Most detection programs stall in the same place. Someone reads a report, writes a rule, pastes it into a console, and nobody checks it again. Six months later half the library has never fired and the other half fires forty times a shift.
We build the pipeline that removes that failure mode: research grounded against ATT&CK, every rule validated against your own telemetry before it merges, and the whole library shipped as code into Splunk, Sentinel, Elastic, CrowdStrike or Defender.
A model does not decide what is malicious here. It drafts, it maps, it refactors and it tests. Your telemetry and a human reviewer decide what ships.
A general model does not know your field names, your index layout, or which log sources actually populate. We tune against your schema, your existing rule corpus and the query dialect your team writes, so the output compiles instead of inventing fields.
Every rule compiles to each target backend, runs its behavioural test, and replays against your telemetry. Produce no true positive, or more noise than your threshold allows, and the pull request fails.
One rule source, compiled into the language your team already reads. Splunk, Sentinel, Elastic, CrowdStrike and Defender. The pipeline runs in your CI, against your data, not in a portal we own.
An analyst writes the query by hand
Pasted straight into one console
No test, no owner, no re-check
Schema changes and the rule dies silently
groundGrounded against ATT&CK, citations locked
validateSchema and field existence checked per backend
testBehavioural harness must produce a real hit
mutationCan the rule be trivially evaded?
coverageATT&CK delta, regression blocks the merge
exportOne source compiled to five backends
Writing the rule is the small part. We automate the research, the testing and the maintenance that come after it.
Rules are drafted against your real schema and log sources, then checked that every field they reference actually exists before anyone reviews them.
Hunt tier logic runs on a schedule, baselines itself against your environment, and escalates only what survives triage.
New adversary reporting is grounded against ATT&CK, checked against coverage you already have, and only then turned into candidate detections with tests attached.
Rules live in Git, compile to five backends from one source, and are blocked at the pull request if any gate fails.
We map how detection content moves through your organisation today, measure where it decays, and hand you the target architecture.
We build the repository, the CI and the validation harness, deploy it against your live stack, and hand it over with the runbook.
We tune against your schema and rule corpus, then keep the pipeline accurate as the environment drifts.