What Confidential Computing Actually Means for Regulated AI Workloads
A plain-English breakdown of hardware enclaves, attestation, and why 'encrypt in use' is the missing layer that lets AI agents safely touch live account and patient data.
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Technical writing on confidential computing, regulated AI, and the infrastructure of trust.
A plain-English breakdown of hardware enclaves, attestation, and why 'encrypt in use' is the missing layer that lets AI agents safely touch live account and patient data.
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Financial institutions want AI agents to process transaction histories, credit files, and KYC data. Here's why current cloud architectures make compliance officers say no — and what changes when the workload moves inside an enclave.
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Remote attestation is the cryptographic proof that code is running unmodified inside a genuine hardware enclave. This post explains the chain of trust from CPU firmware to your audit log.
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Hospitals are piloting AI for clinical decision support and patient-record summarization, but HIPAA's 'minimum necessary' standard and BAA obligations have blocked real-data training. Confidential enclaves change the calculus.
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Both Intel TDX and AMD SEV-SNP offer hardware-level memory isolation, but they differ in VM granularity, attestation report format, and cloud availability. Here's how to match your workload profile to the right TEE substrate.
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MPC and TEEs are both privacy-preserving compute approaches, but they have very different performance profiles, deployment complexity, and threat models. For latency-sensitive AI inference on sensitive data, here is where the trade-offs land.
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Japan's Act on the Protection of Personal Information (APPI) and the FISC security guidelines for financial institutions create a layered compliance requirement. This post maps each requirement to what an enclave-based AI pipeline satisfies by default.
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