Book Description:
"Base Model to Vertex: AI Security Evaluation Framework" is a comprehensive guide for organizations navigating the complex journey from selecting external Large Language Models to securely deploying them on Google Vertex AI.
This practical ebook equips data scientists, AI engineers, and compliance officers with a structured, security-first approach to onboarding foundation models. Discover how to implement rigorous scanning pipelines using industry-leading tools (Garak, Rebuff, and LMQL) to assess vulnerabilities, prevent prompt injection attacks, and validate model behavior before deployment.
With step-by-step implementation guidance—covering pre-acquisition due diligence, infrastructure setup, compliance validation, and post-deployment monitoring—you’ll learn to:
- Scan models for security risks, bias, and performance gaps
- Optimize and package models for Vertex AI compatibility
- Deploy with confidence using Docker, ONNX, and GCP best practices
- Establish continuous monitoring for drift, latency, and emerging threats
From BART model case studies to organizational governance frameworks, this book bridges the gap between raw AI potential and trustworthy, compliant production systems. Future-proof your AI initiatives by embedding security and accountability at every stage—because in enterprise AI, innovation must always go hand-in-hand with vigilance.
