Risk & compliance

Govern your AI

Set the rules agents work under and see that they hold. Each agent session is tied to the work it touched, the checks it passed, and the person who signed off, so the record is there before anyone asks for it.

Policy checkpoints
Put a check in the flow where the risk is, and see what passes it.
Audit trail
Answer who did what, with which model, on which work item.
Approval gates
Require a person to sign off before agent work ships, and record it.
Model allowlist
Decide which models may touch which work, and see when work breaks the rule.

See agent work in context
Bloomfilter binds each agent session to the work item it touched in Jira, GitHub, and CI/CD, and to the person who reviewed it, so agent work has an owner and a record.
Put checks where the risk is
Compare compliance and rework by step, team, and model to see where a checkpoint earns its cost and where it only slows work down.
Automate the audit
Once a policy is defined, Bloomfilter checks work against it as it moves and keeps the record, so an audit is a report to run rather than a project to staff.

Compliance rate
How much agent work passed a checkpoint rather than going around it
Time to approval
How long agent work waits for a person to sign off
Unreviewed changes
Agent changes that shipped without a person looking at them
Policy exceptions
Where the rules were set aside, by whom, and how often

Bloomfilter compared with the tools teams use to govern AI today
	Bloomfilter	Vendor admin consoles	GRC tools	Code review	Spreadsheets
Agent session and model on the record	Yes	Yes	No	No	No
Tied to the work item	Yes	No	No	Yes	No
Human sign-off recorded	Yes	No	Yes	Yes	No
Rolled up across teams for audit	Yes	No	Yes	No	No
Audit trail without manual entry	Yes	Yes	No	Yes	No
Independent of the AI seller	Yes	No	Yes	Yes	Yes