Enterprise governance

Scale AI automation without giving up control.

Q: Will AI automatically perform important actions?

Only when the workflow is designed and authorized to do so. Add approval before critical actions.

Q: Can we configure approval before execution?

Yes. Manual approval pauses the run until an authorized decision is recorded.

Q: Can we see who ran and approved an action?

Execution and approval records provide operational evidence. Confirm retention and audit requirements for your deployment.

Q: Where is data stored?

Storage depends on the deployment configuration. Validate residency and retention requirements before production use.

Q: Can it run in a private environment?

Private deployment options depend on edition and agreed infrastructure; the required topology should be reviewed with the team.

Q: Are roles and enterprise support available?

Team roles and enterprise deployment support are available according to the selected plan and agreement.

Put approvals, permissions, execution evidence and deployment boundaries directly into every important workflow.

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Core capabilities

Control at the decision boundary

APPROVAL

Manual approval

Pause a workflow before a critical step and record the reviewer decision.

ACCESS

Roles and permissions

Use team roles and scoped API access to limit operations.

AUDIT

Execution evidence

Retain run status, step output and approval context for review.

DEPLOY

Private deployment review

Validate deployment topology, data boundary and support requirements with the team.

How it runs

AI recommends. A person authorizes. The system records.

The control point is part of the workflow, not a side conversation.

1AI prepares recommendation
2Responsible owner reviewsAPPROVAL
3System executes
4Record is retained
5Administrator audits

Product proof

Governance that is visible in the run

Approval state, execution output and team access are product controls—not certification claims.

01Manual approval before critical steps
02Team roles and scoped API keys
03Inspectable execution and approval history

Reference governance pattern

Reviewable change workflow

AI prepares a proposed change with supporting context. The workflow waits for an authorized owner and records the decision before execution continues.

BEFORE

Before: recommendations and approvals live in separate tools.

WITH BRAIDRUN

After: the proposal, decision and resulting execution share one run history.

The operational gap

Automation without accountability cannot scale

Important workflows need a named decision-maker and durable evidence.

AI acts too early

A recommendation becomes an action before the responsible person can review it.

Access is too broad

Teams cannot safely share automation when roles and credentials are unclear.

Evidence is incomplete

Auditors and operators need to reconstruct who approved what and which run changed state.

FAQ

Questions, answered

Will AI automatically perform important actions?

Can we configure approval before execution?

Can we see who ran and approved an action?

Where is data stored?

Can it run in a private environment?

Are roles and enterprise support available?

Enterprise governance

Test the control flow before production

Model one important decision, add its owner and inspect the complete run history.

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