Every organization that deploys artificial intelligence today faces the same uncomfortable truth. The systems they rely on are fundamentally opaque. Neural networks don't explain themselves, and language models don't come with warranties. The industry has raced to make AI more capable, but it has spent less time ensuring these systems are predictable, explainable, controllable, and properly governed. This book exists to close that gap.

AI safety is not a single problem that can be solved with a single tool. It is a constellation of interconnected challenges spanning engineering, product management, and regulatory compliance. This book delivers a practical, four-part framework organized around predictability, explainability, control, and governance. It's a blueprint for any team deploying AI systems in high-stakes environments, from engineers building models to executives managing organizational risk.

Inside, you'll discover:
• The case for predictable AI and tools for measuring model behavior
• Red teaming and stress testing to find vulnerabilities
• Methods for managing distribution shift and model drift
• Why explainability matters and interpretability methods you can use today
• The control problem and prompt engineering for safety
• Guardrails and scalable oversight for AI systems
• How to build an AI safety culture and navigate the regulatory landscape

The realistic goal is not perfect safety, but layered safety. This book gives you the tools to build that layered defense and the governance to maintain it.