Prioritizing Privacy in Enterprise Computer Vision Solutions

Computer vision is reshaping the way businesses operate.

From optimizing restaurant service through ExpressLane, to enhancing workplace oversight with WorkWatch, to streamlining vehicle service operations with PitCrew, solutions like these are transforming complex environments into data-driven ecosystems. By analyzing movement patterns, dwell times, object interactions, and spatial usage, enterprises can unlock unprecedented operational insight and efficiency.

But as cameras become smarter and vision AI becomes more embedded in everyday business processes, a critical responsibility emerges alongside the innovation: protecting the privacy of everyone captured on camera, including workers, customers, and bystanders.

The Promise and Responsibility of Vision AI

Computer vision technology offers powerful benefits. In quick-service restaurants, it helps identify bottlenecks and reduce wait times. In service bays, it tracks vehicles and personnel to improve throughput. In manufacturing plants, it monitors workflows to catch inefficiencies or safety hazards.

These systems are powered by visual data, including streams of images and videos that often contain people. Whether it's a technician walking through a plant, a family entering a fast-food restaurant, or an employee at their workstation, computer vision can (and often does) see them all. And while the goal isn’t surveillance, the potential for exposing personally identifiable information (PII) such as faces, license plates, or name tags is very real.

That’s why, at Leverege and across the industry, privacy is not an afterthought. It is a design principle.

Privacy by Design: From the Ground Up

When building enterprise-grade vision AI systems, privacy must be prioritized, not treated as an afterthought. This means making intentional choices about what is captured, where it's processed, and how it’s stored or shared.

At Leverege, we approach this in three main ways:

1. Minimization by Default
   We capture only the data necessary to deliver value. If a solution is focused on crowd flow or equipment usage, there’s no need to log detailed facial data. Our models are optimized for event detection, not individual identification.
2. Camera-Level Obfuscation
   Where possible, privacy is enforced on the smart cameras themselves. Some of our partner devices can blur or mask faces before the video even leaves the hardware. This ensures that PII never reaches the cloud or backend systems at all.
3. On-Edge and Cloud-Based Anonymization
   Anonymization can be performed either directly on the edge server or in the cloud, depending on the needs of the application. Anonymization tooling can detect and obscure faces or full bodies. Whether it’s pixelation, blurring, or full object removal, the goal is the same: ensure that individuals cannot be personally identified.

The Role of Data Governance

Protecting privacy isn’t just about anonymizing faces. It also means managing the entire data lifecycle responsibly, especially when dealing with personally identifiable information (PII) like faces, license plates, or employee name tags. That’s where data governance comes in. By establishing clear rules for how visual data is collected, stored, accessed, and deleted, organizations can ensure compliance with privacy regulations, reduce risk, and build trust with both employees and customers. Governance frameworks define who owns the data, who can view or share it, how long it’s retained, and what safeguards are in place to prevent misuse. In short, data governance helps transform visual data from a potential liability into a well-managed, privacy-first asset.

Tactics That Protect Customers and Workers

Modern privacy-preserving tactics include:

• Face blurring or pixelation to automatically obscure facial features in real time or during video review
• Masking or silhouette rendering to replace a person’s figure with a generic outline or remove them from the frame altogether
• Liftable masking to keep footage anonymized by default, with controlled access for authorized users
• Selective region redaction to hide only specific parts of an image, like a name tag or license plate
• Data retention limits to automatically delete visual data after short retention windows, unless flagged for investigation or compliance

These tools allow businesses to reap the benefits of vision intelligence, including tracking equipment, measuring service times, and enforcing workflows, without compromising individual privacy.

Privacy Isn’t Just Compliance, It’s Trust

Privacy is not just about avoiding regulatory penalties, although compliance with GDPR, HIPAA, and other frameworks is essential. It is about building trust with everyone involved.

When workers know that their actions are being analyzed without being personally exposed, they are more likely to adopt and support new technologies. When customers see signs that a store or restaurant uses AI responsibly, they feel safer and more respected.

Ultimately, privacy-preserving practices enable vision systems that are powerful, ethical, and scalable. They protect everyone while still delivering critical business value.

At Leverege, we believe that AI should serve people, not watch them. By designing vision solutions with privacy as a priority, we help our customers operate more intelligently while earning the trust of the people they serve.

Let’s build a future where innovation and responsibility go hand in hand.