Start from a neutral baseline and add what matters to you. Criteria are labeled by source — the platform baseline is architecture-neutral; buyer-contributed criteria are shown separately.
This evaluation is stored in your browser only. We cannot see it, and it is not tied to any account. Save it to a link or create an account to keep it across devices — you can export it at any time either way.
Signing up adds sharing with your team, sending this as an RFP to vendors, and private document sharing. Nothing above is taken away, and nothing here is sent anywhere until you choose to.
Version-controlled, CI/CD-style detection-as-code is the current maturity bar; a GUI-only rule editor with no version history is a real gap for a serious detection-engineering practice.
An explicit pre-production testing workflow against real (or realistic) data is stronger than a "write and deploy" process with no validation step.
A real, queryable coverage-gap view is materially more useful and credible than an unsubstantiated alignment claim.
Automated, tracked detection-validation against simulated attacks is a strong, verifiable maturity signal — probe for a real customer example, not just theoretical capability.
No buyer-contributed criteria yet
Verified buyers can suggest criteria (anonymized before pooling).
Look for real lifecycle tooling (metrics per rule, ownership tracking); detection rule sprawl with no ownership or deprecation process is a common, costly failure mode this category should solve.
A common schema/write-once-deploy-everywhere model is materially stronger than maintaining separate, duplicated rule logic per underlying tool.
A stated rule-authoring-to-deployment turnaround time in response to a real emerging threat is stronger evidence than a static or slow-to-update rule library.
Integration with existing PR-based engineering workflows is stronger than a bespoke, siloed approval system detection engineers have to learn separately.
Look for transparent, predictable scaling economics; a vendor unable to project cost at meaningfully higher scale creates real budget risk for a growing detection program.
Incident-time severity differentiation is a distinct operational need from routine detection-engineering workflow — ask for a specific fast-track mechanism, not just a generic alert queue.
Native compliance-evidence generation from real detection coverage is materially more valuable than raw rule lists requiring manual compilation for every audit cycle.
Trend-over-time reporting is a distinct capability from a point-in-time coverage heatmap — confirm this exists as a maintained, exportable report.
Detection logic itself is sensitive — knowing exactly what isn't monitored is valuable reconnaissance for an insider or an attacker who gains internal access — role-based access control over this specific asset is an often-overlooked consideration.
This is a real, common buyer question given the functional overlap with SIEM-native rule authoring — a vendor should give an honest answer about the boundary and complementarity.
Look for an actively maintained, vendor-curated content source rather than an abandoned community forum — unmaintained shared detection rules can create false confidence in coverage that doesn't actually work.
Strong answers give a concrete, customer-validated migration timeline and describe real automation tooling; a vendor with no migration story is asking the customer to manually rebuild potentially years of accumulated detection logic from scratch.