Facial Recognition Faces a Trust Crisis: Balancing Public Safety and Privacy

Facial recognition technology is increasingly embedded in daily life, from public surveillance to access control in private buildings. Yet despite its growing use, trust in the technology remains low, largely due to concerns over privacy, security, and potential misuse.

Surveillance Systems: Convenience at a Cost

Public surveillance relies on facial recognition to identify individuals in one-to-many scenarios, often in public spaces where consent is neither given nor requested. This model has raised significant concerns about privacy and security. Images are captured, stored, and compared to large databases, sometimes including watchlists of “people of interest.” Yet users often have no knowledge of when or where their data is collected, or how it is subsequently used.

Historical incidents highlight the risks: in 2018, a hacker linked to the Sinaloa cartel exploited Mexico City’s surveillance system to track and harm informants. Recent investigations, such as Congressional scrutiny of Flock Safety’s license plate scanning systems, show that weak security practices persist, leaving sensitive systems vulnerable to criminal access.

Technological solutions like ZeroTier, a software-defined encrypted overlay network, offer ways to mitigate such risks. By isolating each camera’s data feed and ensuring secure, cryptographic communication between devices and databases, the potential for lateral movement by attackers is minimized. While this does not eliminate public distrust, it strengthens security and ensures that only authorized personnel can access sensitive video streams.

Facial Recognition for Access Control: Privacy by Design

In contrast to surveillance, facial recognition used for access authentication is more controllable, consensual, and purpose-specific. Firms like Alcatraz.ai have developed “privacy-first” systems that do not store facial images. Instead, a user’s face is converted into a unique, unintelligible digital representation, or “blob,” which cannot be reverse-engineered to reconstruct the original image.

This approach enables secure entry to offices, data centers, or other controlled spaces without compromising privacy. Additionally, predictive security features, such as tailgating detection and event-pattern analysis, allow for proactive monitoring without tracking personal identities. The system only evaluates anonymized event patterns, providing both safety and privacy.

The Trust Challenge

The public remains wary of facial recognition, particularly when it is used for mass surveillance. Incidents of hacking and lax security underscore the risks, and the potential for authoritarian misuse further erodes confidence. However, applications like access authentication demonstrate that facial recognition can be implemented in ways that are consensual, secure, and privacy-respecting.

Ultimately, the future of facial recognition depends on earning public trust. Systems must be designed with strong security, limited and transparent usage, and privacy-first methods. Only then can this technology balance its benefits in safety and convenience with the ethical and societal concerns it raises.