Know what applies and what must happen.
Maintain regulatory, contractual, policy, and certification requirements with owners, deadlines, and evidence expectations.
AI for risk and compliance
Reduce assurance work without weakening control.
We help risk and compliance teams prepare evidence, review exceptions and verify corrective actions with less manual effort. We build AI into those workflows with clear human approval and measure readiness, cycle time, manual effort and exposure.
The risk mandate
Keep controls and evidence ready for review.
We connect requirements, control evidence and remediation records so your team can see what is ready for review and what still needs attention.
Verify that required safeguards operate.
Map obligations to controls, monitor execution, test evidence, and keep deficiencies visible.
Check whether teams follow the required policies.
Maintain current policy, route attestations and training, and identify where actual work diverges from expectations.
Find unusual or noncompliant activity earlier.
Monitor approved sources, apply defined rules, gather context, and route potential exceptions for review.
Verify corrective actions before closing findings.
Assign corrective work, preserve decisions, verify completion, and keep recurring root causes visible.
Demonstrate what happened and why.
Maintain evidence, ownership, approvals, testing history, exceptions, and remediation in a reviewable trail.
How AI helps
Keep evidence ready for compliance reviews.
AI can monitor evidence and routine compliance work, but interpretation, risk acceptance, and consequential enforcement remain with people.
Structure obligations, policies, contracts, and standards.
Connect requirements to controls, owners, systems, and evidence.
Check approved activity for required state and exceptions.
Assemble context and distinguish likely issues from noise.
Assign, track, and verify corrective work.
Preserve the source, judgment, action, and result.
Division of responsibility
Keep risk decisions with the responsible owners.
The boundary reflects legal interpretation, enforcement authority, materiality, confidentiality, and the consequences of a wrong decision.
- Structure requirements and evidence expectations
- Map controls, owners, systems, and deadlines
- Collect and check routine evidence
- Detect exceptions using approved rules
- Prepare investigations and remediation state
- Maintain traceable review packages
- Legal and regulatory interpretation
- Risk acceptance and materiality
- Policy, control, and enforcement changes
- Consequential employee or customer action
- External representations and certifications
- Cases with incomplete or conflicting evidence
Systems we can build
Start with a recurring control review or remediation process.
Choose a review where evidence collection or follow-up takes significant time. Confirm access to the sources, agree how effort and exposure will be measured, and name the risk owner who accepts the result.
Keep requirements and control owners current.
Structure requirements, owners, deadlines, control mappings, and evidence needs as rules change.
Continuously prepare control evidence.
Collect approved evidence, check expected state, identify gaps, and route tests requiring judgment.
Prepare evidence before the compliance review.
Organize evidence, approvals, exceptions, and remediation history before the audit request arrives.
Route unusual cases through the right authority.
Gather the request and context, apply policy, prepare the decision, and preserve the outcome.
Review supplier risk at onboarding and renewal.
Coordinate questionnaires, evidence, findings, approvals, monitoring, and renewal review.
Confirm remediation and record the evidence.
Assign corrective actions, track deadlines, preserve approvals and test whether the fix addressed the control failure.
Measured in risk outcomes
Measure review readiness and remediation time.
Compare detection and remediation time, evidence quality, repeated failures and manual effort with the starting baseline. Review the AI security decisions for business leaders →
Evidence current
More obligations and controls supported before a review begins.
Exception age
Less time between a potential issue, a decision, and verified remediation.
Findings and recurrence
Fewer preventable findings and repeated control failures.
Manual assurance work
Less time collecting, checking, and reconstructing evidence.
Your risk operation
Find the workflow creating the most assurance effort or exposure.
An AI Audit establishes its current readiness, effort, remediation time, and exposure baseline, then defines a practical implementation roadmap.
How would AI help close a control finding?
Suppose a control requires managers to review access each quarter. AI collects the approved access list and dated review records, then flags a missing sign-off. A reminder or a manager’s name in a spreadsheet does not establish that the review occurred. The control owner checks the evidence, resolves the access decision and records the completed review.
Keep the finding open until the required evidence exists. Measure time to verified closure, reviewer corrections, overdue items and repeated failures. The risk or legal specialist decides whether the evidence meets the applicable obligation. Use launch tests for technical access failures and governance rules for approvals and accountability.