Certified Human-Centric AI Governance Professional (CHCAIG) Certification Program by Tonex
Certified Human-Centric AI Governance Professional (CHCAIG) empowers policymakers and executives to run AI programs where humans remain accountable. The program converts principles into operating models, controls, and decision rights. Learn how to layer accountability, set transparency requirements, and align with international governance models. Content is pragmatic. Templates and checklists turn policy into action. Map responsibilities, define escalation paths, and embed oversight in strategy, products, and operations.
Cybersecurity is baked in. Connect AI governance with security controls, logging, and incident response. Plan defensible audit trails. Reduce attack surface from data flows, third parties, and rapid releases. Build trustworthy AI that resists misuse and accelerates approvals.
The course is vendor-neutral and sector-agnostic. Case studies span public and private domains. Outcomes include a governance charter, a RACI for oversight, a transparency plan, and measurable KPIs. Graduates lead cross-functional efforts, brief boards, and engage regulators with confidence. The emphasis is clarity, accountability, and measurable risk reduction.
Learning Objectives:
- Define human accountability layers across the AI lifecycle
- Establish transparency and disclosure requirements
- Map roles, RACI, and escalation for oversight
- Align programs to global governance models
- Integrate AI governance with cybersecurity controls
- Build metrics for continuous improvement
Audience:
- Policymakers and regulators
- Executives and board advisors
- Compliance and risk leaders
- Data protection and privacy officers
- Legal and ethics professionals
- Cybersecurity professionals
Course Modules:
Module 1: Governance Foundations & Human Accountability
- Accountability hierarchy and decision rights
- Duty of care and fiduciary expectations
- RACI for AI initiatives
- Escalation and sign-off checkpoints
- Oversight committees and charters
- Stakeholder engagement and redress
Module 2: Transparency, Explainability & Documentation
- Transparency requirements and disclosures
- Explainability expectations for rights-holders
- Algorithmic impact assessment artifacts
- Data lineage and traceability notes
- User communications and consent notices
- Public reporting and model registries
Module 3: Risk, Assurance & Control Design
- AI risk taxonomy and severity bands
- Control library and policy mapping
- Human-in-the-loop verifications
- Bias and fairness governance guardrails
- Safety thresholds and kill-switch criteria
- Incident intake and reporting channels
Module 4: International Governance & Cross-Border Operations
- Jurisdictional scanning and gaps
- Data transfer and sovereignty patterns
- Localization and retention rules
- Conflicts-of-law playbooks
- Regulator engagement protocols
- Third-party and vendor oversight
Module 5: Cybersecurity Integration for AI Systems
- Threat modeling for AI services
- Secure SDLC with AI components
- Logging, audit trails, and evidence
- Access, secrets, and key governance
- Adversarial robustness expectations
- Response, recovery, and post-mortems
Module 6: Implementation, Metrics & Continuous Improvement
- Operating model and staffing patterns
- KPIs, KRIs, and thresholds
- Audit cadence and assurance plans
- Change management and training
- Procurement and vendor governance
- Maturity roadmap and benchmarking
Exam Domains:
- Human Accountability and Delegation Strategy
- Cross-Jurisdiction Regulatory Compliance
- Transparency and Recordkeeping Standards
- AI Risk Ethics and Impact Assessment
- Security Governance for AI Programs
- Audit, Assurance, and Continuous Monitoring
Course Delivery:
Expert-led lectures, interactive discussions, case studies, and policy clinics. Participants receive curated readings, templates, and checklists to operationalize governance.
Assessment and Certification:
Quizzes, structured assignments, and a capstone policy brief. Upon successful completion, participants receive the CHCAIG certificate from Tonex.
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the CHCAIG Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to lead accountable AI at scale? Enroll in CHCAIG by Tonex. Build trust, meet global expectations, and protect your organization.