Neelakrishnan (63 Ergebnisse)

Soft cover. Zustand: As New. 1st Edition. Apress (distributed by Springer), copyright 2024. Paperback, 9-1/4 inches tall, xix + 365 pages including index, illustrated with figures. This copy in nearly new condition. Clean, crisp, unmarked text. No hi-liting, underlining or marginal notes. Firm binding no loose pages. Book looks like it wasn t used. The very tips of the first few pages (bottom free corners) are slightly bumped from shelf wear that s the only flaw that I could see. Really nice copy.…

Zustand: New.

Zustand: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books.

Zustand: As New. Unread book in perfect condition.

Paperback. Zustand: New. This book focuses on analyzing the foundational requirements necessary to construct an autonomous data protection solution for enterprise businesses. It navigates readers through various options and tools, outlining the advantages and disadvantages of each. Covering diverse deployment environments including cloud, on-premises, and hybrid setups, as well as different deployment scales and comprehensive channel coverages, it encourages readers to break away from conventional norms in their approach.By exploring the factors that should be taken into account, the book highlights the significant gap in existing data safeguarding solutions, which often rely solely on configured security policies. It proposes a forward-thinking security approach designed to endure over time, surpassing traditional policies and urging readers to consider proactive autonomous data security solutions. Additionally, it delves into the system's ability to adapt to deployed environments, learn from feedback, and autonomously safeguard data while adhering to security policies.More than just a set of guidelines, this book serves as a catalyst for the future of the cybersecurity industry. Its focus on autonomous data security and its relevance in the era of advancing AI make it particularly timely and essential.What You Will learn:Understand why data security is important for enterprise businesses.How data protection solutions work and how to evaluate a solution in the market.How to start thinking and evaluating requirements when building a solution for small, medium, and large enterprises.Understand the pros and cons of security policy configurations defined by administrators and why can't they provide comprehensive protection.How to safeguard data via adaptive learning from the deployed environment - providing autonomous data security with or without policies.How to leverage AI to provide data security with comprehensive proactive protection.What factors to consider when they have to protect and safeguard data.Who this book is for:The primary audience is cybersecurity professionals, security enthusiasts, C-level executives in organizations (all verticals), and security analysts and IT administrators. Secondary audience includes professors and teachers, channel integrators, professional services, and hackers.…

Zustand: New.

Paperback. Zustand: New. This book focuses on analyzing the foundational requirements necessary to construct an autonomous data protection solution for enterprise businesses. It navigates readers through various options and tools, outlining the advantages and disadvantages of each. Covering diverse deployment environments including cloud, on-premises, and hybrid setups, as well as different deployment scales and comprehensive channel coverages, it encourages readers to break away from conventional norms in their approach.By exploring the factors that should be taken into account, the book highlights the significant gap in existing data safeguarding solutions, which often rely solely on configured security policies. It proposes a forward-thinking security approach designed to endure over time, surpassing traditional policies and urging readers to consider proactive autonomous data security solutions. Additionally, it delves into the system's ability to adapt to deployed environments, learn from feedback, and autonomously safeguard data while adhering to security policies.More than just a set of guidelines, this book serves as a catalyst for the future of the cybersecurity industry. Its focus on autonomous data security and its relevance in the era of advancing AI make it particularly timely and essential.What You Will learn:Understand why data security is important for enterprise businesses.How data protection solutions work and how to evaluate a solution in the market.How to start thinking and evaluating requirements when building a solution for small, medium, and large enterprises.Understand the pros and cons of security policy configurations defined by administrators and why can't they provide comprehensive protection.How to safeguard data via adaptive learning from the deployed environment - providing autonomous data security with or without policies.How to leverage AI to provide data security with comprehensive proactive protection.What factors to consider when they have to protect and safeguard data.Who this book is for:The primary audience is cybersecurity professionals, security enthusiasts, C-level executives in organizations (all verticals), and security analysts and IT administrators. Secondary audience includes professors and teachers, channel integrators, professional services, and hackers.…

Paperback. Zustand: New. Large Language Models (LLMs) are rapidly transforming how enterprises operate, powering customer support, internal assistants, automated workflows, search, analytics, and decision-making systems. But as organizations adopt AI at scale, they are also introducing a new and expanding attack surface.?Jailbreaking LLMs?explores how attackers manipulate AI systems through prompt injection, jailbreaks, adversarial inputs, data poisoning, context manipulation, retrieval attacks, and unsafe tool usage to bypass safeguards, leak sensitive data, and influence AI behavior in unexpected ways.  This book provides a practical guide to understanding, testing, and defending enterprise AI systems in the real world. Through real attack scenarios, security frameworks, red-teaming methodologies, governance strategies, and defensive architecture patterns, readers will learn how to build secure, resilient, and enterprise-ready LLM deployments. Covering everything from RAG security and agentic systems to incident response, AI governance, runtime monitoring, and future attack trends, this book connects AI innovation with modern cybersecurity practices. What you will learn Understand how LLM jailbreaks, prompt injection, and adversarial attacks work Identify vulnerabilities across enterprise AI systems, RAG pipelines, agents, and APIs Design and deploy secure, enterprise-ready LLM architectures ?Implement monitoring, logging, detection, and incident response workflows for AI systems Apply red-teaming and defensive testing strategies to evaluate LLM security Build governance, compliance, and ethical AI controls into enterprise deployments Understand emerging AI attack trends and future cybersecurity risks ? Who this book is for This book is for cybersecurity professionals, AI/ML engineers, enterprise architects, security analysts, SOC teams, IT leaders, and technical decision-makers responsible for building, deploying, or securing AI-powered systems. It is also valuable for practitioners who want to better understand the security, governance, and operational challenges that come with adopting Large Language Models in enterprise environments.…

paperback. Zustand: Very Good.

Paperback. Zustand: New. Large Language Models (LLMs) are rapidly transforming how enterprises operate, powering customer support, internal assistants, automated workflows, search, analytics, and decision-making systems. But as organizations adopt AI at scale, they are also introducing a new and expanding attack surface.?Jailbreaking LLMs?explores how attackers manipulate AI systems through prompt injection, jailbreaks, adversarial inputs, data poisoning, context manipulation, retrieval attacks, and unsafe tool usage to bypass safeguards, leak sensitive data, and influence AI behavior in unexpected ways.  This book provides a practical guide to understanding, testing, and defending enterprise AI systems in the real world. Through real attack scenarios, security frameworks, red-teaming methodologies, governance strategies, and defensive architecture patterns, readers will learn how to build secure, resilient, and enterprise-ready LLM deployments. Covering everything from RAG security and agentic systems to incident response, AI governance, runtime monitoring, and future attack trends, this book connects AI innovation with modern cybersecurity practices. What you will learn Understand how LLM jailbreaks, prompt injection, and adversarial attacks work Identify vulnerabilities across enterprise AI systems, RAG pipelines, agents, and APIs Design and deploy secure, enterprise-ready LLM architectures ?Implement monitoring, logging, detection, and incident response workflows for AI systems Apply red-teaming and defensive testing strategies to evaluate LLM security Build governance, compliance, and ethical AI controls into enterprise deployments Understand emerging AI attack trends and future cybersecurity risks ? Who this book is for This book is for cybersecurity professionals, AI/ML engineers, enterprise architects, security analysts, SOC teams, IT leaders, and technical decision-makers responsible for building, deploying, or securing AI-powered systems. It is also valuable for practitioners who want to better understand the security, governance, and operational challenges that come with adopting Large Language Models in enterprise environments.…

Zustand: New.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: As New. Unread book in perfect condition.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Neuware -Large Language Models (LLMs) are rapidly transforming how enterprises operate, powering customer support, internal assistants, automated workflows, search, analytics, and decision-making systems. But as organizations adopt AI at scale, they are also introducing a new and expanding attack surface.¿Jailbreaking LLMs¿explores how attackers manipulate AI systems through prompt injection, jailbreaks, adversarial inputs, data poisoning, context manipulation, retrieval attacks, and unsafe tool usage to bypass safeguards, leak sensitive data, and influence AI behavior in unexpected ways. This book provides a practical guide to understanding, testing, and defending enterprise AI systems in the real world. Through real attack scenarios, security frameworks, red-teaming methodologies, governance strategies, and defensive architecture patterns, readers will learn how to build secure, resilient, and enterprise-ready LLM deployments. Covering everything from RAG security and agentic systems to incident response, AI governance, runtime monitoring, and future attack trends, this book connects AI innovation with modern cybersecurity practices. What you will learn - Understand how LLM jailbreaks, prompt injection, and adversarial attacks work <li class='OutlineElement Ltr SCXW159986485 BCX0' role='listitem' aria-setsize='-1' data-leveltext='¿' data-font='Symbol' data-listid='1' data-list-defn-props='{'335552541':1,'335559685':720,'335559991':360,'469769226':'Symbol','4697.…

Paperback. Zustand: New. This book focuses on analyzing the foundational requirements necessary to construct an autonomous data protection solution for enterprise businesses. It navigates readers through various options and tools, outlining the advantages and disadvantages of each. Covering diverse deployment environments including cloud, on-premises, and hybrid setups, as well as different deployment scales and comprehensive channel coverages, it encourages readers to break away from conventional norms in their approach.By exploring the factors that should be taken into account, the book highlights the significant gap in existing data safeguarding solutions, which often rely solely on configured security policies. It proposes a forward-thinking security approach designed to endure over time, surpassing traditional policies and urging readers to consider proactive autonomous data security solutions. Additionally, it delves into the system's ability to adapt to deployed environments, learn from feedback, and autonomously safeguard data while adhering to security policies.More than just a set of guidelines, this book serves as a catalyst for the future of the cybersecurity industry. Its focus on autonomous data security and its relevance in the era of advancing AI make it particularly timely and essential.What You Will learn:Understand why data security is important for enterprise businesses.How data protection solutions work and how to evaluate a solution in the market.How to start thinking and evaluating requirements when building a solution for small, medium, and large enterprises.Understand the pros and cons of security policy configurations defined by administrators and why can't they provide comprehensive protection.How to safeguard data via adaptive learning from the deployed environment - providing autonomous data security with or without policies.How to leverage AI to provide data security with comprehensive proactive protection.What factors to consider when they have to protect and safeguard data.Who this book is for:The primary audience is cybersecurity professionals, security enthusiasts, C-level executives in organizations (all verticals), and security analysts and IT administrators. Secondary audience includes professors and teachers, channel integrators, professional services, and hackers.…

Taschenbuch. Zustand: Neu. Neuware - This book focuses on analyzing the foundational requirements necessary to construct an autonomous data protection solution for enterprise businesses. It navigates readers through various options and tools, outlining the advantages and disadvantages of each. Covering diverse deployment environments including cloud, on-premises, and hybrid setups, as well as different deployment scales and comprehensive channel coverages, it encourages readers to break away from conventional norms in their approach.By exploring the factors that should be taken into account, the book highlights the significant gap in existing data safeguarding solutions, which often rely solely on configured security policies. It proposes a forward-thinking security approach designed to endure over time, surpassing traditional policies and urging readers to consider proactive autonomous data security solutions. Additionally, it delves into the system's ability to adapt to deployed environments, learn from feedback, and autonomously safeguard data while adhering to security policies.More than just a set of guidelines, this book serves as a catalyst for the future of the cybersecurity industry. Its focus on autonomous data security and its relevance in the era of advancing AI make it particularly timely and essential.What You Will learn:Understand why data security is important for enterprise businesses.How data protection solutions work and how to evaluate a solution in the market.How to start thinking and evaluating requirements when building a solution for small, medium, and large enterprises.Understand the pros and cons of security policy configurations defined by administrators and why can't they provide comprehensive protection.How to safeguard data via adaptive learning from the deployed environment - providing autonomous data security with or without policies.How to leverage AI to provide data security with comprehensive proactive protection.What factors to consider when they have to protect and safeguard data.Who this book is for:The primary audience is cybersecurity professionals, security enthusiasts, C-level executives in organizations (all verticals), and security analysts and IT administrators. Secondary audience includes professors and teachers, channel integrators, professional services, and hackers. …

Zustand: NEW.

Paperback. Zustand: New. Large Language Models (LLMs) are rapidly transforming how enterprises operate, powering customer support, internal assistants, automated workflows, search, analytics, and decision-making systems. But as organizations adopt AI at scale, they are also introducing a new and expanding attack surface.?Jailbreaking LLMs?explores how attackers manipulate AI systems through prompt injection, jailbreaks, adversarial inputs, data poisoning, context manipulation, retrieval attacks, and unsafe tool usage to bypass safeguards, leak sensitive data, and influence AI behavior in unexpected ways.  This book provides a practical guide to understanding, testing, and defending enterprise AI systems in the real world. Through real attack scenarios, security frameworks, red-teaming methodologies, governance strategies, and defensive architecture patterns, readers will learn how to build secure, resilient, and enterprise-ready LLM deployments. Covering everything from RAG security and agentic systems to incident response, AI governance, runtime monitoring, and future attack trends, this book connects AI innovation with modern cybersecurity practices. What you will learn Understand how LLM jailbreaks, prompt injection, and adversarial attacks work Identify vulnerabilities across enterprise AI systems, RAG pipelines, agents, and APIs Design and deploy secure, enterprise-ready LLM architectures ?Implement monitoring, logging, detection, and incident response workflows for AI systems Apply red-teaming and defensive testing strategies to evaluate LLM security Build governance, compliance, and ethical AI controls into enterprise deployments Understand emerging AI attack trends and future cybersecurity risks ? Who this book is for This book is for cybersecurity professionals, AI/ML engineers, enterprise architects, security analysts, SOC teams, IT leaders, and technical decision-makers responsible for building, deploying, or securing AI-powered systems. It is also valuable for practitioners who want to better understand the security, governance, and operational challenges that come with adopting Large Language Models in enterprise environments.…

Taschenbuch. Zustand: Neu. Neuware - Large Language Models (LLMs) are rapidly transforming how enterprisesoperate, powering customer support, internal assistants, automated workflows, search, analytics, and decision-making systems. But as organizations adopt AI at scale, they are also introducing a new and expanding attack surface. Jailbreaking LLMs explores how attackers manipulate AI systems through prompt injection, jailbreaks, adversarial inputs, data poisoning, context manipulation, retrieval attacks, and unsafe tool usage to bypass safeguards, leak sensitive data, and influence AI behavior in unexpected ways.This book provides a practical guide to understanding, testing, and defending enterprise AI systems in the real world. Through real attack scenarios, security frameworks, red-teaming methodologies, governance strategies, and defensive architecture patterns, readers will learn how to build secure, resilient, and enterprise-ready LLM deployments. Covering everything from RAG security and agentic systems to incident response, AI governance, runtime monitoring, and future attack trends, this book connects AI innovation with modern cybersecurity practices.What you will learnUnderstand how LLM jailbreaks, prompt injection, and adversarial attacks work.…

Zustand: New.

Paperback. Zustand: New. This book focuses on analyzing the foundational requirements necessary to construct an autonomous data protection solution for enterprise businesses. It navigates readers through various options and tools, outlining the advantages and disadvantages of each. Covering diverse deployment environments including cloud, on-premises, and hybrid setups, as well as different deployment scales and comprehensive channel coverages, it encourages readers to break away from conventional norms in their approach.By exploring the factors that should be taken into account, the book highlights the significant gap in existing data safeguarding solutions, which often rely solely on configured security policies. It proposes a forward-thinking security approach designed to endure over time, surpassing traditional policies and urging readers to consider proactive autonomous data security solutions. Additionally, it delves into the system's ability to adapt to deployed environments, learn from feedback, and autonomously safeguard data while adhering to security policies.More than just a set of guidelines, this book serves as a catalyst for the future of the cybersecurity industry. Its focus on autonomous data security and its relevance in the era of advancing AI make it particularly timely and essential.What You Will learn:Understand why data security is important for enterprise businesses.How data protection solutions work and how to evaluate a solution in the market.How to start thinking and evaluating requirements when building a solution for small, medium, and large enterprises.Understand the pros and cons of security policy configurations defined by administrators and why can't they provide comprehensive protection.How to safeguard data via adaptive learning from the deployed environment - providing autonomous data security with or without policies.How to leverage AI to provide data security with comprehensive proactive protection.What factors to consider when they have to protect and safeguard data.Who this book is for:The primary audience is cybersecurity professionals, security enthusiasts, C-level executives in organizations (all verticals), and security analysts and IT administrators. Secondary audience includes professors and teachers, channel integrators, professional services, and hackers.…

Paperback. Zustand: New. Large Language Models (LLMs) are rapidly transforming how enterprises operate, powering customer support, internal assistants, automated workflows, search, analytics, and decision-making systems. But as organizations adopt AI at scale, they are also introducing a new and expanding attack surface.?Jailbreaking LLMs?explores how attackers manipulate AI systems through prompt injection, jailbreaks, adversarial inputs, data poisoning, context manipulation, retrieval attacks, and unsafe tool usage to bypass safeguards, leak sensitive data, and influence AI behavior in unexpected ways.  This book provides a practical guide to understanding, testing, and defending enterprise AI systems in the real world. Through real attack scenarios, security frameworks, red-teaming methodologies, governance strategies, and defensive architecture patterns, readers will learn how to build secure, resilient, and enterprise-ready LLM deployments. Covering everything from RAG security and agentic systems to incident response, AI governance, runtime monitoring, and future attack trends, this book connects AI innovation with modern cybersecurity practices. What you will learn Understand how LLM jailbreaks, prompt injection, and adversarial attacks work Identify vulnerabilities across enterprise AI systems, RAG pipelines, agents, and APIs Design and deploy secure, enterprise-ready LLM architectures ?Implement monitoring, logging, detection, and incident response workflows for AI systems Apply red-teaming and defensive testing strategies to evaluate LLM security Build governance, compliance, and ethical AI controls into enterprise deployments Understand emerging AI attack trends and future cybersecurity risks ? Who this book is for This book is for cybersecurity professionals, AI/ML engineers, enterprise architects, security analysts, SOC teams, IT leaders, and technical decision-makers responsible for building, deploying, or securing AI-powered systems. It is also valuable for practitioners who want to better understand the security, governance, and operational challenges that come with adopting Large Language Models in enterprise environments.…

Zustand: New.

Zustand: New.

Zustand: New.

Zustand: New.

Taschenbuch. Zustand: Neu. Neuware -Large Language Models (LLMs) are rapidly transforming how enterprises operate, powering customer support, internal assistants, automated workflows, search, analytics, and decision-making systems. But as organizations adopt AI at scale, they are also introducing a new and expanding attack surface.¿Jailbreaking LLMs¿explores how attackers manipulate AI systems through prompt injection, jailbreaks, adversarial inputs, data poisoning, context manipulation, retrieval attacks, and unsafe tool usage to bypass safeguards, leak sensitive data, and influence AI behavior in unexpected ways. This book provides a practical guide to understanding, testing, and defending enterprise AI systems in the real world. Through real attack scenarios, security frameworks, red-teaming methodologies, governance strategies, and defensive architecture patterns, readers will learn how to build secure, resilient, and enterprise-ready LLM deployments. Covering everything from RAG security and agentic systems to incident response, AI governance, runtime monitoring, and future attack trends, this book connects AI innovation with modern cybersecurity practices. What you will learn - Understand how LLM jailbreaks, prompt injection, and adversarial attacks work <li class='OutlineElement Ltr SCXW159986485 BCX0' role='listitem' aria-setsize='-1' data-leveltext='¿' data-font='Symbol' data-listid='1' data-list-defn-props='{'335552541':1,'335559685':720,'335559991':360,'469769226':'Symbol','4697 760 pp. Englisch.…

Zustand: New.