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Contrary 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.