Certified AI Risk Auditor (CAIRA) Certification Program by Tonex
CAIRA prepares you to audit AI systems for compliance, bias, robustness, and ethical use. You will map risks across data, models, and operations. You will assess governance, document controls, and verify evidence. The approach is practical and methodical. It uses proven audit techniques adapted to AI lifecycles. Findings are concise, reproducible, and defensible.
Cybersecurity impact is central. AI expands the attack surface. Data poisoning, prompt injection, model theft, and insecure pipelines can bypass controls. You will learn to test resilience, verify hardening, and align AI with security policies. The goal is reduced incident likelihood and faster recovery.
The program references leading frameworks and regulations. It emphasizes privacy, safety, transparency, and accountability. You will evaluate fairness, shift risk, and supply-chain integrity. You will also design metrics and continuous monitoring routines. Communication is a core skill. You will brief leaders, regulators, and customers with clarity.
Graduates can drive readiness for internal audits and external reviews. They can plan, execute, and report full-scope AI audits. They can advise teams on remediation that sticks. CAIRA builds confidence to say what is working, what is not, and why.
Learning Objectives:
- Apply risk assessment to AI lifecycle and operations
- Evaluate governance, policies, and control design
- Test bias, fairness, and explainability claims
- Assess adversarial robustness and resilience
- Verify privacy, data protection, and provenance
- Audit vendor and model supply chains
- Build risk scorecards and KPIs
- Produce clear, defensible audit reports
Audience:
- Cybersecurity professionals
- Risk and compliance leaders
- Internal and IT auditors
- Data protection and privacy officers
- AI product and platform owners
- Quality and governance managers
Course Modules:
Module 1: AI Risk & Governance Foundations
- Risk taxonomy and heat maps
- Roles, accountability, and RACI
- Policy and control libraries
- Model lifecycle checkpoints
- Documentation and evidence basics
- Audit scope and planning
Module 2: Compliance & Regulatory Readiness
- Mapping laws and standards
- Control objectives and tests
- Privacy impact assessment review
- Records, retention, and traceability
- Third-party attestations and gaps
- Regulatory response preparation
Module 3: Bias, Fairness & Transparency
- Bias sources and metrics
- Dataset representativeness checks
- Fairness testing workflows
- Explainability claims validation
- Model change and drift impacts
- Remediation and re-testing
Module 4: Robustness & Security Assurance
- Threat modeling for AI
- Adversarial testing strategies
- Prompt and injection defenses
- Model leakage and theft risks
- Pipeline and API hardening checks
- Resilience patterns and controls
Module 5: Data Risk & Supply Chain Assurance
- Data lineage and provenance
- Consent, purpose, and minimization
- Secure training and evaluation flows
- Component and vendor due diligence
- SBOM/SBOM-like artifacts for models
- Dependency and update governance
Module 6: Audit Execution & Reporting
- Fieldwork techniques and sampling
- Evidence quality and chain of custody
- Issue grading and risk statements
- Action plans and owners
- Metrics, KRIs, and dashboards
- Continuous assurance cadence
Exam Domains:
- AI Risk Governance Principles
- Regulatory Controls Evaluation
- Data Protection and Lifecycle Assurance
- Fairness and Transparency Verification
- Adversarial Robustness and Security Testing
- Audit Reporting, Metrics, and Monitoring
Course Delivery:
The course blends expert-led lectures, interactive discussions, and case-based reviews. Participants use curated readings, checklists, and real-world examples. Templates and rubrics support immediate workplace application.
Assessment and Certification:
Participants complete quizzes, short assignments, and a concise audit-style report. Upon successful completion, participants receive the Certified AI Risk Auditor (CAIRA) certificate by Tonex.
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified AI Risk Auditor (CAIRA) Certification Training exam, candidates must achieve a score of 70% or higher.
Strengthen trust in your AI programs. Earn CAIRA and lead risk-first assurance. Enroll with Tonex and audit AI with confidence.