Enterprise AI Governance
Govern what AI can do.
AI tools are moving from generating content to taking actions. Oconee Runtime gives engineering and AI leaders visibility and control over AI activity with context-aware policies that allow, warn, or block actions based on risk.
No sales call required.
- AI agent
- Proposed action
- Oconee Runtime
- Policy evaluation
- Allow / Warn / Block
- Evidence
What changed
AI changed. Enterprise control needs to change with it.
AI systems increasingly execute commands, modify files, interact with repositories, use tools, and reach sensitive resources. The gap is no longer what a model says — it is what it is permitted to do.
Before — Ask → Answer
- The model produced text.
- A person decided what to do with it.
- Review sat between intent and effect.
Now — Ask → Reason → Act
- The model proposes an action.
- Tooling can carry it out directly.
- Nothing sits between intent and effect by default.
Architecture
Put policy between AI intent and execution.
The model can propose an action. Organizational policy determines whether that action should be allowed.
- User + context
- AI agent
- Proposed action
- Oconee Runtime
- Identity, action, resource, context, risk, policy
- Allow / Warn / Block
- Execution + evidence
What it does
See. Govern. Prove.
See
Understand AI activity across supported browser AI tools and AI-assisted engineering surfaces — what was used, in what context, and by which workspace.
Govern
Apply policy at the action layer. Rules evaluate the signal, the resource and the context, and resolve to allow, warn, or block.
Prove
Every decision is recorded — the action, the policy that matched, and the outcome — so a question about what happened has an answer.
Demo
See Oconee Runtime in 30 seconds.
Context
Same action. Different context. Different policy.
Risk is not a property of an action alone. Where it lands decides what it is worth.
Development workspace → Warn
- A risky AI-assisted action in a low-sensitivity workspace.
- The engineer is warned and keeps moving.
- The decision is recorded either way.
Critical repository → Block
- The equivalent action against a sensitive repository.
- Policy refuses it at the point of action.
- Evidence names the rule that matched.
Execution path
A safe prompt doesn't guarantee a safe execution path.
Security has to follow the agent to the point of action.
- Safe prompt
- Agent
- Files, dependencies, repositories, tools
- Proposed action
- Policy decision
Enforcement
Policy that does more than observe.
- Detected
- Policy evaluated
- Warn / Block
- Audit evidence
Why it matters
Adopt powerful AI without giving it unchecked authority.
Accelerate AI adoption
Say yes to capable tools because there is a control layer, not despite there not being one.
Establish guardrails
Define what AI may do — and where — before an incident defines it for you.
Maintain engineering velocity
Warn where a nudge is enough. Block only where the risk warrants it.
Build a governance foundation
A policy and evidence layer that holds as AI systems take on more.
Audience
Built for
- CTO
- VP Engineering / Platform
- Head of AI
- AI Platform Teams
Coverage
Supported workflows
Browser AI
ChatGPT, Claude, Gemini and Perplexity in the browser, through the Oconee Runtime extension for Chrome and Edge.
AI-assisted engineering
The Oconee Runtime extension for VS Code and Cursor, covering in-editor AI activity.
Coding agents
Claude Code, governed at the point a tool call is proposed rather than after it runs.
Repository and workspace context
Policies that resolve differently depending on the repository or workspace an action targets.
Evidence
Know what happened — and why.
Actor, action, resource, context, policy, decision. Recorded together, so the answer to “what happened here?” is one place, not a reconstruction.
AI is becoming more capable.
Your governance should too.
See what AI is doing. Apply policy based on context. Govern what happens next.
No sales call required.
Not ready for a trial?
Tell us what you are trying to govern and we will reply directly.