Detection Engineering

Turn detection engineering from a backlog into a continuous program. Mave finds coverage gaps, tunes noisy detection rules, creates new detections from threat intelligence, hunts, and investigations, and keeps detection content aligned with the threats and systems relevant to your environment.

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OUR SOLUTION

Mave works across detection languages and formats, including SPL, KQL, YARA-L, Sigma, EQL, Lucene, and SQL-based rules. It also connects to detection-as-code repositories in GitHub, GitLab, Bitbucket, and Azure DevOps.  

Agents review, create, translate, test, and tune detection content in your existing workflow, validate it against historical data where it already lives, and commit or deploy approved changes.

PROOF OF VALUE

Increase relevant coverage and reduce alert noise.

Coverage

Percentage of in-scope MITRE ATT&CK techniques covered by validated detections.

Gaps Validated

Number of missing detections confirmed against required techniques and data sources.

Rules Tuned

Number of POC detection rules tuned and validated against historical data.

Precision Rate

Confirmed true positives divided by all alerts generated by each tested detection.

Detections Created

Number of new detections that pass agreed validation criteria in the POC.

Deployment Time

Median time from an approved detection requirement to a deployed rule.