Skilled Artificial Intelligence Risk Analyst

• AI risk ecosystem and organizational assessment framework
• Identification of key stakeholders in AI adoption
• Initial assessment and diagnostic approaches (controls and remediations)
• Technological challenges in starting AI adoption

Exercise: Discovering the overall risk landscape

• Governance structures and ethical leadership in AI
• Definition of roles, accountability, and decision-making
• Types of governance policies and ethics committees
• Governance frameworks (ISO, NIST)

Exercise: AI governance baseline

• Tecnical and operational risk at each stage of the lifecycle
• Risk management responsibilities by stage
• Risk assessment structure
• Tools for AI risk management

Exercise: AI risk treatment plan

• Ethical principles applicable to AI and their social impact
• Promotion of inclusion, equity, and non-discrimination
• Codes of conduct and review of ethical dilemmas
• Suggested ethical tools (UNESCO Guidelines, AI Ethics Impact Assessment, RAIS)

Exercise: Applying ethical frameworks to an AI risk

• Traceability and understanding of automated decisions
• Roles to ensure technical and organizational explainability
• Suggested tools for monitoring

Exercise: Controls, traceability, and explainability in an AI architecture

• Responsible handling of personal data in AI systems
• Roles for compliance with privacy regulations
• Privacy mechanisms: informed consent and anonymization
• Suggested tools: DPIA and compliance checklists (GDPR, ISO/IEC 27701)

Exercise: Privacy gap analysis in an AI scenario

• Protection of AI systems against technical threats
• Roles in vulnerability management and operational continuity
• Authentication controls, monitoring, and encryption
• OWASP AI Security, NIST Cybersecurity Framework

Exercise: Identification of vulnerabilities in AI solutions

• Model validation, accuracy, and robustness
• Roles in quality assurance for data and algorithms
• Controls for performance testing and data review
• Benchmarking and performance metrics

Exercise: Reliability evaluation of AI models

• Identification and mitigation of algorithmic bias
• Roles promoting fair and equitable decision-making
• Bias audits and model retraining
• Fairness toolkits (AI Fairness 360, What-If Tool)

Exercise: Bias detection in simulated scenarios

• Local and international regulations applicable to AI
• Roles for legal and contracts in AI projects
• Controls for legal impact assessments and contractual clauses
• Regulatory mapping and compliance guidance (EU AI Act, ISO/IEC 42001)

Exercise: Mapping regulatory requirements in practical cases