Compare · Governance
AI governance and runtime governance
AI governance programmes establish what an organization's AI should be permitted to do. Runtime governance determines what it actually does, at the moment it acts. The first produces decisions; the second makes them binding.
What AI governance platforms does well
- Establishing policy, risk appetite and accountability
- Model and system inventories mapped to obligations
- Review workflows, sign-off and documentation for regulators
- Reporting on posture across a portfolio of AI systems
Where it stops
- The artefacts describe intent; they cannot refuse an action
- Assessments are point-in-time while behaviour changes daily
- Coverage depends on systems being registered in the first place
- Evidence is assembled from downstream logs rather than the enforcement point
What changes with autonomous execution
- Agents act between review cycles, thousands of times
- Tool calls and agent-to-agent chains cross system boundaries a register never sees
- Supervisors increasingly ask for proof of behaviour, not proof of process
How Skipr complements it
- Policies authored in the governance programme are enforced per action
- Evidence produced at enforcement flows back as proof for the same programme
- Coverage follows the runtime path, so unregistered systems are still governed when they act
Side by side
| Dimension | AI governance platforms | Skipr |
|---|---|---|
| Primary artefact | Documentation and reports | Runtime decision and signed evidence |
| Timing | Before and after | During, in the path of the action |
| Can it stop an action? | No | Yes — allow, deny, escalate or route |
| Scope of principals | Registered AI systems | People, AI agents, applications, machines |
Questions
- Do these compete?
- No. One decides policy, the other enforces it. Organizations that have invested in a governance programme get more from it once enforcement exists.
- Which should come first?
- Policy has to exist before it can be enforced, but the enforcement layer does not require a mature programme to start governing high-risk paths.
- Where does monitoring fit?
- Monitoring observes outcomes after execution. It is useful and it is not enforcement.
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Other comparisons
- Skipr and identity and access managementIAM establishes who a principal is and what they may reach. Skipr decides whether a specific action may execute right now, and proves it.
- Skipr and privileged access managementPAM controls privileged sessions and credentials for administrators. Skipr governs every action — human, agent, application or machine — at runtime.
- Skipr and zero trust network accessZTNA decides whether a principal may connect to a resource. Skipr decides whether the action taken over that connection may execute, and proves it.