Staff Engineer explains why agentic AI tools demand a security-first rethink

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A Staff Engineer writing on DEV Community describes how his focus shifted from accelerating AI agent delivery to evaluating the security risks those agents introduce. Working daily with LLMs, MCP servers, and RAG-based memory systems, he noticed he lacked the same rigour for reviewing permissions and attack surfaces that he applied to reliability and observability. He argues that when an AI model gains access to tools — such as file systems, APIs, or automation pipelines — the risk profile changes fundamentally from generating bad text to executing irreversible actions. To address this gap, he began studying frameworks including the OWASP Top 10 for LLM Applications and the NIST AI Risk Management Framework. As a practical starting point, he shares a checklist for teams to assess tool permissions before deploying any agent, emphasising least-privilege access and the need for human confirmation on irreversible operations.

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