Point 7(d) of Annex III classifies as high-risk AI systems intended to be used by or on behalf of competent public authorities, or by Union institutions, bodies, offices or agencies, in the context of migration, asylum or border control management, for the purpose of detecting, recognising or identifying natural persons, with the exception of the verification of travel documents. Such systems are classified as high-risk because they often apply biometric or identity recognition, where outputs can immediatel y trigger intrusive measures (extra screening, refusal of entry, restriction of movement, or detention) with limited time and a limited ability to contest. The combination of error rates, demographic bias, and opaque decision logic can therefore translate into wrongful identification and discriminatory treatment.
High-risk classification under point 7(d) of Annex III is determined by the intended purpose ; the place of deployment and the sensor modality are not decisive. Verification of travel documents is excluded from the use case listed in point 7(d) of Annex III. On-person checks used within person detection or identification, such as liveness or presentation-attack detection, may fall under that use case and, where biometric identification or categorisation is involved, under the use case listed in point (1) of Annex III.
AI systems used exclusively for search and rescue are not border-control uses and therefore fall outside the the use case listed in point 7(d) of Annex III . Only functions that neither detect, recognise, or identify persons and that do not influence operational decision making may benefit from the exceptions listed in Article 6(3) AI Act.
a) Practical examples of AI systems falling within the use caseof point 7(d) of Annex III
Identification of persons at border-crossing points
- An AI system using live facial recognition for identity checks at border-crossing points, comparing live camera feeds with biometric templates in connected systems and returning identity hits for operator action. Such a system is in scope because the intended purpose is to identify natural persons for border-control purposes, which falls under the terms ‘detect, recognise or identify’. Detection that cues operational action at land or sea borders
- AI-combined satellite imagery services,surveillance towers or unmanned platforms that flag human presence for response for a border-control response acting on the persons detected . Such a system is in scope because the intended purpose to detect people approaching a land border and to generate alerts that trigger patrol dispatch, apprehension or second-line checks.
- An AI system used for maritime surveillance that detects and tracks persons for migration management or border-control operations, including cueing interception or boarding. Such a system is in scope because the intended purpose is to detect and track natu ral persons for these operations, irrespective of whether the platform operates in territorial waters, the contiguous zone or on the high seas. What matters is the intended purpose, not its location. By contrast, if an AI system is tasked exclusively for safety of navigation or lifesaving, with its outputs not cueing a border control unit, but sent to a SAR (search and rescue) coordination, used only to prevent collisions or to coordinate search and rescue, not to initiate or support border-control actions, the system will be out of scope.
- An AI system intended to analyse sensor data to detect the presence or number of persons in vehicles (including hidden persons) and to generate alerts that prompt second-line checks. The system is in scope, since it concerns the detection of natural persons for border-control purposes, even without identification. The filter mechanism cannot apply because the alerts generated by the system drive operational action, which is neither a narrow procedural nor preparatory task. Person-level monitoring in controlled facilities
- An AI system intended to monitor reception or detention areas, detect or track identified persons, and alert staff for intervention. The system detects or tracks natural persons in a migration-management setting and is therefore in scope. If the system only estimates scene-level anomalies (for example, a crowd surge or a loud-disturbance spike), without detecting or tracking persons and without person-specific alerts, it is out of scope.
b) Practical examples of AI systems falling outside the use caseof point 7(d) of Annex III
AI systems that are not border-control or person-detection functions
- An AI system intended to detect persons in distress at sea exclusively to coordinate search-and-rescue operations, with no link to migration-management or border-control tasks. Such a system is out of scope because the intended purpose is lifesaving, not border control.
- An AI system intended to verify travel or identity documents (for example, chip or PKI validity, morphing or ink anomalies, copied signatures). Verification of travel or identity documents is excluded from this use case.
- An AI system embedded in a maritime or unmanned platform used by border authorities, where it is intended and technically limited to collision avoidance (for example detecting obstacles, including persons, only to prevent impacts and without generating ale rts for interception, tracking, triage or any border-control response). Such a system is out of scope because its outputs are used exclusively for safety of navigation, not for migration, asylum or border-control management. Where an AI system with the sam e sensing or detection capability is intended to be used to cue or support border-control actions regarding persons, it will be in scope. Analytics that do not detect, recognise or identify persons
- An AI system intended to analyse historical data to identify patterns of document fraud and to inform general indicators or staffing, without person detection. Such a system is out of scope, since its output does not detect, recognise or identify natural persons. If the patterns identified by the system are later applied to named individuals to triage them, the system would be in scope since it performs a person-level risk assessment.
- An AI system intended to estimate crowd size or gate occupancy to balance lanes and reduce queues. Such a system is out of scope, provided it is designed so that it does not detect, recognise or identify persons and does not generate person-level alerts.
c) Practical example of AI systems falling within the use case but exempted under the filter mechanism of Article 6(3)
- An AI system intended to stabilise, denoise or enhance video feeds without performing any detection, recognition or identification of persons and without producing cues for operational response. Such a system falls within the exception for AI systems intended to perform a preparatory task listed in Article 6(3)(d) AI Act.