How Is AI Used in Surveillance? | Beyond The Camera

AI in surveillance analyzes live or recorded feeds to detect and flag people, faces, objects, and behaviors for human review.

Most people picture AI surveillance as software that “watches” video. In practice, the AI is doing something more limited and more useful: it automates the detection, identification, and alerting work that would otherwise take a room full of human monitors. It flags potential matches or events, then sends them to a person who decides what happens next. That human step is the part that matters most.

Here’s what AI actually does in surveillance systems, how the U.S. government constrains its own use, and why the real-world limits matter more than the marketing.

What Does AI Actually Do in a Surveillance System?

AI in surveillance typically processes camera, video, audio, and sensor data to detect specific things and flag them for attention. The most common surveillance tasks include recognizing faces, spotting license plates, tracking vehicles, detecting objects left behind, and identifying unusual behavior or anomalies in a feed.

The U.S. Department of Homeland Security defines facial recognition as comparing facial features against available images or video for verification or identification. The comparison produces a score or a list of potential matches, not a final verdict about who someone is. Federal legislation introduced in 2025 describes “face surveillance” separately, as using facial recognition with real-time or stored video to track, observe, or analyze someone’s movement, behavior, or actions over time.

For a practical sense of what this looks like in a system you can buy, see our roundup of the best AI surveillance cameras, which compares models built around person detection, facial recognition, and smart alerts.

Who Uses AI Surveillance, and How?

The biggest users of AI surveillance in the United States are government agencies, law enforcement, and private businesses. Government use gets the most scrutiny, and it is governed by a patchwork of policies rather than one national law.

Key areas of use include:

• Investigative support: The FBI says facial recognition produces only a potential investigative lead. It requires human review and additional investigation before any action is taken.
• Verification and identification: Comparing a face against a reference image (verification) or searching a database of images (identification).
• Behavior and anomaly detection: Flagging motion patterns, loitering, crowds, or objects that appear where they shouldn’t be.
• License plate recognition: Reading plates from camera feeds to track vehicles of interest.

The critical constraint across federal agencies is human involvement. DHS materials state that face-recognition results cannot be the sole basis of a law- or civil-enforcement-related action. In plain terms: the system can point, but a person has to decide.

What Rules Apply to AI Surveillance in the U.S.?

Federal rules are not uniform. The DHS, the FBI, and proposed legislation each set different guardrails, and there is no single federal law that expressly authorizes or limits the federal government’s use of facial recognition.

DHS Directive 026-11, issued September 11, 2023, is the most concrete federal rule. It applies to face recognition and face capture by DHS, including government-operated technology used by or on behalf of DHS. The directive requires:

• Testing for unintended bias or disparate impact before deployment.
• An opt-out right for U.S. citizens for non-law-enforcement uses, unless otherwise authorized or required.
• A ban on using face-recognition results as the sole basis for enforcement action.

The FBI adds a training requirement: law-enforcement users must complete facial-recognition training before searching the NGI-IPS database, with training aligned to FISWG guidelines. The GAO separately reports that DHS and DOJ are expected to complete initial privacy reviews when designing, developing, or procuring projects involving personally identifiable information.

Oversight is still evolving. The State Department’s 2023 guiding principles encourage documenting surveillance uses through logs and records to support oversight, and H.R. 4695 (introduced in the 119th Congress) would go further by prohibiting facial recognition for face surveillance and requiring annual NIST benchmark testing for law-enforcement systems.

As the U.S. Commission on Civil Rights noted in its 2024 report, there were no federal laws or regulations expressly authorizing or limiting facial recognition use by the federal government at that time. That remains the key fact: the rules are agency-specific, and they are still being written.

What Are the Biggest Mistakes and Risks?

The most common failure is treating an AI match as proof of identity. A facial-recognition result is a lead, not a conviction, and federal civil-rights guidance warns that false matches are a real risk without corroboration.

Other frequent mistakes include using AI results as the sole basis for an enforcement decision, deploying systems without privacy review or bias testing, and assuming one federal policy applies everywhere. The rules differ across DHS, the FBI, and proposed state-level legislation, so “federal law” is not a single answer. States and localities are also passing their own restrictions, which means the practical answer to “what’s allowed” changes by jurisdiction.