AI Agent Governance for Life Sciences Pharmacovigilance Intake
AI agent governance for pharmacovigilance intake requires per-agent identity, least-privilege tool scoping, runtime policy enforcement evaluated at each tool call, and structured audit logging of agent actions across EHR, case management, and reporting systems. No pharmacovigilance-specific AI agent regulation currently exists, so architecture decisions draw on adjacent references including NIST's AI Risk Management Framework, zero-trust access models, and HIPAA audit control requirements.
Core Controls for AI Agents in Intake Workflows
A short summary of the four architectural controls discussed in this article, before the full technical treatment below.
Agent Identity
Unique, non-human credentials per agent role, distinct from shared service accounts.
Least-Privilege Scoping
Tool and API access limited to the minimum required for each intake task.
Runtime Policy Enforcement
Per-call authorization checks rather than static upfront permissions.
Audit Logging
Structured records of agent identity, tool invoked, data accessed, and outcome.
What Governance Means for AI Agents in Adverse Event Intake
Life sciences organizations are increasingly using AI agents to automate pharmacovigilance intake, including adverse event capture, triage, and case creation across EHR, case management, and reporting systems. Governing these agents means more than defining what data they are allowed to touch at deployment time. It requires a combination of distinct agent identity, task-scoped permissions, enforcement of those permissions at the moment each tool is invoked, and logging sufficient to reconstruct what an agent did and why. NIST's AI Risk Management Framework describes this as a continuous responsibility applied across Govern, Map, Measure, and Manage functions, extending into deployment and post-deployment monitoring rather than stopping at design review. OWASP's guidance on large language model applications identifies "excessive agency" as a leading risk category, where agents invoke external tools without adequate permission scoping. No regulation currently governs AI agents in pharmacovigilance intake specifically. The frameworks referenced here are adjacent risk-management and security references, not certifications or compliance attestations for this use case.
Why Standing Permissions Create Risk in Intake Pipelines
Agents interacting with EHR systems, case management platforms, and regulatory reporting tools often inherit broad standing credentials originally designed for human users or batch service accounts. NIST SP 800-207 describes a zero-trust model in which access decisions are made dynamically, per session or per request, rather than relying on pre-granted trust. This distinction matters in pharmacovigilance intake because a single workflow may involve an agent reading patient records for triage, writing new case data, and aggregating fields into an Individual Case Safety Report struct