SECURING AI-ASSISTED DEVELOPMENT FROM IDE TO PRODUCTION WITH JFROG AND GITHUB
AI coding agents like GitHub Copilot, Claude Code, and Cursor are shipping production code at machine speed. However, rapid code generation introduces critical software supply chain risks, including hallucinated dependencies, over-privileged tools, and shadow AI.
This practitioner’s reference guide provides actionable architectures, governance controls, and best practices to help security and engineering teams safely adopt agentic workflows using JFrog and GitHub.
What you’ll learn:
THE FRICTION POINTS OF UNMANAGED AI ADOPTION
- Agentic Reference Architecture: The seven core components required to build a safe, scalable AI development stack.
- Concrete Guardrails: How to enforce package curation, scope MCP permissions, and block malicious open-source packages at the perimeter.
- Unified Source & Binary Security: Combine source scanning with binary analysis in GitHub Code Security to reduce vulnerability noise by up to 50%.
- Automated Remediation: Implement agentic workflows that automatically identify, prioritize, and fix security findings.
- A Staged Rollout Plan: A step-by-step roadmap to transition safely from a single repo to enterprise-wide production.
Download the eBook to learn how to accelerate AI-assisted development while maintaining complete software supply chain integrity.