Make consequential AI decisions accountable.
Connect the case, the applicable control, and the accountable human at the point an AI-assisted workflow affects a customer or business outcome.
The action to govern
Change a credit limit
The control has to reach the individual case.
A credit recommendation can be well documented while the final limit change still lacks the right review. KLA Processes bring policy and human decisions into the path from recommendation to action.
A credit recommendation needs an exception decision.
The agent prepares a proposed limit change. A configured exception condition requires the credit reviewer to examine the supporting case before the change can proceed.
Increase a business credit limit
The recommendation falls outside the standard lending criteria and requires a credit review.
- Process
- Credit limit review
- Control
- Lending policy exception
- Reviewer
- Credit risk team
Applicable rule
An exception to the standard criteria requires an authorized credit decision.
Review required
The proposed limit change stays paused until the exception is decided.
Make the control model part of the working process.
Start with one consequential decision. Identify its inputs, the permitted next steps, and the role accountable for an exception.
Business owner
The decision boundary
Name the customer or operational outcome and the exact action that changes it.
Risk team
The review requirement
Translate the selected control into a policy condition and an accountable reviewer.
Engineering team
The governed Process
Connect the case inputs, policy checks, review, and downstream result in the execution path.
Execution Lineage
Give reviewers a case they can follow.
Review the record from the initial request through the human decision and execution result. Use the evidence to assess how the selected controls operated.
Explore Lineage Explorer- What was the decision based on?
- The recorded case inputs and references available to the reviewer.
- How was the exception handled?
- The policy outcome, assigned review, and recorded human decision.
- What was the final result?
- The subsequent action and its execution status, linked to the case.
Define what a successful evaluation must show.
Bring one workflow, the action you need to control, and the people who own its rules. Agree the integration scope and acceptance criteria with KLA.
Discuss your workflow- The team can identify the controlled action and its accountable owner.
- Standard and exception cases take the configured paths.
- An independent reviewer can follow the case and identify missing evidence.
Does using KLA establish regulatory compliance?
KLA supports the execution and evidence of selected controls. Legal classification, applicable obligations, policy adequacy, and compliance conclusions remain matters for your legal and control teams.
What does high-risk mean on this page?
Here it describes AI-assisted work with material consequences, such as credit, claims, or eligibility decisions. A legal high-risk classification depends on the applicable framework and the specific use of the system.
How much of the process should we start with?
Choose one consequential action with an identifiable owner, a clear policy, and test cases. Expand after the team has verified the control path and reviewed the resulting evidence.