AiSOC vs MCPJungle
AiSOC: AiSOC is an open-source, self-hostable AI Security Operations Center designed to ingest security telemetry, detect and correlate threats, and investigate incidents using auditable AI agents. It provides proposed responses that require human approval, ensuring secure and transparent operations.; MCPJungle: MCPJungle is an open-source, self-hosted gateway designed for managing and centralizing access to all your Model Context Protocol (MCP) servers and their tools. It allows developers to register and manage MCP servers, while AI clients can discover and consume these tools from a single unified "Gateway" MCP server, providing features like security, privacy, and access control.
TL;DR
- You want a self-hosted SOC where an AI agent investigates incidents, logs every prompt and tool call, and a human approves each response.
- You want to run with no API key: it bundles a local `llama3.2:3b` model.
- Your telemetry is in Splunk, Sentinel, Elastic, CrowdStrike, Okta, AWS, Wiz or Kubernetes, among 84 connectors.
- You cannot give it 8 GB memory and 20 GB disk for the core profile, or about 12 GB for the full one.
- You need a hosted model: the README says no hosted model provider has been tested.
- You need proven detection: 2,603 of 6,991 rules are executable, which proves reachability, not that they catch attacks.
- You want one gateway endpoint for many MCP servers, managed from a CLI.
- You want tool groups, global enable or disable of tools, and prompts.
- You want to start with docker compose, using SQLite by default and PostgreSQL for larger deployments.
- You need OAuth to upstream MCP servers: the README says it is not supported yet, and SSE support is described as immature.
- You need clear licence terms: the README text shows no licence, so check the repository.
- You want access control and OpenTelemetry metrics in the open build: the README attaches them to enterprise mode.
Side-by-Side Comparison
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