Overview

What happens when your AI product encounters its first adversarial attack-are you prepared or just hopeful? Your language model is live, serving thousands of users. While you monitor performance metrics, bad actors are probing for prompt injection vulnerabilities, bias exploits, and safety gaps. This book closes the gap between AI deployment and AI protection for product teams who cannot afford to learn safety lessons through public failure. Inside, you will learn: - How to architect red-teaming exercises that reveal hidden failure modes before malicious users find them - Specific prompt injection techniques and adversarial attack frameworks tailored for GPT and modern LLMs - Methods for embedding safety guardrails directly into your product infrastructure without killing model performance - Quantifiable metrics for bias, fairness, and robustness that satisfy both engineering requirements and compliance demands - Strategies for building a continuous safety testing culture that scales with your product roadmap This is not theoretical research. Each chapter provides implementable code, testing protocols, and real-world examples from production systems that experienced safety breaches. You will understand how to integrate safety engineering into sprint cycles, establish clear accountability for model risks, and create fail-safe mechanisms that activate when models behave unpredictably. Build AI products that remain resilient under adversarial pressure. Get your copy today and make safety engineering a competitive advantage, not an afterthought.

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