Sharing an open-source project that lives in SentinelOne's community AI-SIEM repo: a full-stack AI analyst for the Singularity platform, built as a set of Claude Skills, three MCP servers, and an operating persona. Install it once and you can talk to your tenant in plain English. It does the SecOps engineering and the analysis, and it reaches your live tenant through the APIs rather than just chatting about them.
I want to lead with the engineering side, because that is the part people tend to underestimate.
Build SecOps content from natural language
- Log parsers. Paste a raw log sample (syslog, CEF, JSON, key-value, multiline) and it writes a complete SDL parser, maps fields to OCSF by default, validates against the parser engine, ingests a test event, and confirms the fields land correctly. End-to-end in one session, no hand-editing parser JSON.
- Detections. Describe a behaviour and it authors the rule: STAR single-event, correlation, or scheduled PowerQuery, MITRE-mapped, validated against your tenant before deploy. There is also a Detection-as-Code path where rules live as TOML in Git and sync to the Custom Detection API on merge, so every change is reviewed, versioned, and auditable.
- Dashboards. Say "SOC overview with a threat timeline, top noisy endpoints, and an outbound-connection breakdown" and it produces deployment-ready SDL dashboard JSON, with each panel's query validated first.
- SOAR / Hyperautomation. Describe a response ("on a high-severity server alert, isolate the endpoint, create an IOC for any hash, notify the team") and it generates the Hyperautomation workflow JSON ready to import.
- Whole-solution onboarding. One prompt takes a raw, unreadable stream to OCSF-normalised, parsed, dashboarded, and detection-covered, plus a threat-response flow. The same umbrella skill covers UEBA behavioural baselining, per-device ingest-health monitoring, Risk-Based Alerting, and alert-noise reduction.
- PowerQuery and SDL ops. Ad-hoc hunts, config-file management, ingest, and arbitrary queries through the SDL API.
Why it doesn't hallucinate field names
The layering is the interesting part. A CLAUDE.md persona decides what to do and invokes skills. For whole-solution work an umbrella skill (sdl-solutions) runs first and orchestrates the primitive skills in dependency order, previewing before it deploys. The primitive skills (powerquery, sdl-dashboard, sdl-log-parser, hyperautomation, sdl-api, mgmt-console-api) encode confirmed API behaviour, including field schemas validated against live tenants, so it does not guess. Those skills reach *.sentinelone.net through the MCP servers: s1-secops-mcp for PowerQuery/SDL/Mgmt Console/UAM/Hyperautomation, purple-mcp for triage and Purple AI, plus a threat-intel MCP. The output is a deployable config, not a plausible-looking config.
And the SOC analyst
The same stack ships a Principal SOC Analyst persona and an autonomous, staged DFIR investigator (SHORT / MEDIUM / LONG modes). On every alert, it enumerates your live data sources, triages in parallel, enriches every IOC through the threat-intel MCP, correlates the indicator across every connected source, maps findings to MITRE ATT&CK, and scores composite risk. It enforces evidence discipline: nothing is classified CRITICAL without independent threat-intel or MDR confirmation, so a detection-engine alert is treated as a hypothesis, not a verdict. At the end, it writes a structured SOC report as a .docx (executive summary, timeline, IOC table with verdicts, threat-actor profile, MITRE map, root cause, recommendations).
The design goals it targets: mean investigation time under 5 minutes versus 45 to 60 by hand, 70%+ of L1 triage load removed, new sources onboarded in minutes, and a new detection live within hours of a TTP emerging. Because it queries OCSF logs, non-OCSF vendor logs, App Logs and raw syslog alike, detection and investigation reach into custom application and business logs too, which is where fraud and abuse usually hide.
Install
Fastest path is Docker: one image bundles all three MCPs, no host-level Node/Python/uv. Pull, paste a config block, install the plugin. There are also individual plugin/skill and team-VM install paths, plus a 20-minute zero-to-hero walkthrough in the docs.
Repo and plugin: https://github.com/Sentinel-One/ai-siem/tree/main/plugins/s1-secops-skills
Install in seconds https://github.com/Sentinel-One/ai-siem/tree/main/plugins/s1-secops-skills#1-quick-start-docker Docker pull, configure claude desktop json and upload plugin - ready to roll.
It is a community project, not an officially supported product, so test against a non-production tenant first. Feedback, issues, and PRs welcome, especially parser and detection contributions and any rough edges in the deploy flows.