Insights

Insights

Technical writing on confidential computing, regulated AI, and the infrastructure of trust.

Abstract illustration for article: What Confidential Computing Actually Means for Regulated AI Workloads
confidential computing TEE

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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Abstract illustration for article: Why Banks Can't Run GPT on Real Customer Data
fintech banking

Why Banks Can't Run GPT on Real Customer Data — and the Path Forward

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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Abstract illustration for article: Attestation in Practice — What Your CISO Needs to Know
attestation CISO

Attestation in Practice: What Your CISO Actually Needs to Know

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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Abstract illustration for article: Healthcare AI and HIPAA — How Enclaves Close the Gap
healthcare HIPAA

Healthcare AI and HIPAA: How Enclaves Close the Gap Between Innovation and Compliance

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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Abstract illustration for article: Intel TDX vs AMD SEV-SNP Workload Decision Guide
Intel TDX AMD SEV-SNP

Intel TDX vs AMD SEV-SNP: A Workload Decision Guide for Platform Engineers

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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Abstract illustration for article: Multi-Party Computation vs TEE for AI Inference
MPC TEE

Multi-Party Computation vs TEE for AI Inference: When to Use Which

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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Abstract illustration for article: APPI, FISC, and Confidential AI
APPI FISC

APPI, FISC, and Confidential AI: The Japanese Financial Sector's Data Governance Stack

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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