If you’ve been building in the AI space recently, you’ve probably played around with the Model Context Protocol (MCP). It is a massive step forward in standardizing how LLMs interact with external tools and data.
But as we transition from chatbots to fully autonomous agentic workflows—where agents take actions inside CRMs, databases, and production environments—a glaring problem is emerging: Trust.
Right now, connecting an agent directly to your tools often grants it "God Mode."
If you give an LLM direct access to an enterprise API, how do you prevent it from dropping a table, emailing the wrong client, or exposing PII in its context window? You can't rely on the LLM to govern itself via prompt engineering.
The "What": Enter the Agent Access Security Broker (AASB)
We realized that agents need the equivalent of an API Gateway or a Cloud Access Security Broker (CASB). We call this an Agent Access Security Broker (AASB).
SecuriX is built to sit directly between the AI Agent and the Enterprise/Private Data. It acts as a "Secure MCP" middleware layer.
Instead of your agent talking to your database, it talks to SecuriX. SecuriX then executes the action based on strict policies.
Building the Trust Layer in Public
The transition to the Agentic Era won't happen until enterprise security teams trust the infrastructure. We are currently building this AASB category out in the open.
If you are dealing with these MCP limitations, trying to secure your agentic workflows, or just interested in the architecture behind AI security, I am documenting the entire journey, the technical hurdles, and the solutions in a daily series.
You can follow along and read the technical deep-dives here: #30DaysOfTrust - Building the Agent Access Security Broker
I’d love to hear from other devs building agentic tools.
Top comments (0)