Balancing speed and safety: A control framework for AI coding agents

This article presents a security control framework for AI coding agents that balances development speed with safety through author-time and build-time guardrails.

• Author-time controls shape agent output in the IDE using steering documents, specifications, MCP tool scoping, and automated hooks
• Build-time controls verify code in the pipeline with layered security scanning, quality gates, and AI-assisted review before production
• Seven key risks addressed: prompt injection, data disclosure, uncontrolled changes, supply chain issues, external access, hallucinations, and scope creep
• Framework combines deterministic checks (SAST, SCA, secrets detection) with non-deterministic review (LLM-assisted screening) and human judgment
• Kiro provides IDE-level guardrails; AWS CodeBuild and CodePipeline implement pipeline controls
• Steering documents encode security invariants as natural-language constraints; specifications define scope before code generation
• MCP servers provide controlled access to external tools with least-privilege credentials and allowlists

The framework enables organizations to adopt AI coding agents at scale while maintaining security through layered controls that prevent unreviewed risk from reaching production.