A housing authority has live SLAs with a case-management AI vendor. Which practice best verifies ongoing AI security commitments?

A housing authority with live SLAs for a case-management AI vendor best verifies ongoing AI security commitments by monitoring whether the vendor meets those SLAs through periodic evidence and issue tracking. That practice works because signed SLAs are promises, not proof; continuous or scheduled evidence—control attestations, incident metrics, patch/model-change notices, and remediation tickets—shows whether AI security commitments remain true in operation for tenant-facing case systems.

Reviewing vendor model performance dashboards and user satisfaction scores for AI security SLA assurance fails because model accuracy and satisfaction scores measure functional quality and user experience, not whether AI security controls and SLA commitments are actually being met. Monitoring vendor SOC 2 renewal status and ISO 27001 certification scope for AI security SLA confirmation fails because generic point-in-time certifications do not evidence whether the specific AI security SLA commitments are met in ongoing operation, including incident handling and model-change control. Auditing vendor encryption settings and access review logs against baselines for AI security SLA oversight fails because narrow point-in-time technical checks alone do not verify the full scope of AI security SLA commitments or track exceptions and remediation to closure.

Exam caveat: monitoring volume must match risk; monthly deep dives on low-risk widgets waste capacity, while annual glances at high-impact housing AI invite surprise failures. Operational check: define SLA evidence artifacts and cadence, track exceptions in a shared issue log with due dates, escalate missed AI security SLAs to contract remedies, and report status to housing leadership each quarter.