Edge AI Security
Edge AI security changes the trust model because the device is physically present, resource constrained, and often deeply embedded in a larger system.
Overview
At the edge, AI is deployed close to sensors, users, and actuators. This improves latency and sometimes privacy, but it also gives attackers more direct access to the implementation.
Threat model
Edge attackers may probe the board, monitor power or timing, inject glitches, replace firmware, tamper with sensors, or extract model artifacts from storage and memory. Lightweight devices cannot always afford heavyweight security controls.
Countermeasures
Good defenses combine secure boot, storage protection, device attestation, hardware hardening, lightweight runtime checks, sensor validation, and workload-aware protections against leakage and fault abuse.
Open challenges
The core challenge is practicality: defenses must respect latency, power, area, and cost limits. Edge AI security therefore benefits from hardware-aware and deployment-aware design rather than generic one-size-fits-all solutions.