Computing Requirements for Secure GenAI Inference Workloads

Secure GenAI inference requires isolated compute (dedicated GPUs with memory clearing), encrypted data paths for prompts and outputs, access-controlled serving endpoints, and audited logging of every request — a computing posture that protects both the model and the data flowing through it. For the broader security framework, see building secure AI infrastructure for LLMs.

The Four Computing Requirements

Isolated compute: dedicated GPUs ensure prompts, responses, and model weights are not exposed to other tenants through shared GPU memory. For the isolation mechanism, see how solo capacity stops data leakage. Encrypted data paths: prompts in transit to the model, KV cache in GPU memory, and responses in transit and at rest — all encrypted, with keys governed within the required boundary. Access-controlled serving: only authenticated, authorized identities can reach the inference endpoint, with per-user or per-key access scoping. Audited logging: every prompt, response, and access event logged, tamper-resistant, and governed for retention and residency. For the logging governance, see AI workload deprovisioning security.

FAQ

What computing requirements does secure GenAI inference need?

Isolated compute (dedicated GPUs), encrypted data paths, access-controlled serving endpoints, and audited logging. Security is at the compute level, not just the application level. See the four requirements above and building secure AI infrastructure.

Summary

Secure GenAI inference computing requires isolation, encryption, access control, and audit — at the infrastructure level. For the full security design, see building secure AI infrastructure.