Why datRail?

As AI agents gain autonomous access to local file systems, personal/proprietary data via protocols like MCP, and third-party APIs, traditional static security rules quickly fall short. Agents are non-deterministic by design—predicting every API call, token threshold, or file touch in advance is nearly impossible without crippling their functionality. The datRail project solves this by introducing profile-driven runtime protection: it monitors passively your agent in a control/test run to build a precise baseline of its resource usage and data boundaries, called security profile.  Such a security profile is then used to detect deviations during agent runtime. By turning observed behavior into enforceable guardrails, datRail lets developers and power users deploy agentic workflows without giving agents a blank check to their critical data and infrastructure.

Features