Vector databases and AI APIs have become foundational components of modern AI systems, but they also introduce new and often misunderstood security risks.
Securing Vector Databases and AI APIs is a technical, practitioner-focused guide for engineers and security professionals responsible for protecting embeddings, prompts, and model endpoints in production environments. The book moves beyond generic API security to address risks unique to AI architectures.
It provides concrete threat models and control strategies tailored to systems that rely on embeddings, retrieval pipelines, and model-driven interfaces.
Readers will learn how to:
- Identify threats specific to vector databases and embedding stores
- Prevent leakage of sensitive prompts and embeddings
- Design access controls and authentication for AI APIs
- Apply rate limiting and abuse detection for model endpoints
- Use encryption and isolation to protect AI data flows
- Build secure API designs for AI-powered applications
This book helps teams reduce exposure across critical AI infrastructure and apply security controls that scale with modern AI workloads.
