USDM’s proprietary seven-pillar governance architecture — designed specifically for life sciences AI. Aligned to FDA CSA, EU AI Act Articles 9–15, ISO 42001, NIST AI RMF, and GAMP 5 Second Edition. Click each pillar to explore requirements and USDM capabilities.
AI systems in GxP environments must have documented, auditable decision logic. Regulators and auditors expect to understand how AI outputs are generated — and companies must be able to explain AI-driven decisions in human-interpretable terms. USDM implements explainability frameworks that satisfy FDA and EU AI Act Article 13 disclosure requirements.
AI systems must perform as intended — consistently, repeatably, and within defined boundaries. USDM applies FDA CSA risk-based validation principles to establish performance baselines, validate AI outputs, and maintain ongoing monitoring for drift and degradation across the model lifecycle.
AI models trained on biased or non-ALCOA+ compliant data don’t just produce bad predictions — in a GxP context, they can generate biased outputs that influence regulatory submissions, patient safety decisions, and quality judgments. USDM’s bias assessment methodology addresses both algorithmic fairness and data integrity at the foundation.
AI systems introduce novel cybersecurity risks — LLM data leakage, prompt injection, model poisoning, and adversarial attacks — on top of existing GxP data security requirements. USDM integrates AI-specific cybersecurity controls with your existing GxP security framework and regulatory data integrity obligations.
Tested AI is validated AI — with documented evidence that the system performs as intended under all conditions relevant to its GxP use. USDM applies structured testing methodologies adapted from FDA CSA and GAMP 5 to produce inspection-ready validation packages for AI systems of every risk classification.
Human oversight is non-negotiable for high-risk AI in life sciences. EU AI Act Articles 14–15 and FDA’s AI/ML guidance both require documented human-in-the-loop controls, clear accountability structures, and formal governance mechanisms. USDM establishes the operating model that makes accountability real — not just documented.
AI systems should not remain static after deployment — especially in regulated environments where performance, risk, and business context evolve over time. USDM applies a controlled lifecycle approach to continuously refine AI systems through testing, monitoring, feedback, and governed updates so improvements are captured without compromising compliance, validation status, or operational integrity.
