AI Action Governance Use Cases
Govern AI actions across the workflows that matter.
Oconee Runtime helps security and engineering teams apply policy to AI activity across coding agents, IDE workflows, browser AI tools, repositories, MCP tools, and enterprise resources.
No sales call required.
Use cases
Start with the problem you have
AI coding agent governance
Agents now run commands, change files and install dependencies, not just suggest them.
Evaluate each proposed action against policy before it executes, and keep the decision.
Protect critical repositories
The same command is routine in a sandbox and unacceptable in production.
Classify repositories, and let one policy resolve differently in each.
MCP and AI tool governance
An agent with a tool list has as much authority as the most dangerous tool on it.
Detect tool invocation, evaluate it against policy, and record what was decided.
Prevent sensitive AI actions
Risk appears after the prompt, in what the agent reads and assembles.
Detect the risky action itself, and decide on it with full context.
For security teams
Dashboards report AI activity after the fact and change nothing about it.
Enforce policy at the action, and keep evidence an investigation can use.
For engineering leaders
Blanket restrictions push engineers to unmanaged tools and lose you the visibility.
Warn where a nudge is enough, block only where the resource warrants it.
For MSSPs
Clients adopt AI faster than their controls evolve, and ask you what to do.
Deliver AI governance as a service on a platform you did not have to build.
For private equity portfolios
Every portfolio company adopts AI differently, and nobody can compare them.
Deploy one consistent governance approach company by company.
The model
Every use case runs the same decision
The surfaces differ and the policies differ. What does not change is what gets evaluated, and what gets kept afterwards.
- Actor
- Agent
- Action
- Resource
- Context
- Policy
- Decision
- Evidence
The action proceeds, and the decision is still recorded.
The person is told, and keeps working.
The action is refused at the point of action.
Sensitive content is removed before the action continues.
A blocked action can be appealed through an exception request, granted as single-use or time-limited. Multi-level approval workflows are an enterprise capability.
AI proposes. Policy decides.
See the decision model applied to your own repositories and agents.