Organizations of all sizes are increasing their investment in AI products, tools and developments. However, these transformative technologies come with significant risks and challenges. This actionable guide builds scalable and practical AI governance program that encourages innovation and minimizes risks. Practical AI Governance will show senior AI, tech and businesses leaders how to design an AI governance program built on proactive engagement, centralized intelligence and adaptive governance, all driven by continuous monitoring. Providing a strategic oversight structure to evaluate and…mehr
Organizations of all sizes are increasing their investment in AI products, tools and developments. However, these transformative technologies come with significant risks and challenges. This actionable guide builds scalable and practical AI governance program that encourages innovation and minimizes risks. Practical AI Governance will show senior AI, tech and businesses leaders how to design an AI governance program built on proactive engagement, centralized intelligence and adaptive governance, all driven by continuous monitoring. Providing a strategic oversight structure to evaluate and implement AI governance, the book covers the building blocks to creating an AI governance program, the necessary structures and tools for oversight and how to future proof an organization's efforts. Suitable for any business, this book will demonstrate how to align AI strategies with the needs of multiple stakeholders, the changing regulatory environment, technological trends and an organization's innovation and risk appetite. It explains how to embed AI governance in existing cybersecurity and risk management frameworks and scale AI governance across an organization. Going beyond traditional compliance checklists, it instead offers a logical and holistic approach to governance.
Shoshana Rosenberg is an experienced AI governance thought leader and practitioner. She is the Chief AI Governance and Privacy Officer at WSP. She is the co-founder of the organization Women in AI Governance which works to advance equity in AI policy and leadership and is the founder of SafePorter, an award-winning SaaS data minimization solution. Shoshana Rosenberg is an advisor to the Institute of Operational Privacy by Design and is based in New York, NY.
Inhaltsangabe
Section ONE: Foundations of holistic AI governance; Chapter 01: The AI governance imperative; Chapter 02: Understanding the AI governance ecosystem; Chapter 03: Data governance the bedrock of AI governance; Chapter 04: Governance as a cultural framework; Chapter 05: AI foundations for governance: models, capabilities and trade offs; Section TWO: Strategic foundations core building blocks for AI governance; Chapter 06: The structural foundations of AI governance; Chapter 07: Proactive engagement ensuring AI governance reflects operational reality; Chapter 08: Centralized intelligence tracking AI and AI governance issues in one system; Chapter 09: Adaptive governance ensuring oversights evolves as AI expands; Chapter 10: Continuous monitoring tracking AI governance effectiveness at the program level; Section THREE: Strategic oversights: PRISM as mechanism for building and evaluating AI governance; Chapter 11: PRISM a structured approach to building and evaluating AI governance; Section FOUR: Operationalizing AI governance structures, tools, and oversights mechanisms; Chapter 12: Anchoring AI governance in organizational structures; Chapter 13: Assessing data risks to govern AI use, development and deployment; Chapter 14: Aligning AI governance with business strategy and risk appetite; Section 15: Tracking AI enabled tools, systems and vendor deployments; Chapter 16: Implementing the AI intake process governing AI from the start; Chapter 17: AI governance policies, guidelines and enforcement mechanisms; Chapter 18: Upskilling leadership and key staff for AI governance responsibilities; Chapter 19: Incident response: structuring oversight for AI failures; Chapter 20: Systems level continuing monitoring and adaptive AI governance; Section FIVE: Scaling and future proofing AI governance; Chapter 21: Scaling AI governance across business units and global operations; Chapter 22: Measuring AI governance effectiveness and maturity; Chapter 23: Preparing for regulatory evolution and multi jurisdictional compliance; Chapter 24: Integrating AI governance with enterprise risk and cybersecurity programs; Chapter 25: Future proofing AI governance against emerging technologies and risks
Section ONE: Foundations of holistic AI governance; Chapter 01: The AI governance imperative; Chapter 02: Understanding the AI governance ecosystem; Chapter 03: Data governance the bedrock of AI governance; Chapter 04: Governance as a cultural framework; Chapter 05: AI foundations for governance: models, capabilities and trade offs; Section TWO: Strategic foundations core building blocks for AI governance; Chapter 06: The structural foundations of AI governance; Chapter 07: Proactive engagement ensuring AI governance reflects operational reality; Chapter 08: Centralized intelligence tracking AI and AI governance issues in one system; Chapter 09: Adaptive governance ensuring oversights evolves as AI expands; Chapter 10: Continuous monitoring tracking AI governance effectiveness at the program level; Section THREE: Strategic oversights: PRISM as mechanism for building and evaluating AI governance; Chapter 11: PRISM a structured approach to building and evaluating AI governance; Section FOUR: Operationalizing AI governance structures, tools, and oversights mechanisms; Chapter 12: Anchoring AI governance in organizational structures; Chapter 13: Assessing data risks to govern AI use, development and deployment; Chapter 14: Aligning AI governance with business strategy and risk appetite; Section 15: Tracking AI enabled tools, systems and vendor deployments; Chapter 16: Implementing the AI intake process governing AI from the start; Chapter 17: AI governance policies, guidelines and enforcement mechanisms; Chapter 18: Upskilling leadership and key staff for AI governance responsibilities; Chapter 19: Incident response: structuring oversight for AI failures; Chapter 20: Systems level continuing monitoring and adaptive AI governance; Section FIVE: Scaling and future proofing AI governance; Chapter 21: Scaling AI governance across business units and global operations; Chapter 22: Measuring AI governance effectiveness and maturity; Chapter 23: Preparing for regulatory evolution and multi jurisdictional compliance; Chapter 24: Integrating AI governance with enterprise risk and cybersecurity programs; Chapter 25: Future proofing AI governance against emerging technologies and risks
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