AI Risk Management, Analysis, and Assessment.

The discussion advances into rigorous risk analysis techniques, combining quantitative and qualitative approaches such as probabilistic risk assessment, scenario simulation, and bias audits. AI-specific modeling techniques including causal networks, Monte Carlo simulations, and agent-based models are explored, highlighting tools to detect and mitigate bias and fairness issues while improving explainability.

Frameworks and standards like NIST AI RMF, ISO/IEC guidelines, and OECD principles provide structured approaches to risk assessment, while operational practices and toolkits integrate risk considerations directly into AI development pipelines.

Governance sections detail internal structures, accountability mechanisms, and legal challenges including cross-border compliance, data protection, and liability. Third-party and supply chain risks emphasize the complexity of AI ecosystems.

Industry-focused chapters explore sector-specific risks in healthcare, finance, and defense, illustrating practical applications and regulatory requirements.

Finally, the book addresses emerging risks from generative AI, autonomous agents, and AI-enhanced cyber threats, as well as the profound challenges posed by AGI. It advocates for resilience engineering, human-centered design, and multi-stakeholder governance to build trustworthy AI and ensure responsible innovation in an uncertain future.