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Enterprise AI Governance

Govern what AI agents are allowed to do — before execution.

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. Or talk through your AI governance architecture.

  1. AI agent
  2. Proposed action
  3. Resource
  4. Context
  5. Policy
  6. Allow / Warn / Approval required / Block
  7. Enforcement
  8. 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.

  1. User + context
  2. AI agent
  3. Proposed action
  4. Oconee Runtime
  5. Identity, action, resource, context, risk, policy
  6. Allow / Warn / Block
  7. 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.

Free AI governance assessment

Benchmark your AI governance readiness.

Evaluate visibility, action authorization, coding-agent controls, MCP/tool governance, and audit evidence across your organization.

Get My Readiness Score

Takes approximately 3–5 minutes. No email required.

For engineering teams: prefer a hands-on technical check? Run the free AI Agent Risk Scanner

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.

  1. Safe prompt
  2. Agent
  3. Files, dependencies, repositories, tools
  4. Proposed action
  5. Policy decision

Enforcement

Policy that does more than observe.

  1. Detected
  2. Policy evaluated
  3. Allow / Warn / Approval required / Block
  4. Enforced
  5. 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.

Coding agents

Claude Code is the most autonomous tool your engineers run.

It executes commands, edits files and acts on repositories, and it does it from a terminal rather than a UI anyone is watching. Governing it is one extension install.

One install, no rollout

The extension registers the Claude Code guard itself — no managed-settings file to distribute, no MDM ticket.

Governed before it runs

Commands, file changes, repository actions and tool calls are evaluated at the point they are proposed.

Your policy, in the terminal

The organization's resolved policy reaches the agent, so posture is what your admins configured rather than a default.

Nothing happens invisibly

Decisions from the terminal surface in the editor's AI Activity panel, and unsupervised sessions are recorded as autonomous.

See how Claude Code governance works — what the extension evaluates, the decisions it returns, and what changes for a developer.

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.

Oconee Runtime dashboard showing AI activity and policy decisions

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.

Or see how this applies to a specific workflow in the use cases.

Not ready for a trial?

Tell us what you are trying to govern and we will reply directly.

Learn more about AI governance

Technical explainers on the controls described above.