Ai Rmf A Practical Guide For Nist Ai Risk Management Framework
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AI-RMF a Practical Guide for NIST AI Risk Management Framework
Unlock the Power of Responsible AI with "AI-RMF: A PracticalGuide for NIST AI Risk Management Framework".As artificial intelligence (AI) systems become increasinglyintegrated into our daily lives, organizations face the criticalchallenge of managing the associated risks and ensuring thetrustworthy development and deployment of AI technologies."AI-RMF: A Practical Guide" is your comprehensive handbook fornavigating the complexities of AI risk management using theNational Institute of Standards and Technology's ArtificialIntelligence Risk Management Framework (AI-RMF).This book offers a deep dive into the AI-RMF, providing step-by-step guidance on implementing this powerful framework acrossvarious industries. You'll explore the history and evolution of AIrisk management, understand the key components of the AI-RMF,and learn practical strategies for applying the framework to yourorganization's unique needs.Whether you're an AI developer, data scientist, securityprofessional, business leader, or system engineer, this book isyour essential guide to operationalizing AI risk management andunlocking the full potential of AI while safeguarding yourorganization and stakeholders.
Unveiling the NIST Risk Management Framework (RMF)
Author: Thomas Marsland
language: en
Publisher: Packt Publishing Ltd
Release Date: 2024-04-30
Gain an in-depth understanding of the NIST Risk Management Framework life cycle and leverage real-world examples to identify and manage risks Key Features Implement NIST RMF with step-by-step instructions for effective security operations Draw insights from case studies illustrating the application of RMF principles in diverse organizational environments Discover expert tips for fostering a strong security culture and collaboration between security teams and the business Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis comprehensive guide provides clear explanations, best practices, and real-world examples to help readers navigate the NIST Risk Management Framework (RMF) and develop practical skills for implementing it effectively. By the end, readers will be equipped to manage and mitigate cybersecurity risks within their organization. What you will learn Understand how to tailor the NIST Risk Management Framework to your organization's needs Come to grips with security controls and assessment procedures to maintain a robust security posture Explore cloud security with real-world examples to enhance detection and response capabilities Master compliance requirements and best practices with relevant regulations and industry standards Explore risk management strategies to prioritize security investments and resource allocation Develop robust incident response plans and analyze security incidents efficiently Who this book is for This book is for cybersecurity professionals, IT managers and executives, risk managers, and policymakers. Government officials in federal agencies, where adherence to NIST RMF is crucial, will find this resource especially useful for implementing and managing cybersecurity risks. A basic understanding of cybersecurity principles, especially risk management, and awareness of IT and network infrastructure is assumed.
Responsible AI in Practice
This book is the first practical book on AI risk assessment and management. It will enable you to evaluate and implement safe and accurate AI models and applications. The book features risk assessment frameworks, statistical metrics and code, a risk taxonomy curated from real-world case studies, and insights into AI regulation and policy, and is an essential tool for AI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities. The centerpiece of this book is a risk management and assessment framework titled “Safe Human-centered AI (SAFE-HAI),” which highlights AI risks across the following Responsible AI principles: accuracy, sustainability and robustness, explainability, transparency and accountability, fairness, privacy and human rights, human-centered AI, and AI governance. Using several statistical metrics such as Area Under Curve (AUC), Rank Graduation Accuracy, and Shapley values, you will learn to apply Lorenz curves to measure risk and inequality across the different principles and will be equipped with a taxonomy/scoring rubric to identify and mitigate identified risks. This book is a true practical guide and covers a real-world case study using the proposed SAFE-HAI framework. The book will help you adopt standards and voluntary codes of conduct in compliance with AI risk and safety policies and regulations, including those from the NIST (National Institute of Standards and Technology) and EU AI Act (European Commission). What You Will Learn Know the key principles behind Responsible AI and associated risks Become familiar with risk assessment frameworks, statistical metrics, and mitigation measures for identified risks Be aware of the fundamentals of AI regulations and policies and how to adopt them Understand AI governance basics and implementation guidelines Who This Book Is For AI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities