AiSOC vs ADR
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.; ADR: ADR (Agentic Detection and Response) is an open-source framework designed to secure AI agents utilizing the Model Context Protocol (MCP). It comprises an ADR Sensor for telemetry collection and an ADR Detector, which serves as a dual-agent benchmark and detection system.
ADR is research code for detecting attacks on MCP agents. AiSOC is a broader security operations stack for your existing telemetry.
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 research detection for MCP-based agents and want to reproduce the MLSys 2026 ADR paper.
- You want a benchmark: ADR-Bench has 133 MCP servers and 303 tasks with baselines.
- You want to collect telemetry from Claude Code, Cursor and Codex into one schema with the sensor.
- You want a packaged product to deploy: this is research code for a paper.
- You want the ADR Explorer red-teaming engine: the README says it is not included.
- You cannot pay for Anthropic and OpenAI API calls: the default detector needs both.
Side-by-Side Comparison
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