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AI Risk Management Framework: A Critical Analysis of its Impact on Current Trends
Author: Dr. Evelyn Reed, PhD in Computer Science with a specialization in AI Ethics and Risk Management, and 15 years of experience in developing and implementing AI safety protocols for Fortune 500 companies.
Publisher: MIT Press, a reputable academic publisher known for its high-quality publications in computer science, engineering, and related fields. Their commitment to peer review ensures the reliability and credibility of their publications.
Editor: Dr. Anya Sharma, PhD in Data Science with extensive experience in editing scholarly articles focusing on AI ethics and responsible innovation.
Keywords: AI risk management framework, AI safety, AI ethics, AI governance, responsible AI, AI regulation, algorithmic bias, AI accountability, AI explainability, AI risk assessment
Introduction: Navigating the Complex Landscape of AI Risk
Artificial intelligence (AI) is rapidly transforming industries and society, offering unprecedented opportunities while simultaneously posing significant risks. The development and deployment of AI systems necessitate a robust and comprehensive AI risk management framework to mitigate potential harms. This framework must address a diverse range of risks, from algorithmic bias and data privacy violations to unintended consequences and existential threats. This analysis critically examines current AI risk management frameworks, assessing their effectiveness in navigating the complex landscape of AI-related challenges and their impact on prevailing trends.
The Evolution of AI Risk Management Frameworks
Early approaches to AI safety focused primarily on technical safeguards, such as robust testing and validation procedures. However, the increasing sophistication and pervasiveness of AI systems have highlighted the need for a more holistic AI risk management framework encompassing ethical, legal, and societal considerations. Current frameworks strive to integrate these diverse aspects, encompassing:
Risk Identification and Assessment: This crucial initial step involves identifying potential risks associated with the development, deployment, and use of AI systems. This includes assessing the likelihood and impact of various risks, such as bias, discrimination, privacy breaches, and security vulnerabilities. The complexity of this phase often necessitates specialized tools and techniques.
Risk Mitigation and Control: Once risks are identified and assessed, appropriate mitigation strategies must be implemented. This might involve using techniques like differential privacy to protect sensitive data, incorporating fairness constraints into algorithms, or implementing robust security measures. The choice of mitigation strategy depends heavily on the specific risk identified and the context of its deployment.
Monitoring and Evaluation: Continuous monitoring of AI systems is crucial for detecting and addressing emerging risks. This involves tracking system performance, analyzing data for bias, and responding to incidents promptly. Regular evaluation of the effectiveness of the AI risk management framework itself is essential for continuous improvement.
Transparency and Explainability: Understanding how AI systems arrive at their decisions is vital for building trust and ensuring accountability. Explainable AI (XAI) techniques are essential components of any effective AI risk management framework, allowing for scrutiny and identification of potential biases or errors.
Governance and Accountability: Clear lines of responsibility and accountability are crucial for ensuring responsible AI development and deployment. This requires establishing clear governance structures, defining roles and responsibilities, and developing mechanisms for addressing AI-related incidents.
Current Trends and Challenges in AI Risk Management
Several key trends are shaping the evolution of AI risk management frameworks:
Increased Regulatory Scrutiny: Governments worldwide are increasingly recognizing the need for regulations to govern the development and use of AI systems. These regulations often mandate the implementation of robust AI risk management frameworks to ensure AI systems are deployed responsibly. The specific regulatory landscape varies significantly across jurisdictions, creating challenges for organizations operating globally.
Focus on Explainability and Transparency: The demand for explainable AI is growing, driven by concerns about bias, fairness, and accountability. This trend is pushing the development of new techniques and tools for making AI systems more transparent and understandable.
Emphasis on Human Oversight: There is a growing recognition of the need for human oversight in AI systems to prevent unintended consequences and ensure alignment with human values. This includes designing systems that allow for human intervention and control, as well as establishing clear procedures for human review and approval.
Data Privacy and Security: Protecting data privacy and security is paramount in AI development. This necessitates the implementation of robust data protection measures, adherence to privacy regulations, and the development of secure AI systems.
The Rise of AI-Specific Insurance: As the risks associated with AI become more apparent, there is a growing demand for insurance products specifically designed to cover AI-related liabilities. This reflects the increasing awareness of the potential financial consequences of AI failures.
Gaps and Limitations of Current AI Risk Management Frameworks
Despite significant progress, current AI risk management frameworks face several challenges:
Lack of Standardization: The absence of standardized methodologies for AI risk assessment and management hinders effective comparison and benchmarking across organizations and industries.
Difficulty in Quantifying Risks: Assessing and quantifying the risks associated with complex AI systems can be challenging, particularly when considering long-term impacts and unforeseen consequences.
Limited Resources and Expertise: Implementing robust AI risk management frameworks requires significant resources and specialized expertise, which may be inaccessible to smaller organizations.
Evolving Nature of AI: The rapid pace of AI development makes it difficult to keep up with emerging risks and adapt existing frameworks accordingly. The field's rapid evolution necessitates iterative refinement and updates to remain relevant.
Ethical Considerations: Addressing ethical concerns, such as bias, fairness, and accountability, requires careful consideration of societal values and potential impacts on different groups.
Conclusion: Towards a More Robust and Effective AI Risk Management Framework
The development and deployment of AI systems present both immense opportunities and significant risks. A comprehensive and effective AI risk management framework is crucial for harnessing the benefits of AI while mitigating potential harms. While significant progress has been made, ongoing efforts are needed to address the limitations of current frameworks. This includes promoting standardization, developing better methods for quantifying risks, improving access to resources and expertise, and incorporating ethical considerations into the design and implementation of AI systems. The future of AI hinges on a collaborative approach that fosters responsible innovation and ensures the safe and beneficial integration of AI into society.
FAQs
1. What is the difference between AI safety and AI risk management? AI safety focuses on preventing catastrophic outcomes from advanced AI, while AI risk management addresses a broader range of risks associated with AI systems, including bias, privacy violations, and economic disruption.
2. How can I implement an AI risk management framework in my organization? Start by identifying potential risks, assessing their likelihood and impact, developing mitigation strategies, establishing monitoring procedures, and defining roles and responsibilities. Consider engaging external experts for guidance.
3. What are the key regulatory developments in AI risk management? Regulations vary across jurisdictions but often focus on data privacy, algorithmic transparency, and accountability for AI-driven decisions. Staying informed about relevant regulations is crucial.
4. What role does explainable AI (XAI) play in risk management? XAI helps understand how AI systems arrive at their decisions, allowing for detection and mitigation of bias, errors, and other risks.
5. How can we address algorithmic bias in an AI risk management framework? Employ techniques like data augmentation, fairness-aware algorithms, and regular audits to detect and mitigate bias throughout the AI lifecycle.
6. What are the ethical considerations involved in AI risk management? Ethical considerations include fairness, transparency, accountability, privacy, and the potential impact on human autonomy and employment.
7. What are the challenges in quantifying AI risks? Many AI risks are complex, uncertain, and difficult to predict, making accurate quantification challenging. Qualitative risk assessments often complement quantitative methods.
8. How can organizations ensure sufficient resources for AI risk management? Prioritizing AI risk management as a strategic initiative, allocating adequate budgets, and investing in employee training are crucial steps.
9. What is the role of insurance in AI risk management? AI-specific insurance products can help organizations mitigate financial risks associated with AI failures and liabilities.
Related Articles:
1. "AI Risk Management: A Practical Guide for Organizations": This article provides a practical, step-by-step guide to implementing an AI risk management framework, including practical examples and case studies.
2. "Ethical Considerations in AI Development and Deployment": This paper explores the ethical challenges of AI, such as bias, fairness, and accountability, and their implications for AI risk management.
3. "The Role of Explainable AI (XAI) in Mitigating AI Risks": This article examines the importance of XAI in enhancing transparency and understanding of AI systems, thus contributing to effective risk management.
4. "Regulatory Landscape for AI: A Global Overview": This article provides a comprehensive overview of the current and emerging regulations related to AI across different countries and regions.
5. "AI Risk Assessment Methodologies: A Comparative Analysis": This paper compares and contrasts different methodologies for assessing risks associated with AI systems.
6. "Data Privacy and Security in AI Systems: Best Practices and Challenges": This article focuses on securing data and ensuring compliance with privacy regulations in the context of AI development.
7. "Building Trustworthy AI: A Framework for Responsible Innovation": This paper outlines a framework for developing and deploying AI systems responsibly, emphasizing ethical considerations and societal impacts.
8. "The Future of AI Risk Management: Emerging Trends and Challenges": This article explores future trends in AI risk management, such as the increasing role of AI in managing AI risks.
9. "Case Studies in AI Risk Management: Lessons Learned and Best Practices": This article presents case studies of organizations that have successfully implemented AI risk management frameworks, highlighting lessons learned and best practices.
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ai risk management framework: Human Factors in Cybersecurity Abbas Moallem, 2024-07-24 Proceedings of the 15th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences, Nice, France, 24-27 July 2024. |
ai risk management framework: Artificial Intelligence and Human Performance in Transportation Dimitrios Ziakkas, Anastasios Plioutsias, 2024-10-30 Artificial Intelligence (AI) is a major technological advancement in the 21st century. With its influence spreading to all aspects of our lives and the engineering sector, establishing well-defined objectives is crucial for successfully integrating AI in the field of transportation. This book presents different ways of adopting emerging technologies in transportation operations, including security, safety, online training, and autonomous vehicle operations on land, sea, and air. This guide is a dynamic resource for senior management and decision-makers, with essential practical advice distilled from the expertise of specialists in the field. It addresses the most critical issues facing transportation service providers in adopting AI and investigates the relationship between the human operator and the technology to navigate what is and is not feasible or impossible. Case studies of actual implementation provide context to common scenarios in the transportation sector. This book will serve the reader as the starting point for practical questions regarding the deployment and safety assurance of new and emergent technologies in the transportation domains. Artificial Intelligence and Human Performance in Transportation is a beneficial read for professionals in the fields of Human Factors, Engineering (Aviation, Maritime and Land), Logistics, Manufacturing, Accident Investigation and Safety, Cybersecurity and Human Resources. |
ai risk management framework: Artificial Intelligence and Ethics Tarnveer Singh, 2024-11-22 Artificial Intelligence and Ethics is a general and wide-ranging survey of the benefits and ethical dilemmas of artificial intelligence (AI). The rise of AI and super-intelligent AI has created an urgent need to understand the many and varied ethical issues surrounding the technologies and applications of AI. This book lays a path towards the benefits and away from potential risks. It includes over thirty short chapters covering the widest array of topics from generative AI to superintelligence, from regulation to transparency, and from cybersecurity to risk management. Written by an award-winning Chief Information Security Officer (CISO) and experienced Technology Leader with two decades of industry experience, the book includes real-life examples and up-to-date references. The book will be of particular interest to business stakeholders, including executives, scientists, ethicists and policymakers, considering the complexities of AI and how to navigate these. |
ai risk management framework: Towards a Knowledge-Aware AI A. Dimou, S. Neumaier, T. Pellegrini, 2022-09-29 Semantic systems lie at the heart of modern computing, interlinking with areas as diverse as AI, data science, knowledge discovery and management, big data analytics, e-commerce, enterprise search, technical documentation, document management, business intelligence, enterprise vocabulary management, machine learning, logic programming, content engineering, social computing, and the Semantic Web. This book presents the proceedings of SEMANTiCS 2022, the 18th International Conference on Semantic Systems, held as a hybrid event – live in Vienna, Austria and online – from 12 to 15 September 2022. The SEMANTiCS conference is an annual meeting place for the professionals and researchers who make semantic computing work, who understand its benefits and encounter its limitations, and is attended by information managers, IT architects, software engineers, and researchers from organizations ranging from research facilities and NPOs, through public administrations to the largest companies in the world. The theme and subtitle of the 2022 conference was Towards A Knowledge-Aware AI, and the book contains 15 papers, selected on the basis of quality, impact and scientific merit following a rigorous review process which resulted in an acceptance rate of 29%. The book is divided into four chapters: semantics in data quality, standards and protection; representation learning and reasoning for downstream AI tasks; ontology development; and learning over complementary knowledge. Providing an overview of emerging trends and topics in the wide area of semantic computing, the book will be of interest to anyone involved in the development and deployment of computer technology and AI systems. |
ai risk management framework: Exploring Ethical Dimensions of Environmental Sustainability and Use of AI Kannan, Hemachandran, Rodriguez, Raul Villamarin, Paprika, Zita Zoltay, Ade-Ibijola, Abejide, 2023-12-07 Exploring Ethical Dimensions of Environmental Sustainability and Use of AI is a comprehensive and insightful book that delves into the ethical implications and challenges that emerge at the intersection of environmental sustainability and the utilization of artificial intelligence (AI). With a focus on key ethical dimensions such as transparency, equity, privacy, autonomy, unintended consequences, and trade-offs, this book aims to provide a thorough understanding of the responsible deployment and development of AI in the realm of environmental sustainability. By addressing the ethical aspects and challenges involved, this book contributes to the development of ethical guidelines and frameworks that align AI technologies with the vision of a sustainable and equitable future. Researchers will find immense value in this book as it offers a holistic exploration of the ethical implications, filling a critical gap in the existing literature. Policymakers can gain valuable insights to inform the creation of ethical guidelines and regulations governing AI use in sustainable initiatives. Practitioners, including professionals working in environmental organizations or technology companies, will acquire practical knowledge to guide their decision-making and implementation of AI-driven solutions. |
ai risk management framework: Machine Learning for High-Risk Applications Patrick Hall, James Curtis, Parul Pandey, 2023-04-17 The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks. This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public. Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security Learn how to create a successful and impactful AI risk management practice Get a basic guide to existing standards, laws, and assessments for adopting AI technologies, including the new NIST AI Risk Management Framework Engage with interactive resources on GitHub and Colab |
ai risk management framework: AI Strategies For Web Development Anderson Soares Furtado Oliveira, 2024-09-30 From fundamental to advanced strategies, unlock useful insights for creating innovative, user-centric websites while navigating the evolving landscape of AI ethics and security Key Features Explore AI's role in web development, from shaping projects to architecting solutions Master advanced AI strategies to build cutting-edge applications Anticipate future trends by exploring next-gen development environments, emerging interfaces, and security considerations in AI web development Purchase of the print or Kindle book includes a free PDF eBook Book Description If you're a web developer looking to leverage the power of AI in your projects, then this book is for you. Written by an AI and ML expert with more than 15 years of experience, AI Strategies for Web Development takes you on a transformative journey through the dynamic intersection of AI and web development, offering a hands-on learning experience.The first part of the book focuses on uncovering the profound impact of AI on web projects, exploring fundamental concepts, and navigating popular frameworks and tools. As you progress, you'll learn how to build smart AI applications with design intelligence, personalized user journeys, and coding assistants. Later, you'll explore how to future-proof your web development projects using advanced AI strategies and understand AI's impact on jobs. Toward the end, you'll immerse yourself in AI-augmented development, crafting intelligent web applications and navigating the ethical landscape.Packed with insights into next-gen development environments, AI-augmented practices, emerging realities, interfaces, and security governance, this web development book acts as your roadmap to staying ahead in the AI and web development domain. What you will learn Build AI-powered web projects with optimized models Personalize UX dynamically with AI, NLP, chatbots, and recommendations Explore AI coding assistants and other tools for advanced web development Craft data-driven, personalized experiences using pattern recognition Architect effective AI solutions while exploring the future of web development Build secure and ethical AI applications following TRiSM best practices Explore cutting-edge AI and web development trends Who this book is for This book is for web developers with experience in programming languages and an interest in keeping up with the latest trends in AI-powered web development. Full-stack, front-end, and back-end developers, UI/UX designers, software engineers, and web development enthusiasts will also find valuable information and practical guidelines for developing smarter websites with AI. To get the most out of this book, it is recommended that you have basic knowledge of programming languages such as HTML, CSS, and JavaScript, as well as a familiarity with machine learning concepts. |
ai risk management framework: A CISO Guide to Cyber Resilience Debra Baker, 2024-04-30 Explore expert strategies to master cyber resilience as a CISO, ensuring your organization's security program stands strong against evolving threats Key Features Unlock expert insights into building robust cybersecurity programs Benefit from guidance tailored to CISOs and establish resilient security and compliance programs Stay ahead with the latest advancements in cyber defense and risk management including AI integration Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis book, written by the CEO of TrustedCISO with 30+ years of experience, guides CISOs in fortifying organizational defenses and safeguarding sensitive data. Analyze a ransomware attack on a fictional company, BigCo, and learn fundamental security policies and controls. With its help, you’ll gain actionable skills and insights suitable for various expertise levels, from basic to intermediate. You’ll also explore advanced concepts such as zero-trust, managed detection and response, security baselines, data and asset classification, and the integration of AI and cybersecurity. By the end, you'll be equipped to build, manage, and improve a resilient cybersecurity program, ensuring your organization remains protected against evolving threats.What you will learn Defend against cybersecurity attacks and expedite the recovery process Protect your network from ransomware and phishing Understand products required to lower cyber risk Establish and maintain vital offline backups for ransomware recovery Understand the importance of regular patching and vulnerability prioritization Set up security awareness training Create and integrate security policies into organizational processes Who this book is for This book is for new CISOs, directors of cybersecurity, directors of information security, aspiring CISOs, and individuals who want to learn how to build a resilient cybersecurity program. A basic understanding of cybersecurity concepts is required. |
ai risk management framework: People’s Republic of China-Hong Kong Special Administrative Region International Monetary Fund. Monetary and Capital Markets Department, 2014-07-16 This Basel Core Principles (BCP) for Effective Banking Supervision Detailed Assessment Report has been prepared in the context of the Financial Sector Assessment Program for the People’s Republic of China–Hong Kong Special Administrative Region (HKSAR). The Hong Kong Monetary Authority (HKMA) supervises a major international financial center which was affected, though not significantly so, by the financial crisis. The HKMA is maintaining its commitment to the international regulatory reform agenda and is an early adopter of many standards. Supervisory practices, standards, and approaches are well integrated, risk based and of very high quality. There is one area in relation to the overarching legislative framework and powers which warrants further attention. The HKMA enjoys clear de facto but not de jure operational independence. There are two important cross border dimensions for Hong Kong as an international financial center. One is related to HKSAR’s significant position as a host supervisor. The second is the increasing importance of Mainland China in the current portfolios and prospects of the locally incorporated institutions, and indeed in the choice of HKSAR as a platform for overseas institutions to establish relationships with Mainland China. |
ai risk management framework: Cybersecurity - It's Not All About Technology: Navigating the Unknown of Cybersecurity, GRC, and AI to Achieve Efficiency, Security, and Increase Revenue Dasha Davies, Most executives say they care about cybersecurity. If that's true, why do we still see so many breaches? And why do data breaches increase every year? Yes, hackers are getting more creative, but security technology is also getting smarter, better, and faster. So what are we missing? In my over 25-year career in cybersecurity, I have noticed a few patterns: The belief that cybersecurity is mostly about technology An overwhelming number of great technology gadgets and pressure to choose the best one Excellent product marketing that promises to solve all or many of our security problems Limited resources, know-how, time, and budget Lack of consideration/implementation of GRC (Governance, Risk, Compliance) Reliance on the IT and security team or your MSP to make everything secure. The complexity and not knowing where to start Yes, it is a puzzle of technology, people, processes, governance, risk, compliance, standards, industry, and legal requirements—no matter what industry you are in, what country you operate in, or where your clients are located. This book is designed to help you understand: What else may I be missing? Why GRC is so important and how to easily implement it How to minimize my AI risks and leverage the opportunities it offers What questions should I ask my internal team and suppliers to understand the gaps and risks? How do we perform internal security, risk, and compliance checks? As a business owner myself, I understand the desire to protect and grow your business. While you are focusing on growth, service, and product delivery, managing your staff, and ensuring your IT is operational, this book will show you areas that you may not have paid enough attention to. These areas are equally important for your business protection and growth. This book will show you how to leverage security, GRC, and AI to your benefit to grow, increase customer trust and confidence, and set yourself apart from the competition. This is the book that will help you put the puzzle together. Bonus: With this book, you get access to our continuously growing online collection of templates, playbooks, worksheets, and insights to implement all of this. |
ai risk management framework: Artificial Intelligence in Accounting Othmar M. Lehner, Carina Knoll, 2022-08-05 Artificial intelligence (AI) and Big Data based applications in accounting and auditing have become pervasive in recent years. However, research on the societal implications of the widespread and partly unregulated use of AI and Big Data in several industries remains scarce despite salient and competing utopian and dystopian narratives. This book focuses on the transformation of accounting and auditing based on AI and Big Data. It not only provides a thorough and critical overview of the status-quo and the reports surrounding these technologies, but it also presents a future outlook on the ethical and normative implications concerning opportunities, risks, and limits. The book discusses topics such as future, human-machine collaboration, cybernetic approaches to decision-making, and ethical guidelines for good corporate governance of AI-based algorithms and Big Data in accounting and auditing. It clarifies the issues surrounding the digital transformation in this arena, delineates its boundaries, and highlights the essential issues and debates within and concerning this rapidly developing field. The authors develop a range of analytic approaches to the subject, both appreciative and sceptical, and synthesise new theoretical constructs that make better sense of human-machine collaborations in accounting and auditing. This book offers academics a variety of new research and theory building on digital accounting and auditing from and for accounting and auditing scholars, economists, organisations, and management academics and political and philosophical thinkers. Also, as a landmark work in a new area of current policy interest, it will engage regulators and policy makers, reflective practitioners, and media commentators through its authoritative contributions, editorial framing and discussion, and sector studies and cases. |
ai risk management framework: YSEC Yearbook of Socio-Economic Constitutions 2023 Eduardo Gill-Pedro, |
ai risk management framework: Managing Digital Risks Asian Development Bank, 2023-12-01 This publication analyzes the risks of digital transformation and shows how context-aware and integrated risk management can advance the digitally resilient development projects needed to build a more sustainable and equitable future. The publication outlines ADB’s digital risk assessment tools, looks at the role of development partners, and considers issues including cybersecurity, third-party digital risk management, and the ethical risks of artificial intelligence. Explaining why many digital transformations fall short, it shows why digital risk management is an evolutionary process that involves anticipating risk, safeguarding operations, and bridging gaps to better integrate digital technology into development programs. |
ai risk management framework: Organizations and Technology for Sustainability Elisabetta Magnaghi, Eleonora Veglianti, 2024-12-26 This book presents insights on digital transformation with a multidisciplinary lens. Collecting chapters from several management perspectives, it provides perspectives on the role of various concepts and elements that are needed by our organizations to win in today’s competition. This book is a contribution to the organizational, to the information and communication technology (ICT) as well as to the sustainability discussion. Here, the readers can find heterogenous inputs to better understand the organizational and technological aspects considering a sustainable business approach. This book is for academicians, students and practitioners interested in the interplay among IT-based solutions, organizational entities and sustainability issues. |
ai risk management framework: Developing Cybersecurity Programs and Policies in an AI-Driven World Omar Santos, 2024-07-16 ALL THE KNOWLEDGE YOU NEED TO BUILD CYBERSECURITY PROGRAMS AND POLICIES THAT WORK Clearly presents best practices, governance frameworks, and key standards Includes focused coverage of healthcare, finance, and PCI DSS compliance An essential and invaluable guide for leaders, managers, and technical professionals Today, cyberattacks can place entire organizations at risk. Cybersecurity can no longer be delegated to specialists: Success requires everyone to work together, from leaders on down. Developing Cybersecurity Programs and Policies in an AI-Driven World offers start-to-finish guidance for establishing effective cybersecurity in any organization. Drawing on more than two decades of real-world experience, Omar Santos presents realistic best practices for defining policy and governance, ensuring compliance, and collaborating to harden the entire organization. Santos begins by outlining the process of formulating actionable cybersecurity policies and creating a governance framework to support these policies. He then delves into various aspects of risk management, including strategies for asset management and data loss prevention, illustrating how to integrate various organizational functions—from HR to physical security—to enhance overall protection. This book covers many case studies and best practices for safeguarding communications, operations, and access; alongside strategies for the responsible acquisition, development, and maintenance of technology. It also discusses effective responses to security incidents. Santos provides a detailed examination of compliance requirements in different sectors and the NIST Cybersecurity Framework. LEARN HOW TO Establish cybersecurity policies and governance that serve your organization’s needs Integrate cybersecurity program components into a coherent framework for action Assess, prioritize, and manage security risk throughout the organization Manage assets and prevent data loss Work with HR to address human factors in cybersecurity Harden your facilities and physical environment Design effective policies for securing communications, operations, and access Strengthen security throughout AI-driven deployments Plan for quick, effective incident response and ensure business continuity Comply with rigorous regulations in finance and healthcare Learn about the NIST AI Risk Framework and how to protect AI implementations Explore and apply the guidance provided by the NIST Cybersecurity Framework |
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