Descriptions
AI-Native LLM Security: Threats, defenses, and best practices for building safe and trustworthy AI, Adversarial attacks against generative AI and LLMs create novel security challenges that exploit how these models are trained and used. This book provides a practical, research-informed guide for securing generative AI and LLM applications: it explains common and emerging attack vectors, shows how to apply threat modeling and established taxonomies (OWASP, NIST, MITRE) to identify vulnerabilities, and presents secure-by-design strategies and MLSecOps practices to harden systems across development, CI/CD, MLOps, and operations. Through real-world examples and industry best practices, the authors demonstrate how to incorporate security controls into the AI development lifecycle and protect open-access LLM deployments.
Built on coauthors’ expertise in LLM security, the book also covers incident detection and response, governance, legal and ethical considerations, and continuous improvement approaches for trustworthy AI. Readers will learn how to design secure LLM architectures with isolation and access controls, mitigate risks from data curation through production operations, and adopt practical defenses to develop, deploy, and maintain safer AI systems with confidence.
Book Features
- Understand unique security risks posed by large language models and generative AI
- Use threat modeling and established taxonomies (OWASP, NIST, MITRE) to identify vulnerabilities and attack vectors
- Detect, respond to, and remediate security incidents in operational LLM deployments
- Design secure-by-design defenses and apply MLSecOps practices across CI/CD and MLOps pipelines
- Develop governance, legal and ethical strategies for trustworthy AI
- Mitigate risks across the entire LLM life cycle from data curation to production operations
- Architect secure LLM deployments with isolation, access controls, and operational monitoring
Who this book is for
- Cybersecurity professionals securing AI and LLM deployments
- ML engineers and data scientists building production LLM systems
- DevSecOps and MLOps engineers responsible for CI/CD and operational security
- Security architects and risk managers designing governance and controls
- Researchers and practitioners interested in LLM threats and defenses
- Product and platform teams deploying generative AI in applications
Specificatoin of AI-Native LLM Security: Threats, defenses, and best practices for building safe and trustworthy AI
- Publisher : PacktPub
- Teacher : Vaibhav Malik
- Language : English
- Level : All Levels
- Pages: 416
- Chapters: 8
- Format: PDF, EPUB
Content of AI-Native LLM Security: Threats, defenses, and best practices for building safe and trustworthy AI
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