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Design PII redaction across speech, retrieval, model inputs, actions, outputs and logs without destroying the context a real-time AI avatar needs.
Test how a real-time AI avatar degrades when speech, retrieval, models, GPUs, media and business APIs fail—before users discover the weakness.
Classify live avatar data by sensitivity and consequence, then enforce where capture, inference, retrieval, actions and logs may process it.
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.
