Trust Me - AI Risk Management

Do you know what your AI is doing? One flawed algorithm can cost billions, damage reputations overnight, or put human lives at risk. From biased hiring tools to autonomous vehicles gone wrong, recent crises prove that artificial intelligence carries risks as powerful as its promise. That is why ISO 23894 — the international standard for AI risk management — has become essential for organizations that want to innovate responsibly.In Trust Me: AI Risk Management, you’ll find clear strategies, practical tools, and actionable insights to identify, assess, and control AI risks before they spiral out of control. This book gives leaders, auditors, and practitioners the roadmap to build resilient, trustworthy AI systems — and the confidence to manage uncertainty in an AI-driven world.Trust Me: AI Risk ManagementThis book covers the following AI risk management and risk-controls material:AI Uncertainty: Uncertainty is today’s ‘new normal’ in a world due to the complex and rapidly changing nature of our interconnected world. Navigating this uncertainty requires adaptability, resilience, and the ability to make risk-based decisions in the face of incomplete information.ISO 31000 Risk Management Principles: ISO 31000 is a risk management standard for the process of identifying, assessing, and managing risks. ISO 31000 Risk Management Framework: Risk management framework is a set of processes and procedures that helps companies to identify, assess, and manage risks. AI Risk Principles: AI trust involves many of its characteristics that are based on context. AI First Enterprise: Every company will shortly become an AI enterprise. An AI enterprise is a company that is focused on the development and use of AI. AI companies are found in a variety of industries including healthcare, finance, and manufacturing.AI Governance: AI governance is the risk management framework, risk processes, and risk policies put in place to assure responsible and ethical AI development, deployment, and use of systems.AI Context: AI context includes the environment and stakeholders involved in AI development and use. These include AI developers, users, and other impacted humans.AI Risk Assessment: AI risk assessment is the process of identifying, evaluating, and analyzing potential AI system risks. It involves examining the risks posed by AI technologies to understand their risk likelihood and potential consequences for various stakeholders, systems, and organizations. AI risk assessment involves three elements: 1. Identify AI risks; 2. Evaluate AI risks; and 3. Analyze AI risks.AI Risk Appetite: AI risk appetite is an organization's willingness to accept and tolerate certain levels of risk with the development, deployment, and use of AI systems. Or expressed in another way, it represents the extent to which an organization is willing to take on risks in pursuit of its AI objectives.AI Risk Treatment: AI risk treatment is the process of implementing measures, risk-controls, and actions to manage, mitigate, or control risks with AI systems. Risk treatment measures will depend on the nature of the AI system, the identified risks, the organization's risk appetite, regulatory requirements, and ethical considerations.AI Conformity Assessment: AI conformity assessment is a process of evaluating whether an AI system complies with relevant laws, regulations, and technical standards. Conformity assessment is the basis for trusting AI software and systems. It is an important part of any enterprise's AI risk management program and framework.AI Futures: AI involves many present and future challenges. Read more