The Certified AI Risk and Maturity Modeler (CAIMM) program equips professionals with the expertise to assess, model, and manage AI risks while evaluating organizational AI maturity. This certification blends AI governance, compliance, risk modeling, and AI capability maturity frameworks into one powerful credential, enabling participants to guide AI transformation initiatives with confidence.
CAIMM prepares participants to support government, defense, enterprise, and regulated industries in developing AI systems that are responsible, auditable, secure, and scalable.
Learning Objectives
Participants will be able to:
- Apply structured methodologies for AI risk identification, assessment, and modeling.
- Conduct AI maturity assessments using global standards (e.g., NIST AI RMF, OECD, EU AI Act readiness).
- Align AI systems with organizational risk appetite and governance structures.
- Develop and implement risk mitigation strategies across the AI lifecycle.
- Create custom AI risk and maturity dashboards, reports, and scorecards.
- Identify red flags in AI development, deployment, and use cases.
- Communicate AI risk posture and maturity levels to stakeholders and leadership.
Target Audience
- AI Governance Officers
- Risk Managers and Compliance Leaders
- Data Scientists and ML Ops Professionals
- CIOs / CTOs / Digital Transformation Leaders
- Internal Auditors and Legal/Policy Experts
- Consultants working with AI Governance or AI Readiness
Course Modules
Module 1: Foundations of AI Risk and Governance
- Defining AI risk types: operational, ethical, regulatory, adversarial
- Introduction to AI governance frameworks: NIST AI RMF, ISO/IEC 42001, OECD AI Principles
- Mapping the AI lifecycle to risk domains
Module 2: Risk Modeling for AI Systems
- Risk scoring methods for data, model, and outcome layers
- Threat modeling for AI (TARA, STRIDE for AI, ATLAS integration)
- Residual risk, risk acceptance, and reporting
Module 3: AI Maturity Model Development
- AI maturity frameworks: organizational, technical, cultural
- NLL.ai maturity matrix model
- Capability benchmarking across AI use cases
Module 4: Assessing and Communicating AI Risk Posture
- Creating dashboards, heatmaps, and risk registers
- Case studies on failed/unsafe AI deployments
- Effective communication with executive leadership and regulators
Workshops and Case Studies
- Workshop 1: Map AI risks for a real-world use case (e.g., facial recognition or loan scoring)
- Workshop 2: Build a maturity model for a fictional organization’s AI program
- Capstone Exercise: Draft a risk & maturity assessment report for presentation to a stakeholder panel