This episode examines on-device and edge AI security, focusing on models deployed in mobile, IoT, or embedded systems where resources are constrained and connectivity may be intermittent. For certification purposes, learners must understand the unique risks of on-device AI, including theft of model files, tampering with local execution environments, and loss of centralized monitoring. The exam relevance lies in being able to describe why edge environments demand different safeguards compared to centralized cloud AI deployments.

Practical scenarios include attackers extracting proprietary models from mobile apps, manipulating IoT devices to alter inference results, or exploiting offline execution to bypass policy enforcement. Best practices include encrypting model files at rest, using secure enclaves or trusted execution environments for sensitive tasks, and enforcing code signing to prevent tampered binaries. Troubleshooting considerations highlight the difficulty of pushing security updates to distributed devices and ensuring privacy compliance when data is processed locally. Learners should be prepared to explain exam-ready defenses that balance performance constraints with the need for strong protection in edge AI systems. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.