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Condizione: Sehr gut. Zustand: Sehr gut | Seiten: 124 | Sprache: Englisch | Produktart: Bücher | The extent to which cloud computing is being used will only grow; by the end of 2016, it is anticipated that cloud computing will constitute the majority of IT spending. The National Institute of Standards and Technology has defined the model for cloud computing. Generally there are some real trends that are towards a massively scalable computing. The service orientation, the visualization and internet have all converged for sponsoring a phenomenon which will enable the individuals as well as the businesses for choosing and how they can acquire or delivering IT services with no emphasis on its constraints of the licensing models of both hardware and software. The cloud computing based services would encourage delivery and consumption based on the economy that ranges from storage to computation of the video for financial deduction management. Cloud computing is another name given to an existing technology called grid computing and is not entirely a new concept. It has a similar vision like that of decreasing the storage computing cost and providing reliability and flexibility in such a way that computers can autonomously operate. The issues for cloud and grid are almost the same. The cloud computing based services would encourage delivery and consumption based on the economy that ranges from storage to computation of the video for financial deduction management. Cloud computing is another name given to an existing technology called grid computing and is not entirely a new concept. It has a similar vision like that of decreasing the storage computing cost and providing reliability and flexibility in such a way that computers can autonomously operate.…

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Hardcover. Condizione: new. Hardcover. For industry practitioners, academic researchers, and governance professionals alike, this book offers both clarity and depth in one of the most important domains of modern technology. As AI matures, trust and risk management will define its successand this book lays the groundwork for achieving that vision. As AI continues to permeate sectors ranging from healthcare to finance, ensuring that these systems are not only powerful but also accountable, transparent, and secure, is more critical than ever. This book offers a vital exploration into the intersection of trustworthiness, risk mitigation, and security governance in artificial intelligence systems, serving as a definitive guide for professionals, researchers, and policymakers striving to build, deploy, and manage AI responsibly in high-stakes environments. Using a comprehensive approach, it explores how to integrate technical safeguards, organizational practices, and regulatory alignment to manage the unique risks posed by AI, including algorithmic bias, data misuse, adversarial attacks, and opaque decision-making. The result is a strategic approach that not only identifies vulnerabilities, but also promotes resilient, auditable, and trustworthy AI ecosystems. At its core, AI TRiSM is a forward-looking concept that embraces the realities of AI in production environments. The framework moves beyond traditional static models of governance to propose dynamic, adaptive controls that evolve alongside AI systems. Through real-world case studies, the book outlines how tools like model cards, bias audits, and zero-trust architectures can be embedded into the AI development lifecycle. Readers will find the volume: Introduces concepts to stay ahead of regulations and build trustworthy AI systems that customers and stakeholders can rely on;Addresses security threats, bias, and compliance gaps to avoid costly AI failures;Explores proven frameworks and best practices to deploy AI responsibly and strategies to outperform;Provides comprehensive guidance through real-world case studies and contributions from industry and academia. Audience AI and machine learning engineers, data scientists, cybersecurity and risk management specialists, academics, researchers, and policymakers specializing in AI ethics, security, and risk management. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Hardback. Condizione: New. For industry practitioners, academic researchers, and governance professionals alike, this book offers both clarity and depth in one of the most important domains of modern technology. As AI matures, trust and risk management will define its success-and this book lays the groundwork for achieving that vision. As AI continues to permeate sectors ranging from healthcare to finance, ensuring that these systems are not only powerful but also accountable, transparent, and secure, is more critical than ever. This book offers a vital exploration into the intersection of trustworthiness, risk mitigation, and security governance in artificial intelligence systems, serving as a definitive guide for professionals, researchers, and policymakers striving to build, deploy, and manage AI responsibly in high-stakes environments. Using a comprehensive approach, it explores how to integrate technical safeguards, organizational practices, and regulatory alignment to manage the unique risks posed by AI, including algorithmic bias, data misuse, adversarial attacks, and opaque decision-making. The result is a strategic approach that not only identifies vulnerabilities, but also promotes resilient, auditable, and trustworthy AI ecosystems. At its core, AI TRiSM is a forward-looking concept that embraces the realities of AI in production environments. The framework moves beyond traditional static models of governance to propose dynamic, adaptive controls that evolve alongside AI systems. Through real-world case studies, the book outlines how tools like model cards, bias audits, and zero-trust architectures can be embedded into the AI development lifecycle. Readers will find the volume: Introduces concepts to stay ahead of regulations and build trustworthy AI systems that customers and stakeholders can rely on;Addresses security threats, bias, and compliance gaps to avoid costly AI failures;Explores proven frameworks and best practices to deploy AI responsibly and strategies to outperform;Provides comprehensive guidance through real-world case studies and contributions from industry and academia. Audience AI and machine learning engineers, data scientists, cybersecurity and risk management specialists, academics, researchers, and policymakers specializing in AI ethics, security, and risk management.…