Practical AI Security
Packt Publishing Limited (Verlag)
9781806119936 (ISBN)
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Key Features
Clearly identify and manage real-world risks unique to generative AI, confidently explaining their implications to both technical teams and business stakeholders while understanding the complete AI security ecosystem.
Book DescriptionContrary to general AI texts or cybersecurity books with limited AI coverage, this guide offers a comprehensive dive into securing the generative AI ecosystem.
It moves through five parts: Foundations explains why AI security is unique, covering threat modeling, attack surfaces, and defense principles. Attacks examines vectors against system anatomy, data/models, prompt injection, memory, RAG, and agents, concluding with red teaming and evaluation. Designing, Deploying, and Architecting Secure AI Systems covers secure infrastructure/MLOps, APIs, defensive prompting, agent security, supply chain integrity, and Zero Trust patterns. Operationalizing AI Security and Responsibility addresses governance, risk, compliance (GRC), security operations, safety/alignment, and AI-driven misinformation. Building Sustainable AI Security Programs focuses on organizational capability, threat intelligence, collaboration, and the future of AI security. Throughout, you will gain access to practical insights and structured approaches applicable to real-world scenarios.
By the end, you will be able to design, implement, and maintain security programs for generative AI, defend against advanced threats, communicate risks to stakeholders, and establish governance ensuring secure, compliant operations across the lifecycle.What you will learn
Identify AI-specific risks and clearly communicate them to business teams
Defend models, data, RAG, and agents from threats like poisoning, prompt injection, jailbreaking, and data exfiltration
Design resilient cloud/MLOps with Zero Trust, supply chain security, and isolation
Build secure APIs, apps, and agents with strong auth, validation, and safe tool use
Apply AI-focused GRC, alignment checks, bias mitigation, monitoring, and incident response
Translate complex concepts into actionable steps, using threat intel and collaboration for lasting security
Who this book is forThis book is for mid- to senior-level cybersecurity professionals, security architects, and tech leaders managing risks in generative AI deployments. It’s also valuable for early-career practitioners, AI/ML engineers, red teamers, DevSecOps, governance specialists, compliance officers, and product stakeholders with foundational cybersecurity knowledge. Readers should have basic familiarity with security concepts, some exposure to cloud platforms (AWS, Azure, or GCP), and a fundamental grasp of AI/ML, though no prior AI security expertise is required.
Kris Kimmerle is a recognized leader in AI security and governance, currently driving strategic initiatives across global organizations. With over 20 years of experience spanning cybersecurity, cloud architecture, and artificial intelligence, Kris has helped international enterprises implement generative AI systems securely and in compliance with regulatory standards. His work bridges deep technical expertise with executive-level strategy, enabling organizations to scale AI without compromising trust, privacy, or resilience. He holds CISSP and AI Governance Professional (AIGP) certifications, along with specialized AI credentials from AWS, Azure, and Google Cloud. Kris is a trusted voice in the field, known for translating complex risks into practical guidance. He regularly speaks at industry events, advises global clients on securing AI across the development lifecycle, and publishes insights that shape the conversation on AI risk, governance, and security at scale. David Okeyode is a leading cloud security architect with extensive experience in Azure security consulting, training, and research. He has authored multiple cloud security courses and speaks at major cybersecurity events worldwide.
Table of Contents
Why AI Security Is Different
Threat Modeling AI Systems
The AI Attack Surface
Foundations of AI Defense
Anatomy of an AI System
Prompt Injection and Jailbreaking
Memory, Context, and State Abuse
Attacks on RAG Systems
Agent Architecture and Vulnerabilities
Agent Exploitation Techniques
Attacks on Training Data and Model Integrity
AI Red Teaming and Evaluation
Securing AI Infrastructure, MLOps, and Runtime Environments
Building Secure AI Applications and APIs
Defensive Prompt Engineering
Guardrails and Human Oversight
External Dependencies, Supply Chain Integrity & Multi-Tenant Risk
Securing AI Agents with Zero Trust Architecture
AI Governance, Risk, and Compliance
AI Security Engineering
AI Security Operations
Building Sustainable AI Security Programs
The Future of AI Security
| Erscheinungsdatum | 11.12.2025 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Informatik ► Netzwerke ► Sicherheit / Firewall |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-13 | 9781806119936 / 9781806119936 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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