Security context engine that feeds grounded evidence to AI agents
cerebro, developed by Writer, is a security and compliance context engine that bridges enterprise security systems and LLM-based agents. The tool supplies real-time, evidence-backed context through a Model Context Protocol (MCP) server so agents and analysts can ask what changed, which actions are risky, and which policies apply. Operator surfaces include a web UI, a CLI, and a Slack companion, and the project is open-source with a Go-based runtime.
Acts as a runtime evidence layer for agentic AI
The tool converts raw signals from cloud, identity, and SaaS systems into structured, queryable evidence so autonomous agents and analysts can make grounded decisions. It implements a Model Context Protocol (MCP) server to let models request security posture, identity risk, and compliance evidence in real time. The stated operational outcomes include answering what changed, which resources are exposed, and which policies govern an action.
Produces evidence-backed outputs that reduce unsupported assertions
Evidence-backed context generation is a core capability, presenting traceable items agents can cite when reasoning about security events. That approach reduces the chance of unsupported assertions in automated workflows, but the reliability of generated evidence aligns with the completeness and timeliness of upstream telemetry and vendor feeds. When source feeds are incomplete or delayed, the produced evidence reflects those gaps rather than inventing details.
Integrates with security stacks through an extensible connector catalog
The architecture centers on an extensible connector catalog that ingests data from third-party vendors and cloud platforms. Examples of supported integrations include:
- Wiz
- CrowdStrike
- AWS
- GitHub
Operators can extend the catalog for additional tools. The tool is not a replacement for SIEM or SOAR products; it functions as an evidence contract layer that sits above those investments so agents can query consolidated security state.
Operator surfaces and runtime match SecOps and developer workflows
The project exposes multiple operator surfaces: a web-based operator UI, a command-line interface, and a Slack companion for chat-driven queries. The runtime is written in Go and is presented as a deployable component compatible with environments that support MCP. The open-source monorepo model lets engineering teams inspect runtime behavior, customize source catalogs, and adjust configuration semantics for their operational needs.
Best suited to SecOps teams building autonomous-agent workflows
The tool is a practical option for SecOps teams and AI developers who require grounded, queryable security evidence for agentic automation; it improves the defensibility of automated decisions. Expect engineering effort to extend connectors and validate upstream feeds before delegating critical actions to agents. Teams that can commit to connector maintenance and operational validation gain the most value from the project.
