Understanding P.A.L.O. and PolicyWatcher
Abstract
AI governance becomes difficult when the evidence behind a decision is distributed across provider policies, technical dependencies, internal controls, and changing system versions. This report explains how PALO and PolicyWatcher connect these elements in a workflow that supports accountable review. The first part introduces the roles of the governance framework, the policy-monitoring service, and the technical implementations through a fictional change to an inference provider's retention policy. It explains how a source observation becomes a review candidate and why applicability still depends on organizational context. The technical part examines versioned graph records, explicit service mappings, historical queries, validated ingestion, authenticated review, and persistent decision history. A reproducible example identifies two review candidates across three fictional AI systems. The accompanying source snapshot includes the dependency query and a separate operational runtime with persistent inventory and review records. Verification comprises 18 query tests, 18 runtime tests, and a dated live connector check on five public events with their evidence packets and native PALO signals. These results establish specific implementation properties and connectivity; they do not measure production effectiveness or the accuracy of policy interpretation. The report provides a common vocabulary and an inspectable implementation for engineers, governance practitioners, and readers who need to understand how policy monitoring relates to the AI systems already in use.
