Policy-based AI governance is a model where customer-defined rules determine how AI outputs and agent actions are evaluated, with each governed interaction producing an explicit decision and a reviewable record.
Without defined policies, AI governance is ad hoc and inconsistent. Teams make case-by-case decisions about what AI outputs are acceptable, with no structured framework, no repeatable evaluation criteria, and no traceable decision trail. This creates governance gaps that grow as AI deployments scale.
Policy-based governance replaces ad hoc decisions with structured, repeatable evaluation. Customer-defined policies specify what is allowed, restricted, and what requires escalation. Each governed AI output and connected agent action is evaluated against these policies, producing explicit decisions and reviewable records.
Without policy-based governance, organizations cannot demonstrate how AI decisions were made. With it, each governed interaction has a defined evaluation path, an explicit outcome, and a record that can be reviewed, audited, or exported.
Ethics frameworks provide principles and guidelines. Policy-based governance provides enforceable rules that produce explicit decisions on each governed interaction.
Checklists are periodic and manual. Policy-based governance evaluates each governed AI output and connected agent action in real time against defined rules.
Manual review requires human evaluation of outputs. Policy-based governance automates evaluation against customer-defined policies, escalating to humans only when policies require it.