AI Risk Management Foundation Course

The AI Risk Management Foundation course provides a comprehensive introduction to the principles, frameworks, and best practices for managing risks associated with Artificial Intelligence (AI) systems. As organizations increasingly integrate AI into their operations, it becomes essential to understand and mitigate the unique risks these technologies present.

This foundation-level course is designed for professionals seeking to develop a solid understanding of AI risk management, including data privacy, model bias, accountability, transparency, and regulatory compliance. The course aligns with global standards and frameworks such as the AI Risk Management Framework (AI RMF) and other emerging industry guidelines.

Training Syllabus

Day 1: Understanding AI Risks & Governance Frameworks

Module 1: Introduction to AI Risk Landscape

1. Overview of AI risk categories: bias, privacy, cybersecurity, job displacement, environmental, misinformation, existential/AGI risks
2. Discussion on “present harms” (discrimination, surveillance) vs “future harms” (autonomous weapons, AGI takeover)
3. Case Study: Algorithmic hiring tool that screens out female candidates; root causes, impact, and remediation.

Module 2: Ethical, Legal & Regulatory Dimensions

1. Ethical challenges: bias, transparency, accountability
2. Global regulations: EU AI Act, US Executive Order, NIST AI Risk Management Framework
3. Standards: ISO 42001, PECB AI Risk Manager, GARP RAI program
4. Case Study: Deployment of a risk-parity credit scoring AI; mapping risks against regulatory and ethical frameworks

Module 3: Human-Centric & Skill-Centric Risks

1. Human risk: mental-health effects, deskilling, loss of empathy
2. Skill risk: workforce transformation, required human-centric skills like empathy and adaptability
3. Strategies: reskilling, upskilling, human-AI collaboration frameworks
4. Case Study: Customer service chatbot causing user frustration; impact on human jobs, emotional intelligence gaps

Module 4: Technical & Security Risks

1. Cybersecurity threats: AI-powered attacks, adversarial ML
2. Dual-use risks: biosecurity (pathogen design), autonomous weapons, deepfakes
3. Model hazards: lack of transparency, explainability, uncontrollability
4. Case Study: Sophisticated phishing attack using LLMs; detecting, responding, preventing

Day 2: Risk Management Practices & Organizational Readiness

Module 5: AI Risk Governance & Framework Application

1. Balanced governance: oversight, auditing, third-party validation
2. Introduction to NIST AI RMF, IEEE 7000 series and ISO 42001
3. Roles & responsibilities: IT, legal, HR, operations, C‑suite
4. Case Study: Implementing NIST RMF in a financial services firm, from risk identification to continuous monitoring

Module 6: Standards & Frameworks in AI Risk Management

1. ISO/IEC 23894:2023 – Guidance on AI Risk Management
2. ISO/IEC TR 24027:2021 – Bias in AI Systems
3. ISO/IEC TR 24028:2020 – Trustworthiness & Resilience
4. ISO/IEC TR 24029‑1:2021 – Explainability Metrics
5. IEEE 7000 Series – Ethical System Design (Transparency, Bias, Agency, Well-being, Data Governance)
6. NIST AI RMF 1.0 (2023)

Module 7: Risk Assessment & Mitigation Techniques

1. Risk assessment process: identify, evaluate, and prioritize AI risks
2. Mitigation: bias audits, impact assessments, red-teaming, human-in-the-loop interventions, transparency reporting
3. Case Study: Bias audit of a credit recommendation model; finding biases and applying corrective actions

Module 8: Organizational and Cultural Integration

1. Building AI risk-aware culture: cross-functional collaboration, training, communication
2. Change management: countering AI resistance, fostering trust, embedding human-centric skill development
3. Case Study: Company-wide rollout of AI analytics tool; training, monitoring, and employee feedback loops

Module 9: Futureproofing & Emerging Threats

1. Existential risks and AGI considerations: governance lessons from arms race and runaway AI
2. Anticipating surprises: environment, animal welfare, sentient AI
3. Readiness strategies: horizon scanning, partnerships with NGOs and governments
4. Case Study: AI arms-race scenario between rival firms; risk of rushing unsafe deployment and mitigation options

 Module 10: Case Study

Case Study – Global Bank Chatbot: A multinational bank deployed an AI chatbot for customer service. Using ISO 23894, they identified data drift risks during promotions, assessed stakeholder concerns (age bias), continuously monitored model performance, and integrated stakeholder feedback into design, achieving 15% reduction in misdirected service calls

Case Study – E-commerce Pricing Engine: An online retailer found its AI pricing model disadvantaged users from certain zip codes. Applying ISO 24027 methods, they audited training data, rebalanced cohorts, added fairness constraints, and validated re-training impact, leading to equitable pricing and improved public perception

Case Study – Industrial Robotic System: A manufacturing firm’s AI-controlled robot experienced desynchronization under sudden sensor faults. Leveraging ISO 24028, they implemented adversarial simulation, fail-safe modules, and fallback strategies, preventing costly downtime

Case Study – Credit Approval ML Model: A fintech company used explainability scores to benchmark model outputs. By applying ISO 240291 evaluation, they discovered lower explainability for minority group cases, adjusted model design, and improved transparency, boosting user trust and regulatory acceptance

Case Study – Workday HR AI Platform: Workday mapped RMF to its internal security/privacy controls, created cross-functional risk team, enabled modular oversight; resulting in transparent hiring recommendations and continuous monitoring of bias/drift within application UI