AI GOVERNANCE
AI Governance for Regulatory and High Stakes Life Sciences Work
AI governance in life sciences must address more than technology selection. Organizations need clear accountability for how AI is used, what information informs its outputs, when expert review is required, and how decisions can be traced after the fact.
GOVERNANCE
Governance that starts with the workflow, not a policy document
A practical governance model begins with the workflow. Teams identify the decisions where AI can accelerate work and the points at which an output could create material scientific, clinical, regulatory, safety, or commercial risk. Those higher-risk decisions require defined review roles, documented rationale, and escalation paths.
01
Identify the risk points
Teams identify the decisions where AI can accelerate work and the points at which an output could create material scientific, clinical, regulatory, safety, or commercial risk. Those higher-risk decisions require defined review roles, documented rationale, and escalation paths.
02
Make judgment visible and reusable
Humalign supports this operating discipline by making senior judgment visible inside the workflow. Expert decisions are captured as reusable guidance with named validation, version control, and review triggers, helping teams move beyond one-off AI pilots toward a durable, accountable approach to adoption.
03
Know when not to answer
Strong governance also requires appropriate boundaries. AI should not be expected to answer outside approved evidence, expertise, or scope. Knowing when not to answer is a core safeguard in regulated work.
