Certified AI Governance Auditor (C-AIGA) Certification Program by Tonex
This program prepares professionals to audit, assure, and govern AI systems with confidence. You will learn how to translate policy into enforceable controls, maintain a living risk register, evaluate suppliers, and verify audit trails end-to-end.
The curriculum blends governance principles with practical audit mechanics, so you can test what matters and report what counts. Participants build fluency in documenting control objectives, designing evidence requests, and assessing conformance across data, models, and operations. The focus is clear: defensible governance, measurable risk reduction, and credible assurance.
Cybersecurity impact is central. Strong AI governance shrinks attack surfaces, safeguards sensitive data, and prevents unchecked model behaviors that can lead to breaches. You will learn to harden pipelines, trace actions, and preserve chain-of-custody for evidence. The result is audit-ready AI with accountability, transparency, and resilience—aligned to security and compliance expectations.
Learning Objectives:
- Translate AI policies into testable controls
- Build and maintain an AI risk register with ownership and SLAs
- Assess supplier and third-party AI risks
- Design evidence requests and evaluate audit trails
- Map controls to regulations and standards
- Report findings with clear remediation paths
Audience:
- Cybersecurity Professionals
- IT Risk and Compliance Managers
- Internal/External Auditors
- Data Governance Leads
- AI/ML Engineering Managers
- Procurement and Vendor Risk Analysts
Program Modules:
Module 1: AI Governance Foundations
- Scope AI systems, stakeholders, and accountability
- Governance models and lines of defense
- Policy stack: principles, standards, procedures
- Control objectives for data, models, and runtime
- Documentation baselines and versioning
- Governance metrics and oversight cadence
Module 2: Policy and Control Architecture
- Drafting AI policy statements and exceptions
- Control design, preventive vs. detective
- Segregation of duties and approvals
- Data handling, retention, and access rules
- Model lifecycle gates and sign-offs
- Control mapping to frameworks and laws
Module 3: Risk Register & Impact Analysis
- Risk taxonomy for AI/ML use cases
- Likelihood, impact, and residual risk scoring
- Owners, mitigations, and acceptance criteria
- Triggers for risk updates and reviews
- Key risk indicators and thresholds
- Board-level summaries and dashboards
Module 4: Supplier & Third-Party Assurance
- Due diligence for AI vendors and APIs
- Contractual controls and audit rights
- Secure integration and data sharing terms
- Continuous monitoring and attestations
- Sub-processor transparency and change control
- Exit, termination, and data return plans
Module 5: Audit Trails, Evidence & Reporting
- Evidence types, chain-of-custody, and storage
- End-to-end traceability across data and models
- Sampling, walkthroughs, and re-performance
- Ticketing, deviations, and corrective actions
- Drafting issues, severity, and owners
- Final report, management response, and follow-up
Module 6: Operational Oversight & Continuous Assurance
- Controls testing schedules and independence
- Runtime monitoring and alert governance
- Incident intake, triage, and lessons learned
- Change management and model drift reviews
- Metrics for effectiveness and maturity
- Program improvement roadmap and audits calendar
Exam Domains:
- AI Governance Standards Alignment
- Algorithmic Risk Quantification and Controls
- Third-Party and Supply Chain Assurance
- Evidence Management and Traceability
- Regulatory Readiness and Compliance Reporting
- Ethical Oversight, Accountability, and Transparency
Course Delivery:
The course uses lectures, interactive discussions, case studies, and guided walkthroughs led by Tonex experts in AI governance. Participants gain access to curated resources, templates, and checklists for policies, risk registers, supplier reviews, and audit evidence packages.
Assessment and Certification:
Participants are evaluated through quizzes, assignments, and a capstone project. Upon successful completion, learners receive the Certified AI Governance Auditor (C-AIGA) certificate from Tonex.
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified AI Governance Auditor (C-AIGA) Certification Program by Tonex exam, candidates must achieve a score of 70% or higher.
Ready to lead trustworthy AI audits? Enroll now to validate your skills, strengthen your organization’s assurance posture, and advance your career with Tonex.