When you submit a protocol draft to an AI tool, where does that data actually go? For most clinical AI platforms, the answer is a shared, multi-tenant cloud where your sponsor's intellectual property sits alongside competitors' data on the same infrastructure. That architecture creates three risks regulators are now targeting directly: data leakage through cache timing attacks and shared vector databases, intellectual property exposure that may never be detected, and audit trails you can't fully control during an FDA inspection.
The January 2025 proposed HIPAA Security Rule update addresses AI explicitly, requiring protections for ePHI in AI training data and algorithms, and eliminating the distinction between required and addressable safeguards. Encryption, access controls, and incident response become mandatory for any system processing ePHI. Meanwhile, 21 CFR Part 11 and ICH E6(R3) require sponsors to validate AI systems in production and demonstrate custodial control over audit records. On shared platforms, the vendor controls logs, retention, and access, creating a governance dependency that's hard to defend in an inspection. Private infrastructure isn't a luxury for enterprise clinical AI anymore. It's becoming the only defensible architecture under converging regulatory frameworks.