Facial recognition has moved from being a niche security technology to a widespread feature embedded in public CCTV systems, retail analytics, access control, transportation hubs and law enforcement tools. While often presented as an efficiency booster or safety enhancer, facial recognition introduces high-stakes privacy, ethical and regulatory risks. These risks increase significantly when the technology is combined with large-scale video surveillance systems powered by AI. This article explores the major dangers associated with facial recognition, the regulatory frameworks governing its use and the operational safeguards organizations should adopt.
Before examining the risks, it is crucial to understand how regulators classify facial recognition technology. Globally, most privacy laws treat facial recognition as high-risk biometric processing.
Under the GDPR, biometric data used for uniquely identifying a person is considered a “special category of personal data,” requiring heightened safeguards [1]. The UK ICO states that facial recognition used in surveillance settings constitutes biometrics and typically requires legitimate, necessary and proportionate justification [2]. In the United States, states such as Illinois regulate facial recognition under dedicated biometric laws like BIPA, which impose strict consent and retention requirements [3].
Facial recognition becomes especially sensitive when combined with CCTV networks, where continuous monitoring allows ongoing identification without a person’s knowledge. This amplifies the privacy impact significantly and is subject to increasing scrutiny from regulators worldwide.
One of the widely documented dangers of facial recognition lies in accuracy gaps and algorithmic bias, especially in diverse real-world environments.
The U.S. National Institute of Standards and Technology (NIST) has repeatedly found large variations in false positive and false negative rates across different algorithms, with error rates disproportionately affecting women and people with darker skin tones [4]. These accuracy disparities make facial recognition unreliable for critical decisions such as access control or law enforcement identification.
Video surveillance footage often suffers from poor lighting, suboptimal camera angles or motion blur, further reducing matching accuracy. This problem intensifies in large CCTV networks, where cameras vary in resolution and placement.
Misidentification can result in wrongful detentions, denial of services, workplace discrimination or incorrect blacklisting. Multiple real-world incidents have shown how reliance on flawed facial recognition can lead to significant personal and organizational harm [4][5].
Facial recognition dramatically changes the nature of CCTV by enabling tracking, profiling and behavioural inference at scale.
Traditional CCTV passively records activity for later review. Facial recognition transforms this into active monitoring capable of identifying individuals in real time. The European Data Protection Board (EDPB) warns that real-time biometric identification in public places is rarely lawful under the GDPR [6].
Continuous identification in public can discourage free movement, participation in political events or visits to sensitive locations. Civil liberties groups argue that facial recognition enables pervasive surveillance similar to tracking, creating societal pressure to self-censor behaviour.
Facial recognition data can be combined with loyalty systems, mobile analytics, law enforcement databases or commercial datasets. This cross-correlation enables invasive profiling with little transparency for individuals.
Organizations deploying facial recognition face not only ethical concerns but also substantial legal exposure under multiple regulatory frameworks.
Under GDPR, facial recognition generally requires explicit consent or a narrowly defined exception such as substantial public interest [1]. Most commercial deployments do not meet these thresholds. The EDPB’s guidance states that biometric identification in public places is typically unlawful [6].
Several U.S. states enforce biometric privacy laws. The Illinois BIPA is the strictest, requiring written consent, retention limits and private rights of action [3]. Major companies have faced multi-million-dollar lawsuits for non-compliance with BIPA.
European regulators have repeatedly fined organizations for deploying facial recognition without adequate legal basis, transparency or risk assessments. These cases demonstrate that biometric processing is treated as high-risk and requires strong accountability safeguards.
Biometric data is extremely sensitive because, unlike passwords, it cannot be changed if compromised.
Facial templates stored in systems can be exploited to impersonate individuals across services. Attackers may use techniques such as deepfake injection or synthetic face reconstruction to bypass systems. Data breach impacts are long-lasting because biometric identifiers are permanent.
Many CCTV systems operate on outdated hardware or insecure networks, making them vulnerable to unauthorized access. Real-time facial recognition processing adds additional attack surfaces, often requiring powerful cloud-based or edge AI systems.
Interconnected surveillance networks create a single point of failure. A breach in one subsystem can expose biometric data across multiple interconnected systems or departments.
Facial recognition raises concerns beyond legal compliance, touching on fundamental societal values.
Biased algorithms can disproportionately misidentify minorities, leading to unjust profiling or increased scrutiny. Studies by NIST and academic institutions consistently highlight these risks [4][5].
Many facial recognition deployments lack clear policies, signage or public communication. Individuals may not know they are being identified, how long their data is stored or who it is shared with.
When organizations deploy facial recognition covertly, it can undermine public confidence and result in reputational damage, especially when inaccuracies or controversies emerge.
Despite the risks, organizations can minimize harm through responsible design and governance.
When identification is not strictly necessary for security purposes, anonymization or automatic face blurring can protect privacy while still enabling operational use of video footage. Modern tools such as Gallio PRO automatically detect and anonymize faces and license plates, offering organizations a compliant alternative without disabling surveillance functionality.
Facial recognition should only be used when demonstrably necessary. Regulators advise implementing a necessity-and-proportionality assessment before deployment [2][6].
A detailed Data Protection Impact Assessment helps identify risks and required safeguards. Regulators increasingly expect DPIAs for any biometric deployment, especially in public settings.
No. Several jurisdictions restrict or ban it in public spaces, and many require explicit consent.
Because it processes biometric identifiers, enables mass tracking and can cause harm through misidentification.
Yes. Wrongful arrests, discrimination or denial of services can result in lawsuits and regulatory penalties.
Yes. Blurring or anonymization reduces risk significantly while allowing footage to remain useful.
Accuracy is improving, but bias and environmental distortion remain persistent problems.
