This program equips professionals to lead risk and control assessments for high-risk AI systems. It focuses on evaluating AI vulnerabilities, mapping threat surfaces, identifying bias and adversarial risks, and ensuring system auditability. Participants learn how to design tailored controls and assessment frameworks. The training builds practical skills in assessing real-world AI systems, emphasizing accountability, governance, and responsible AI deployment. CLAI enables organizations to navigate AI risks confidently and uphold compliance and ethical standards.
Audience:
- Risk managers
- AI project leads
- AI governance officers
- Compliance professionals
- Security architects
- Technical auditors
Learning Objectives:
- Understand AI-specific risk profiles and threat vectors
- Map and evaluate AI threat surfaces and vulnerabilities
- Assess system behaviors for bias, drift, and adversarial risks
- Design tailored controls for different AI risk scenarios
- Measure auditability, explainability, and system transparency
Program Modules:
Module 1: AI Risk Landscape and Frameworks
- Introduction to AI risk categories
- Regulatory trends and global AI risk guidelines
- Risk appetite and AI use-case alignment
- High-risk AI system definitions
- Frameworks for AI risk management
- Mapping AI lifecycle to risk points
Module 2: Threat Surface Mapping for AI Systems
- Entry points and exposure paths
- Data, model, and inference surface threats
- Integration risk with external systems
- Threat modeling for AI pipelines
- External attack vectors (e.g., API abuse)
- AI-specific threat scenarios
Module 3: Bias, Drift, and Adversarial Risk Assessment
- Types of bias in AI models
- Monitoring for concept/data drift
- Adversarial attacks: white-box and black-box
- Validation techniques for risk detection
- Building detection pipelines
- Control responses to drift and bias
Module 4: System Auditability and Metrics
- Metrics for explainability and transparency
- Traceability in AI decision-making
- Model versioning and change logging
- Audit trail implementation
- Accountability structures in AI teams
- Tools for system monitoring
Module 5: Control Design for AI Systems
- Tailoring controls for AI-specific risks
- Preventive vs. detective control strategies
- Human-in-the-loop safety mechanisms
- Ethics and governance layers
- Role-based control responsibilities
- Documenting control objectives and results
Module 6: Leading AI Risk Assessments
- Planning and scoping the assessment
- Stakeholder alignment and communication
- Evidence gathering techniques
- Risk scoring and prioritization
- Reporting findings with impact
- Post-assessment follow-up and improvements
Exam Domains:
- AI Risk Taxonomy and Impact Analysis
- Adversarial and Model Vulnerability Assessment
- Governance and Regulatory Alignment
- Auditability, Explainability, and Transparency
- AI Control Framework Implementation
- Assessment Leadership and Stakeholder Communication
Course Delivery:
The course is delivered through a combination of lectures, interactive discussions, and project-based learning, facilitated by experts in the field of Certified Lead AI Assessor (CLAI). Participants will have access to online resources, including readings, case studies, and tools for practical exercises.
Assessment and Certification:
Participants will be assessed through quizzes, assignments, and a capstone project. Upon successful completion of the course, participants will receive a certificate in Certified Lead AI Assessor (CLAI).
Question Types:
- Multiple Choice Questions (MCQs)
- True/False Statements
- Scenario-based Questions
- Fill in the Blank Questions
- Matching Questions (Matching concepts or terms with definitions)
- Short Answer Questions
Passing Criteria:
To pass the Certified Lead AI Assessor (CLAI) Certification Training exam, candidates must achieve a score of 70% or higher.
Join the CLAI Certification Program to become a trusted leader in AI risk assessment and ensure your organization builds and deploys AI systems responsibly and securely.