Retail organizations face an unprecedented obstacle in protecting high-value assets as organized retail crime (ORC) groups and internal theft have expanded in both scope and sophistication. The National Retail Federation's 2025 Impact of Theft & Violence study reveals that retailers reported an 18% increase in average shoplifting incidents per year in 2024 versus 2023, with threats or acts of violence during theft events increasing 17% during the same period (Source: National Retail Federation).

As criminal enterprises become more brazen and dangerous, asset protection leaders must adopt multi-layered security strategies that combine physical security measures, intelligent video analytics, employee training, and data-driven decision-making. For Asset Protection Managers and Directors, the hurdle is no longer just about stopping opportunistic shoplifting; it is about managing multi-location security operations against coordinated attacks while ensuring internal compliance.

This article provides actionable guidance on implementing video analytics specifically for protecting high-value assets and locked cabinets. We will explore integration strategies, operational metrics, and best practices for minimizing shrinkage while maintaining compliant, efficient retail operations.

Key terms to know

Before diving into specific strategies, it is helpful to clarify the terminology used in modern asset protection technology.

• Video Analytics: Software that automatically examines video streams to detect events, recognize objects, and surface trends in real time, moving beyond passive recording to active intelligence.

• Organized Retail Crime (ORC): Theft or fraud conducted with the intent to convert illegally obtained merchandise into financial gain, typically involving groups of individuals.

• Shrinkage: The difference between recorded inventory and actual available stock, often caused by theft, administrative error, or vendor fraud.

• Edge AI: Processing video data locally on the device or on-premise hardware rather than sending all raw footage to the cloud, enabling faster alerts and lower bandwidth usage.

• Electronic Article Surveillance (EAS): A technology used to guard against shoplifting where tags are attached to merchandise and cause an alarm to sound if removed from the store without being deactivated.

The disconnect in modern asset protection

Asset protection leaders today face a critical gap between current practices and emerging threats. Traditional video systems only provide value after incidents occur, requiring hours of manual footage review while losses accumulate. By the time theft is discovered, the opportunity for intervention has passed. This reactive approach contributes to the $100 billion annual cost of shrinkage for U.S. retailers (Source: Silver Star Protection Group).

The core frustrations for retail security leaders often revolve around three specific areas:

• Reactive security systems: Legacy cameras record crimes but do not stop them.

• False alarm fatigue: Inaccurate alerts from basic motion sensors can cause staff to ignore them.

• Limited visibility into internal theft: Internal theft accounts for a large part of shrink but remains difficult to detect without overbearing monitoring that damages workplace culture.

Video analytics bridges this gap by transforming video from a passive record into an anticipatory tool. Instead of asking "What happened?" days later, video analytics enables teams to ask "What is happening right now?" and intervene in real time.

Strategic role of video analytics in asset protection

The fundamental distinction between traditional camera systems and modern video analytics is the shift from passive recording to real-time threat detection. For high-value assets like jewelry, electronics, and pharmaceuticals, this distinction is critical.

Moving beyond traditional monitoring

Video analytics systems employ advanced computer vision to detect suspicious behavior in real time. This allows asset protection teams to standardize security across hundreds of locations without increasing headcount.

Detecting high-value asset theft

Video analytics designed for high-value protection operates through several interconnected detection mechanisms.

• Loitering detection: Systems monitor approach patterns and dwell times around high-value display cases. They flag instances where individuals return repeatedly to the same locked case or linger in specific areas longer than typical shopping behavior would indicate.

• No-go zone alerts: Analytics trigger alerts if display cases are accessed outside normal business hours or by employees without authorization. This is critical for protecting stockrooms and cash offices.

• Suspicious behavior identification: AI identifies patterns such as customers concealing items or moving in unusual ways that suggest deliberate avoidance of detection.

Locked cabinet security and high-value item protection

While video analytics provides the intelligence layer, effective high-value asset protection requires a robust physical security foundation. Locked display cases serve as the primary barrier, but they must be integrated with digital monitoring to be effective against sophisticated ORC groups.

Integrating electronic locks and access control

Modern locked cabinet security increasingly incorporates electronic locks that can be remotely monitored. Unlike traditional keys, which can be lost or copied, electronic locks create audit trails.

• Automated audit trails: Electronic locks document every instance a case is opened, by whom, and for how long.

• Video verification: When integrated with video analytics, an access event automatically triggers video recording. If a display case is opened, the system captures the visual context—who opened it and what was removed.

• Exception reporting: Analytics can flag access events that occur outside of normal parameters, such as a cabinet being opened five times in an hour by an employee who typically accesses it twice a shift.

RFID and inventory tracking

Radio Frequency Identification (RFID) technology enhances locked cabinet security by tracking inventory in real time.

• Real-time movement tracking: RFID tags embedded in merchandise enable the system to detect when an item is removed from a locked case.

• Exit detection: If an RFID-tagged item passes through an exit without proper deactivation at the point of sale (POS), the system triggers an alarm and correlates the event with video footage.

• Minimizing internal theft: This integration helps identify if items are moving from the stockroom to the sales floor or out the back door without authorization.

Advanced video analytics capabilities for retail

To effectively protect high-value assets, retailers are deploying advanced computer vision models that go beyond simple motion detection.

Computer vision and object recognition

Computer vision models can identify tens of thousands of consumer product SKUs, even when labels are partially occluded (Source: WiserBrand). This capability allows systems to:

• Detect missing items: Recognize when specific high-value items disappear from locked cases or shelves.

• Monitor shelf availability: Identify out-of-stock situations that might indicate theft or operational failure.

• Verify transactions: Correlate the visual evidence of an item with the transaction data to ensure the correct product is being purchased.

Behavioral analysis and proactive alerting

Behavioral analysis systems identify pre-theft indicators. ORC operatives typically conduct reconnaissance before stealing, such as photographing locking mechanisms or testing case accessibility.

• Reconnaissance detection: Flags individuals who linger near high-value displays without interacting with staff.

• Coordination detection: Identifies groups entering together who then split up to distract staff, a common ORC tactic.

• Anticipatory intervention: Alerts staff to offer customer service to loitering individuals, often deterring theft before it occurs.

Point-of-sale (POS) integration

Integrating video analytics with POS systems is one of the most effective ways to combat internal theft and sweethearting (under-ringing items for friends).

• Transaction verification: When a high-value item is scanned (or voided), the system flags the associated video clip.

• Sweethearting detection: Analytics can detect when an item is passed around the scanner or when a low-value code is entered for a high-value item.

• Refund fraud monitoring: Matches video to refund transactions to ensure a customer is actually present and merchandise is being returned.

Case studies: Operational impact of video analytics

Real-world applications demonstrate how integrated video analytics transform asset protection from reactive to forward-looking.

Jewelry retail: Reducing ORC impact

• The pain point: Coordinated attacks across 500 stores during peak hours when staff was stretched thin.

• The solution: High-resolution cameras focused on display cases, integrated with electronic locks and edge-based analytics to detect loitering and unauthorized access.

• The outcome: ORC losses decreased by 35% in the first quarter. Investigation time dropped from 4 hours to 30 minutes per incident (Source: Spot AI).

Pharmacy retail: Controlling internal diversion

• The primary obstacle: Internal theft of high-value medications and controlled substances.

• The solution: RFID tracking combined with video analytics. Access events triggered video analysis to verify that removed items matched dispensing records.

• The outcome: Controlled substance inventory loss fell by 42% within the first year, and compliance with DEA regulations improved (Source: Spot AI).

Best practices for implementation

Implementing video analytics for high-value asset protection requires a strategic approach to ensure ROI and operational adoption.

Layered security architecture

No single measure is sufficient. Effective strategies employ overlapping layers.

• Physical security: High-quality locked cabinets and display cases.

• Access control: Electronic locks with audit trails.

• Monitoring technology: Video analytics and RFID for real-time detection.

• Personnel: Trained staff who understand how to respond to analytics alerts.

Employee training and culture

Technology is only as effective as the people using it. Loss prevention culture should emphasize accountability.

• Clear procedures: Train staff on how to respond to "Person Enters No-go Zone" or "Loitering" alerts.

• De-escalation: Ensure employees know how to approach suspicious individuals safely, prioritizing customer service over confrontation (Source: Axonify).

• Transparency: Inform employees about monitoring to improve compliance and trust. Research shows employees are more comfortable with monitoring when the purpose is clear (Source: Apploye).

Measuring ROI and success

To justify the investment, asset protection leaders must track specific metrics.

• Incident investigation time: Measure the hours saved on footage review. Spot AI users often cut investigation time from hours to minutes (Source: Spot AI).

• Shrinkage improvement: Track loss rates in high-value categories specifically. A 15-30% drop in shrinkage is achievable in the first year (Source: Spot AI).

• Alert precision: Monitor the percentage of alerts that lead to actionable interventions to tune the system and minimize nuisance alarms.

Comparison: Spot AI vs. traditional video systems

When evaluating solutions for high-value asset protection, it is essential to consider deployment speed, flexibility, and intelligence capabilities.

Conclusion

The retail landscape has shifted, and asset protection strategies must evolve to meet the threat of organized retail crime and sophisticated internal theft. Relying on reactive video systems that only document losses is no longer a viable strategy for protecting high-value assets.

By integrating video analytics with physical security measures like locked cabinets and electronic access control, retailers can transform their security operations. This approach enables real-time detection of suspicious behavior, automates compliance monitoring, and drastically reduces investigation time. The result is not just a reduction in shrinkage, but a safer environment for employees and customers, and a clear return on investment through operational efficiency.

For asset protection leaders, the next step is moving from legacy systems to a unified, data-driven platform. To see how video AI can help protect your high-value inventory, request a Spot AI demo and experience the platform in action.

Frequently Asked Questions

How does video analytics help minimize retail theft?

Video analytics helps deter theft by detecting suspicious behaviors—such as loitering near high-value items or entering restricted areas—in real time. This allows staff to intervene with customer service, often deterring the theft before it occurs, rather than just recording the incident.

Can video analytics integrate with my existing cameras?

Yes, modern video AI platforms like Spot AI are camera-agnostic. They can connect to most existing IP cameras, allowing you to upgrade your system with advanced analytics without the cost and disruption of ripping and replacing your current hardware (Source: Spot AI).

What are the ROI considerations for investing in asset protection technologies?

ROI is driven by three main factors: a direct drop in shrinkage (often 15-30% in the first year), operational savings from less time spent on investigations (cutting hours to minutes), and lower potential insurance premiums due to improved risk management (Source: Spot AI).

How do I monitor locked display cases effectively?

Effective monitoring involves a layered approach: use high-resolution cameras focused on the cases, implement electronic locks that create access logs, and use video analytics to trigger alerts when cases are opened outside of normal business hours or by unauthorized personnel.

How can retailers best address internal theft?

Combating internal theft requires visibility. Integrating video analytics with Point of Sale (POS) systems allows you to correlate transactions with video evidence, exposing schemes like sweethearting, fake refunds, and merchandise sliding. Additionally, monitoring "no-go zones" ensures stockrooms and cash offices are only accessed by authorized staff (Source: Spot AI).

About the author

Sud Bhatija is COO and Co-founder at Spot AI, where he scales operations and GTM strategy to deliver video AI that helps operations, safety, and security teams boost productivity and reduce incidents across industries.