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Live enforcement demo

Govern Enterprise AI Before Risk Becomes an Incident

See Oconee Runtime detect sensitive activity, evaluate organizational policy, enforce the appropriate response, and create centralized security evidence.

Text version

What this demo shows: Live enforcement demo — sensitive data

Sensitive content is entered into an AI tool, detection fires, policy is evaluated, the action is blocked, and the event is recorded for security.

  1. Detect

    Sensitive content is entered into a browser AI tool. Detection identifies the category in real time, before the submission leaves the browser.

  2. Evaluate

    Organizational policy is applied to the detected activity to determine the appropriate response for that content class and that tool.

  3. Enforce

    The action is blocked, and the person is told what was found and which rule stopped it — rather than the request failing without explanation.

  4. Record

    The event lands in the dashboard as centralized evidence: what was attempted, what policy decided, and what happened. Recorded as a classification and metadata, not as the sensitive content itself.

  1. Sensitive activity
  2. Detection
  3. Policy evaluation
  4. Block
  5. Audit event

How it works

Detect, evaluate, enforce, record

Enforcement without evidence is a support ticket. Evidence without enforcement is a report nobody acts on. These only work together.

Step 1

Detect

Identify supported sensitive AI activity in real time.

Step 2

Evaluate

Apply organizational policies to determine the appropriate response.

Step 3

Enforce

Allow, warn, or block according to policy.

Step 4

Record

Give security centralized evidence for investigation and governance.

Start For Free

No sales call required.

Beyond the prompt

AI Risk Doesn't Stop at the Prompt

Coding agents raise a different question: what is the AI attempting to do?

  1. Commands
  2. Files
  3. Repositories
  4. Development tools
  5. AI-assisted actions
See Engineering Governance

Text version

What this demo shows: AI engineering governance demo

An AI-assisted engineering action evaluated against repository context and organizational policy — the same action producing a different outcome in a different repository.

  1. AI action

    An AI-assisted engineering action is attempted: a command run, a file modified, or a change staged in a repository.

  2. Context evaluated

    The evaluation reads the development context — which repository, its sensitivity, and the nature of the action — rather than only the prompt that produced it.

  3. Policy applied

    Organizational policy is applied to that action in that context.

  4. Warn or block

    In a low-sensitivity scratch repository the developer is warned and keeps moving. The same action in a production or critical repository is blocked. The decision changes because the context does, not because the action does.

  5. Audit evidence

    Both outcomes are recorded, so a warned action and a blocked one are equally visible to security afterwards.

Related

The controls in this demo, explained in more depth.