Governing AI Agents in Life Sciences: The Case for an AI Management System (AIMS)
AI Agents are already running in production across clinical trial management, eTMF operations, pharmacovigilance, and regulatory submissions. Governance frameworks have not kept pace. The result is a deployment-governance gap that creates three distinct categories of risk: FDA and EMA inspection findings, undetected model drift affecting clinical data quality, and M&A due diligence exposure that sophisticated buyers will price into transaction terms.
This white paper makes the operational and regulatory case for an AI Management System — the control plane that sits above individual agents and makes an AI program manageable, inspectable, and compliant at scale.
What's inside:
- What FDA (GMLP, PCCP) and EMA guidance actually require from organizations deploying AI in GxP contexts
- ISO 42001 mapped to life sciences — what's already covered by your QMS and where the real gaps are
- The six dimensions of the AIMS control plane: provisioning, cost monitoring, health and accuracy, observability, compliance, and incident management
- A seven-stage AI agent lifecycle governance framework with proportionate risk-tier controls
- An AIMS maturity model for assessing your current state across all six dimensions
- The Cloudbyz AIMS: an AWS-native control plane integrated across the full eClinical suite
