AI Detection EngineeringNew
A mature detection engineering pipeline reads threat intelligence every day, decides what applies to each environment, writes and tests rules, hunts retroactively, and reports the results. Most teams never build one because it consumes senior engineering time. On LimaCharlie, the pipeline runs as AI agents, and it ships working.
Ingest intelligence
Open-source threat feeds arrive daily, covering botnet IOCs, breach reports, CVE disclosures, and attacker TTPs. Adding a new source is a prompt edit, so indicators from a research paper published this morning can be in tonight's run.
Filter for relevance
Raw intelligence is cross-referenced against each environment's profile. A CVE on a platform no tenant runs gets deprioritized, and an IOC targeting Windows gets escalated for Windows-heavy tenants. The output is a per-environment picture of what matters today.
Write and test rules
A detection engineering agent writes rules for the filtered intelligence, writes unit tests for each one, and runs them out of band against the last week of retained telemetry per tenant, iterating on false positive suppression until the rules are ready. Engineers review and approve before anything goes live.
Hunt retroactively
A threat hunting agent searches every tenant for evidence the day's threats are already present. Findings open cases automatically, and clean environments get documented, which gives you an auditable record that each tenant was checked.
Report
Each customer gets a report covering what was found, what rules were written, whether they were affected, and what to patch. The team gets an aggregate view across all tenants.
Why it matters
This turns detection engineering into daily, demonstrable value. A CVE surfaces in the morning, and by the afternoon you can tell every customer: we saw it, we checked your environment, and a rule is now watching for it.
Deploy your first sensor on the free community tier, or walk through it with a solutions engineer.
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