Retail security tech that only counts catches quietly erodes shopper trust

Retail security technology usually gets judged on what it catches: the theft flagged, the incident recorded, the loss prevented. One security integrator who works on AI-enabled store systems says that view leaves something out. A system can rack up detections and still hurt the business it's supposed to protect. The cost he points to is the honest shopper who ends up treated like a suspect.

Mitchell's alternative is a broader scorecard: safety, customer confidence and how well the store runs. Getting there, in his account, takes decisions before deployment that most retailers either make late or skip. Define what customers expect. Answer their privacy questions with real controls behind the answers. Bring more departments into planning. Test systems against how people actually shop. His message for loss prevention, IT and operations leaders: a tool tuned only for catches can slowly wear down the trust that brings customers back.

Security decisions should follow what the shopper came for

Mitchell starts from this premise: there's no single right level of security friction. It depends on what the customer expects from a given store. In luxury and beauty, he says, the experience is a big part of why people make the trip. "People want personal attention," Mitchell said. They want room to explore, and "they wanna feel welcome."

Grocery, he says, is a different case. Shoppers there want "a safe, convenient trip" and want "to get through checkout without being unnecessarily hassled." The beauty customer values lingering and attention. The grocery customer values speed and being left alone. A deployment that works in one format could be intrusive or useless in the other.

That leads to his central requirement. Security, and the decisions the system makes, "needs to support those experiences." In practice, the expected customer journey becomes a design input. It isn't something to think about after the cameras and analytics have been picked. The rest of his argument builds on this.

Fair questions need accurate answers and real controls

Once cameras and analytics are visible in a store, Mitchell says retailers "should expect questions," and he calls them fair. These are the questions he expects shoppers to ask:

• What personal information is being captured or collected?
• Who can see it?
• What is it used for within the retailer?
• Does the data stay with the retailer?
• Can outside parties access it?
• How long is it kept?

He sees the integrator's job as helping the retailer answer these "accurately, succinctly," and that includes what the technology provider itself can do with the data. That last part matters. When a vendor sits between the shopper and the data, the retailer's answer depends on terms the retailer may not have spelled out yet.

He also argues this is "not just a communication thing." A clean privacy statement is worth little if the system behind it has no limits. He wants "real thought" put into collection and access, "reasonable limits" on who can see the data, and "solid controls tied to it." Put simply, what a retailer tells customers should describe how the system really works, not how the retailer wishes it sounded.

Why the planning table needs more than AP and IT

Asset protection and IT are usually in the room when retailers evaluate this technology, Mitchell says. He doesn't think that's enough. He wants input from across business groups, and the earlier the better. His list covers operations, legal and privacy, cybersecurity, customer experience and internal communications. He adds HR, especially when a new system could affect employees.

He gives store leadership extra weight, because store leaders know how the floor runs day to day. In beauty, they understand consultations, testers and how customers work with advisors. In grocery, they understand checkout, pickup and "what the team can realistically handle" during a rush. A central project team may not have that knowledge, and it decides whether anyone can act on the alerts at all.

Mitchell also recommends bringing in whoever owns customer data, such as a beauty client profile or a grocery rewards account, so the retailer can set "clear boundaries around whether security data connects to them." That call can change what the system knows about a person. Link security footage or alerts to a loyalty identity and you've changed what the system is. In regulated settings like pharmacies, he adds, the people who own those privacy requirements need to be involved too.

He describes Telaid's role as explaining the actual setup: camera coverage, access, what gets shared, retention and how the system stays secure, all tied to the use cases it's meant for. He also wants the response process settled in advance. Who gets the alerts? How do employees respond safely? Who handles questions, including ones about law enforcement or other outside access? "Store teams are on the front line," he says, and they need those answers before launch.

Start with a problem, then test against real shopping

For evaluation, Mitchell says to start with the problem, not the product. He gives example questions: Are investigations taking too long? Is the store missing safety issues? From there, the retailer defines what a better result looks like and what value, "soft or hard," comes from getting it. That keeps the deployment tied to outcomes the business can measure, not to whatever the tool happens to detect.

Testing, he argues, has to match how people actually shop in that retailer's stores. Beauty customers use testers and compare products. Grocery customers handle produce and "consistently move items between carts and bags." A system tuned without that context can turn ordinary behavior into "a stream of suspicious alerts." That burns staff time and raises the odds that an honest customer gets confronted.

He makes the same point about checkout. A missed scan, Mitchell says, "needs review. It doesn't necessarily tell you somebody's intent." An alert records that something happened. It doesn't explain why. Teams that treat alerts as proof of wrongdoing are the ones most likely to create the singled-out customer he warned about at the start.

That distinction may be the most useful filter in his argument. Spotting an unscanned item or a safety hazard is a different capability from recognizing who someone is. If the problem is slow investigations or missed safety incidents, the retailer may not need person-level identification at all. Leaving it out shrinks the list of privacy questions the business has to answer.

Human review comes before action

Mitchell names four things an integrator should help evaluate in any tool: accuracy, whether outcomes are fair, data security around the system, and whether the team "can actually manage the alerts" it produces. The last one goes back to what store leaders know about staffing at busy times. A system that looks accurate on paper but sends more alerts than staff can handle will break down in practice, or push employees into rushed responses.

His firmest position is about who decides. "Human review needs to come before action involving anything from a customer or employee standpoint," he says. The system can surface events. A person should assess them before anyone is approached, questioned or disciplined.

Put together, these steps show what measuring customer confidence looks like in practice. A retailer evaluating AI-enabled security would need to show four things: the system fits the shopping experience, the privacy answers match real controls, the store team knows how to respond, and no customer or employee faces consequences from an unreviewed alert. The catch rate still counts. But by Mitchell's measure, a deployment that fails these tests isn't working well, no matter how much it detects.