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Government of Canada – DTPR AI Register

AI-Assisted Anomaly Detection at Airport Security Checkpoints

Safety & Security · Enforcement

What it collects that can identify you

Biometric
Identifiable data
  • Video imagery of individuals captured at non-passenger screening checkpoints, including images of persons and their movements, processed by computer vision algorithms to detect screening anomalies.

Also collects about a place, which is anonymized data.

Run by
Canadian Air Transport Security Authority (CATSA)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization
Your copy
You cannot see the data it holds about you. What you can do

What it is for

Safety & SecurityEnforcement

This AI system uses computer vision to automatically flag unusual screening activity at non-passenger screening checkpoints in Canadian airports, alerting human security staff for review. It is being developed by the Canadian Air Transport Security Authority (CATSA) to support its oversight of screening operations. The system involves personal information and its use is disclosed to those affected. It is currently in development and not yet fully deployed.

What it collects and what happens to it

Data taken in

Biometric
Identifiable data
  • Video imagery of individuals captured at non-passenger screening checkpoints, including images of persons and their movements, processed by computer vision algorithms to detect screening anomalies.
About a place
Anonymized data
  • Physical checkpoint environment data — camera feeds covering the spatial layout of non-passenger screening checkpoints, used to contextualize person and object detections.

Processing

Computer Vision
  • Open-source computer vision algorithms are applied to checkpoint video feeds to detect screening anomalies — identifying unusual events, behaviours, or configurations at non-passenger screening checkpoints.
Anomaly Detection
  • The system applies anomaly detection to flag departures from expected screening behaviour at non-passenger checkpoints, surfacing these for human review rather than acting autonomously.

What it does

Sensing (Perceptive AI)
Human decides
  • The system senses and interprets video feeds from checkpoint cameras to detect anomalies. Human oversight staff review all flagged detections before any action is taken.
Deciding (Analytical AI)
Human decides
  • The system scores or classifies screening events as anomalous or normal, producing flags for human review. A person reviews every flagged result — the AI advises, the human decides.

Outputs

A recommendation or prediction
Identifiable data
  • The system produces anomaly flags — alerts highlighting potentially irregular screening events — which are presented to CATSA oversight staff (GC employees) for human review and follow-up decision. The output is advisory, not a binding determination.

Run by

Canadian Air Transport Security Authority (CATSA)
  • CATSA is the federal Crown corporation responsible for air transport security screening in Canada. It is developing and deploying this AI oversight system at non-passenger screening checkpoints as part of its operational oversight activities.

Government of Canada AI Register — AI Oversight at NPS-T Checkpoints

Built by

Not stated by the Helpful Places.

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • AI-generated anomaly flags and associated checkpoint data are available to CATSA oversight staff (GC employees) who use them for security oversight purposes.
Not available to me
  • Individuals screened at non-passenger checkpoints do not have access to the AI-generated anomaly flags or the underlying video data processed about them by this system.

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

  • Right to Be Informed of AI UseCATSA discloses to individuals that AI is in use at non-passenger screening checkpoints. Individuals subject to screening are informed that an AI system may be monitoring and flagging activity for human review.
  • Right to a Human ReviewAll anomaly flags produced by this AI system are reviewed by CATSA GC employees before any follow-up action is taken. No automated determination is made without human oversight.

Risks and safeguards

  • Civil liberties harmComputer vision surveillance of individuals at security checkpoints may infringe on privacy and civil liberties, and could disproportionately flag certain individuals.Safeguard: All AI-generated anomaly flags are reviewed by human GC employees before any action is taken; AI use is disclosed to affected individuals; the system is limited to non-passenger screening oversight contexts.
  • Reputational harmFalse-positive anomaly detections could lead to unwarranted scrutiny of innocent individuals at checkpoints, damaging their reputation or causing distress.Safeguard: Human review is required before any action is taken on a flagged event; the system is positioned as a support tool for oversight, not as an autonomous decision-maker.