Securing Modern AI Applications: Practical Security for LLMs, RAG, AI Agents, Governance, Privacy, and AI Development is a practical guide to understanding and securing today's AI-powered applications.
Modern AI systems are no longer limited to generating text. They can retrieve organizational knowledge, access databases, call APIs, browse the web, interact with external services, maintain memory, and perform actions on behalf of users. These capabilities create powerful applications—but they also create new attack surfaces.
This book explores the security challenges that emerge when artificial intelligence becomes part of real-world software systems.
It examines the security of Large Language Models, prompts, RAG architectures, AI agents, tools, memory, sensitive data, APIs, applications, infrastructure, and AI development workflows.
The book also explains how organizations can approach AI governance, privacy, risk management, secure AI development, security testing, and red teaming as integrated parts of the AI lifecycle.
Rather than focusing only on theoretical concepts, the book uses practical security thinking:
- What can go wrong?
- Where is the trust boundary?
- What data can the AI access?
- What happens if the model is manipulated?
- Can an attacker turn model output into a harmful action?
- Can RAG expose unauthorized information?
- Can an AI agent abuse its tools?
- How should sensitive information be protected?
- How should AI applications be tested before and after deployment?
The central message is that AI security is not simply model security.
It is the security of the entire system surrounding the model.
