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Conduct an AI data protection impact assessment for real-time avatars across capture, inference, actions, retention and deployment boundaries.
Design confidential computing for private AI avatars with attestation-gated keys, an explicit trust boundary and twelve architecture-review tests.
Build an AI model licensing rights matrix for on-premise avatars across deployment, copying, optimisation, derived artefacts, support and exit.
Design AI guardrails for real-time avatars across identity, speech, RAG, model output, actions and handover with seven enforceable policy gates.
Prioritise AI patches across models, GPU runtimes, media and private infrastructure using risk-based lanes, end-to-end tests and rollback evidence.
Design AI network segmentation for private avatars with seven trust zones, default-deny flows, workload identity, WebRTC controls and 12 tests.
