Cybersecurity for AI

Artificial Intelligence is transforming every industry.

But most organizations are deploying AI faster than they can secure it.

From prompt injection and model poisoning to RAG vulnerabilities, agentic AI risks, supply chain attacks, and AI governance challenges, modern AI systems introduce an entirely new security landscape that traditional cybersecurity practices were never designed to address.

The question is no longer whether your organization will use AI.

The question is whether your AI systems can be trusted.

In Securing AI, Ethan Vale provides a practical and comprehensive guide to protecting modern AI and machine learning environments. Written for developers, architects, CTOs, security leaders, product managers, and technology teams, this book explains how to build secure AI systems from design through deployment.

Inside you'll learn:

• The OWASP LLM Top 10 and emerging AI security threats
• Prompt injection attacks and practical defenses
• RAG security and knowledge-base protection
• AI agent and MCP security best practices
• Model poisoning and training data attacks
• Secure system prompt design
• AI API security and authentication controls
• Multi-tenant AI application security
• AI monitoring, logging, and observability
• AI governance and risk management
• EU AI Act, GDPR, and NIST AI RMF compliance
• Secure AI Development Lifecycle implementation
• Real-world AI security case studies and frameworks