Most clinical teams focus on model performance, but when FDA or EMA audits arrive, the infrastructure underneath gets scrutinized first. Can your system produce complete audit trails? Enforce data isolation? Survive computer system validation? FDA's 2025 guidance introduced a seven-step credibility framework for AI used in trials, pharmacovigilance, and manufacturing contexts that inform regulatory decisions about drug safety or effectiveness. The documentation demands cannot be satisfied retroactively. EMA's finalized reflection paper is equally clear: AI in clinical development must meet the same GxP standards as any regulated technology, and sponsors remain fully accountable for AI outputs. In January 2026, FDA and EMA jointly published ten Guiding Principles for Good AI Practice, with data governance and lifecycle management front and center. Infrastructure choices determine whether you can meet 21 CFR Part 11 audit trail requirements, HIPAA business associate obligations, and ICH E6(R3) validation scope. Single-tenant or private VPC architectures provide dedicated infrastructure per sponsor, eliminating cross-tenant data exposure risks that multi-tenant SaaS designs carry. A disclosed PostgreSQL vulnerability showed how shared database isolation can fail. GAMP 5 and the new GAMP AI Guide set the validation standard for GxP AI systems. The compliance boundary starts at the infrastructure layer, not the model layer.