Post-deployment monitoring and drift detection
Risk and Assurance
What is post-deployment monitoring and drift detection?
Sets performance metrics, thresholds and review triggers after launch, and treats a model change, a vendor update or new data as a reason to re-check.
Where the frameworks place it: EU AI Act Article 72; NIST AI RMF MEASURE 3 and MANAGE 4.
The interview question it draws
A model has been in production for a year. What do you monitor, what threshold triggers a review, and who gets the alert?
A strong answer walks through the practice itself, with one real case, what you decided, and what the evidence showed afterwards.
Roles that ask for it
- Model Risk Managercore, depth expected
- AI Auditorrequired, working knowledge
- AI Engineer, production systems editionrequired, working knowledge
- AI Evaluation Specialistrequired, working knowledge
- AI Governance Engineerrequired, working knowledge
- AI Model Validatorrequired, working knowledge
- AI Product Manager, responsible product editionrequired, working knowledge
- AI Risk Managerrequired, working knowledge
- AI Incident Response Leadrequired, working knowledge
- Chief Audit Executive, AI audit editionrequired, working knowledge
- Chief Technology Officer, AI engineering editionrequired, working knowledge
- AI Vendor Risk Managerpreferred, working knowledge
- Agentic AI Engineer, orchestration editionpreferred, working knowledge
- Robotics AI Engineer, embodied systems editionpreferred, working knowledge
Backgrounds that already carry it
- Model risk, credit risk and quantitative analysis (described, not yet shown)
Performance monitoring thresholds are familiar.
Where it is taught and graded
15 graded topics, each passed by explaining it back. The first module of every program is free with a free account.
- Module 7: Internal Control Architecture and Control Engineering (1)
- Module 8: Compliance Management Systems and Regulatory Intelligence (1)
- Module 12: Third-Party, Supply-Chain, and Concentration Risk (1)
- Module 18: Governance, Risk and Compliance Technology, Automation, and Evidence Engineering (1)
- Module 20: Governance, Risk and Compliance Leadership and Operating Model Transformation (1)
- Module 7: Rollout, Monitoring, and Incident Response (2)
- Module 11: The Capstone: Deploy and Defend (1)
- Module 3: Shipping AI and Surviving the Incident (1)
- Module 4: Evaluation and Trust (1)
- EU AI Act Implementation Expert2 topics
- Module 3: High-Risk AI Requirements: The Technical File (1)
- Module 8: Post-Market Monitoring and Enforcement (1)
- Module 9: Agentic AI and Workforce Integration (1)
- Module 6: Process and Operations Redesign (1)
- Module 3: Generative AI Compliance (1)
Questions
- What is post-deployment monitoring and drift detection?
- Sets performance metrics, thresholds and review triggers after launch, and treats a model change, a vendor update or new data as a reason to re-check.
- Which AI governance roles ask for post-deployment monitoring and drift detection?
- 14 roles on the map name it, and it is core to Model Risk Manager.
- How do I learn and prove post-deployment monitoring and drift detection?
- 15 graded topics teach it across 7 programs. Each topic is graded by explaining it back against its own transcript, so a pass is evidence, not attendance. The first module of every program is free with a free account.
- What interview question tests post-deployment monitoring and drift detection?
- A model has been in production for a year. What do you monitor, what threshold triggers a review, and who gets the alert? A strong answer shows the practice itself: Sets performance metrics, thresholds and review triggers after launch, and treats a model change, a vendor update or new data as a reason to re-check.