API Security for AI Applications: Practical Defense Strategies for LLMs, Prompt Injection, and Data Leakage
Sankar Srinivasan
This audiobook is narrated by a digital voice.
Every company now has AI in production.
Almost none of them have secured it properly.
That’s not a small problem. That’s a time bomb.
From prompt injection attacks to silent data leaks, modern AI...
Location:
United States
Description:
This audiobook is narrated by a digital voice. Every company now has AI in production. Almost none of them have secured it properly. That’s not a small problem. That’s a time bomb. From prompt injection attacks to silent data leaks, modern AI applications introduce entirely new security risks that traditional API security was never designed to handle. This book is your practical, no-nonsense guide to fixing that. No hype. No “AI will change everything” speeches. Just real-world security strategies that actually work. What You’ll Learn How prompt injection attacks actually work (and why they’re so dangerous) The hidden ways AI systems leak sensitive data without anyone noticing Securing LLM APIs in real production environments How attackers exploit tools, plugins, and agent-based systems Building layered defenses for AI applications Practical threat modeling for AI systems (not theoretical fluff) Secure deployment patterns using Docker and modern pipelines Logging, monitoring, and incident response for AI apps Hands-On, Practical Approach This isn’t a theory book. You’ll work with: Real attack scenarios Code-level defenses Security checklists you can apply immediately Production-ready architecture patterns Who This Book Is For Developers building AI apps with APIs Security engineers entering the AI space Startup teams shipping LLM features fast (and slightly nervously) CTOs who know “this could go wrong” but aren’t sure how What Makes This Different Most books explain AI. This one explains how AI breaks — and how to stop it. If You’re Deploying AI Without Security… You’re not building a product. You’re building a vulnerability. Free Github resources and excel sheet tool attached. Duration - 6h 10m. Author - Sankar Srinivasan. Narrator - Digital Voice Madison G. Published Date - Friday, 09 January 2026. Copyright - © 2026 Sankar Srinivasan ©.
Language:
English
API Security for AI Applications: Practical Defense Strategies for LLMs, Prompt Injection, and Data Leakage
Duration:00:03:31
API Security for AI Applications 4
Duration:00:00:19
Introduction 4
Duration:00:08:54
Chapter 1 : The AI Security Wake-Up Call 11
Duration:00:25:00
Chapter 2 : How AI APIs Actually Work And Where They Break 27
Duration:00:24:37
Chapter 3: Threat Modeling for AI Applications 52
Duration:00:21:20
Chapter 4 : Prompt Injection Attacks 79
Duration:00:26:02
Chapter 5 : Data Leakage: The Silent Killer 112
Duration:00:25:28
Chapter 6: Tool Misuse and Agent Exploits 139
Duration:00:22:46
Chapter 7 : Authentication and Authorization for AI APIs 165
Duration:00:23:04
Chapter 8: Securing API Gateways for AI Systems 191
Duration:00:21:09
Chapter 9: Input Output Filtering and Validation for LLMs 214
Duration:00:32:12
Chapter 10: Rate Limiting, Abuse Prevention and Cost Attacks 253
Duration:00:22:49
Chapter 11: Secure Deployment with Docker and CI CD 278
Duration:00:24:58
Chapter 12: Logging, Monitoring and Incident Response 302
Duration:00:25:31
Chapter 13: Red Teaming AI Systems — Breaking Your Own Product 329
Duration:00:23:26
Chapter 14: Security Checklists and Production Playbooks 355
Duration:00:21:18
Chapter 15: The Future of AI Security 377
Duration:00:18:08