Marke: Adrian Erin
Variante: Taschenbuch
Eigenschaften:
Protect the future of AI — one prompt, one model, and one system at a time.As AI systems grow smarter, attackers are getting smarter too. Hands-On Generative AI Security is your practical guide to defending against the newest wave of cyber threats — prompt injection, data leaks, model poisoning, and the OWASP LLM Top 10 vulnerabilities.Written for AI developers, cybersecurity professionals, and IT leaders, this book bridges the gap between AI innovation and secure implementation.It takes you beyond theory with real-world case studies, hands-on tutorials, and ready-to-use assessment tools that you can immediately apply to your own projects.Inside, you’ll learn how to:Detect and prevent prompt injection attacks before they compromise your data.Secure AI models across the entire development and deployment pipeline.Implement data sanitization, model integrity checks, and secure CI/CD practices.Apply the latest OWASP LLM Top 10 (2025) to real-world AI applications.Respond effectively to AI-specific security incidents using structured playbooks.Build your own AI risk assessment checklists and maturity frameworks.Each chapter combines clear explanations, Key Insights, Pro Tips, and practical labs, making complex AI security topics easy to understand and apply. Whether you’re building LLM-powered chatbots, deploying enterprise AI systems, or managing risk in production, this book helps you stay one step ahead of adversaries.Hands-On Generative AI Security empowers you to:Secure your AI workflows from training data to live deployment.Design resilient architectures that can adapt to evolving attacks.Future-proof your defenses with emerging tools and threat modeling techniques.If you’re serious about protecting your AI systems — and your organization — this book gives you everything you need to turn awareness into action.Perfect for:Cybersecurity professionals transitioning into AI defenseMachine learning engineers and LLM developersCloud security architects and SOC teamsStudents and researchers exploring AI safety and adversarial ML
