Certified Responsible AI Governance & Ethics (C|RAGE) Training in New Zealand

The Certified Responsible AI Governance & Ethics (C|RAGE) by EC-Council is a globally recognized program designed for professionals who aim to govern, audit, and guide AI systems responsibly.
It empowers leaders to build AI frameworks grounded in ethics, transparency, fairness, and accountability — ensuring compliance and trust in AI-driven organizations.

Upon passing the final exam, participants receive the EC-Council Certified Responsible AI Governance & Ethics (C|RAGE) certification — validating expertise in AI ethics, governance, and responsible leadership.  

Bilginç IT Academy is an Official Accredited Training Partner of EC-Council, delivering EC-Council-authorized cybersecurity trainings and certification programs globally.

• Executives and decision-makers (C-suite, compliance, HR)

• Data scientists, AI strategists, and ethics professionals

• Policy makers, legal advisors, and risk officers

• Leaders managing AI transformation programs

Upon completion, participants will:

• Develop frameworks for responsible and ethical AI governance

• Identify and mitigate ethical and regulatory risks

• Align AI practices with global regulations (AI Act, GDPR, ISO, NIST)

• Design transparent, explainable, and auditable AI systems

• Lead organizational AI ethics and compliance initiatives

• Establish AI ethics boards and governance policies

1. Introduction to AI Ethics and Governance

• Principles of responsible AI

• Ethical risks and governance models

• Transparency and accountability in AI systems

2. Global Regulations and Standards

• EU AI Act and compliance requirements

• GDPR, data privacy, and consent management

• ISO/IEC 42001 and NIST AI RMF integration

3. Corporate AI Governance Frameworks

• Designing AI governance models

• AI Ethics Committees and decision-making protocols

• Policy lifecycle and audit mechanisms

4. Managing Ethical AI Risks

• Bias detection and mitigation in datasets and models

• Risk mapping and ethical impact assessments

• Monitoring AI lifecycle for ethical compliance

5. Secure and Explainable AI

• Explainable AI (XAI) and transparency tools

• Secure AI design and accountability layers

• Human oversight and responsibility models

6. Building an Ethical AI Culture

• Institutionalizing responsible AI practices

• Training and awareness programs

• Organizational maturity in AI ethics