrics (with POS or traffic counters)

• Queue length & wait time for staffing and service SLAs

These are mainstream, not experimental. 

6) Can AI trigger actions automatically?

Yes. You can set policies to trigger local sounders, lock/unlock doors, notify roving guards, or escalate to a central SOC when risk thresholds are met (e.g., crowding, suspected concealment, perimeter breach). This is routine in modern deployments. 

7) What are the UK legal and privacy basics I must get right?

• Lawful basis: Most business CCTV relies on Legitimate Interests—but you must document it and balance it against individuals’ rights. (Source: ICO)

• DPIA: Do a Data Protection Impact Assessment before deploying or materially changing surveillance—especially with new analytics. (Source: HMGov)

• Transparency & signage: Provide clear notices and contact details; only capture what’s necessary. (Source: ICO)

• Governance: ICO’s CCTV guidance (currently being updated following the Data (Use and Access) Act 2025) remains the starting point. (Source: ICO)

• Public-space principles: The Home Office Surveillance Camera Code of Practice sets good-practice expectations for proportionate, accountable use. (Source: HMGov)

8) What about facial recognition—can we use it?

Treat it as high-risk. UK regulators are explicit: facial recognition is personal/biometric data and must be lawful, fair and proportionate, with strong justification and safeguards. Most private sector use cases struggle to meet that bar without tight scoping and necessity tests. If you explore it, use an ethical framework (e.g., BSIA guidance) and complete a rigorous DPIA.

9) How do we quantify ROI for AI video?

Typical value buckets:

• Safer stores (fewer assaults/accidents via early alerts).

• Reducing "Sweethearting" - see our insight on this for more info

• Operational uplift (better staffing from queue/footfall insights).

• Capex protection (analytics added to existing cameras, not wholesale replacement).

10) What does “good” look like across multiple sites?

• Standardise on camera classes, mounting heights and FoVs; adopt ONVIF profiles.

• Tier sites (A/B/C) by risk and revenue to prioritise analytics and monitoring coverage.

• KPIs: false alarms per camera per day, MTTD/MTTR, verified incident rate, and BI metrics (footfall/queue).

• Governance: annual DPIA review; retention schedules by use case; audit access; change-control for models.

11) How do we spec new projects without buying the wrong kit?

• Start with problems, not features: theft patterns, assault hotspots, safety breaches, queues.

• Pilot 4–6 cameras per use case; capture ground-truth; tune thresholds.

• Demand evidence: vendor confusion-matrix on your footage.

• Architect for hybrid (edge + cloud), and insist on open standards and exportable metadata.

12) Any pitfalls to avoid?

• Over-promising on facial recognition outside narrow, well-justified contexts.

• Ignoring privacy by design (no DPIA, poor signage, over-broad capture). 

• Under-spec’d hardware pretending to be “AI-ready”—often fine for motion detection, not for advanced analytics. 

13) What’s next?

If you're interested in learning more about entrustIT's Cloud CCTV service, powered by AI - read about it here