ACTION GATE — AI Agent Security

ACTION GATE is a security gateway designed to make AI agents safer when interacting with business tools and data. AI agents can perform useful tasks, but allowing them to execute actions without proper checks can lead to unauthorized changes, excessive refunds, or attempts to bypass security policies. Our project introduces a controlled layer between an AI agent and the tools it can access. Instead of allowing proposed actions to proceed blindly, ACTION GATE evaluates actions against configured security policies and determines whether they should be allowed, require human approval, or be blocked. The system includes an AI Agent Console where users can submit tasks and inspect the agent's proposed actions. Its consequence replay feature simulates the potential impact of a plan before execution, helping identify risky operations and explore safer alternatives. For example, a proposed refund of 9,999 exceeds our configured limit of 500, so the system flags it for approval and suggests a safer capped amount. The project also includes adversarial testing capabilities, configurable policies, and an audit log for reviewing security decisions and their evidence. ACTION GATE is built with Python, FastAPI, SQLite, Streamlit, and Pytest. It includes a simulated business environment and a web dashboard connected to a deployed backend. Our local automated test suite passed 52 tests. Our core principle is simple: AI proposes actions, but security rules determine what happens next. Future work includes a more complete human approval workflow, expanded security testing, and integrations with real-world business systems.