AI Governance
Browse 7 articles tagged ai-governance across approvals, auditability, plugins, and controlled AI operations.
Field Work Is Not Complete Until the Evidence Is Complete
How field service teams define required ticket evidence, enforce complete records at closure, review AI troubleshooting, and report on outcomes.
Read article →Immutable Payment Audit Trails Need Workflow Context
An immutable payment file is not enough for legal discovery. What workflow context an audit trail has to carry alongside it.
Audit Logging for Compliance Operations
What compliance operations teams should record in an audit log so a reviewer can reconstruct a decision without asking anyone.
How Four AI Governance Frameworks Handle Approval vs. Runtime Evidence
How the EU AI Act, ISO 42001, and NIST AI RMF treat approval evidence, and where runtime evidence fills the gap they leave.
Governance-First Approval Systems for AI: What They Prove, What They Miss, and Where Runtime Evidence Fills the Gap
What governance-first approval systems for AI prove, what they miss, and where runtime evidence closes the remaining gap.
The Runtime Control Layer: What This Category of Software Is and Why It Exists
Ticket workflows need a control layer between the suggestion and the action. What that layer does, and why existing tools leave it out.
Why Approval, Auth, and Audit Logic Must Stay in the Core
Why approval, authentication, and audit logic belong in the platform core rather than in plugins that can be swapped or misconfigured.
Start with one workflow
Follow a request through triage, review, and a plugin action. See the record that each step leaves behind.