Problem
Retail security teams cannot watch every camera in every store. Shrink, loitering, and unsafe incidents are often discovered after the fact—when evidence is hard to find and losses are already booked.
Business impact
Delayed discovery increases investigation time, weakens recovery, and leaves multi-store operators without a consistent security operating model. Shrink and incident cost compound quietly across the network.
Current approaches
- Manual CCTV monitoring in a control room
- Periodic spot-checks and after-hours footage review
- Guard tours and store manager walkthroughs
- POS exception reports disconnected from video
Why existing methods fail
- Human attention does not scale to camera count
- Review happens after customers and evidence leave
- Stores use inconsistent rules and alert quality
- Security data rarely connects to operations dashboards
How AI solves it
- Computer vision flags high-risk patterns for human review—not automatic accusations
- Events carry clips, zone context, and timestamps
- The same playbook can run across every store on existing cameras
- Leaders get morning briefs instead of footage dumps
With AnomaAI, detections become business events with evidence—so teams review what matters and leaders see outcomes, not camera walls.
Business outcomes
- Faster exception review
- Lower investigation hours
- More consistent multi-store coverage
- Evidence ready for operations and loss prevention
Frequently Asked Questions
Does AI retail security replace guards?
No. It prioritizes what humans should review so guards and LP teams spend time on high-value events.
Do we need new cameras?
Usually no. AnomaAI connects to existing IP/CCTV infrastructure and maps cameras to store zones.
Is every alert a confirmed theft?
No. Alerts are candidates for human review. AnomaAI is designed for explainable evidence—not automated guilt.
Related use cases
Next step
Ready to see this on your cameras? Book a demo or explore Explore Retail AI.
