baselineRFIDoes the platform support detection-as-code — version-controlled detection rules with CI/CD-style testing and deployment — or is rule authoring done through a GUI with no version history or automated testing?weight 3
Answer key — what a strong answer shows
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.
baselineRFPDetail how detection rules are tested for both true-positive efficacy (does the rule actually fire on the attack technique it targets) and false-positive rate against real production data, before being promoted to production, with a customer reference for the testing workflow.weight 3
Answer key — what a strong answer shows
An explicit pre-production testing workflow against real (or realistic) data is stronger than a "write and deploy" process with no validation step.
baselineRFIHow does the platform map detection coverage to MITRE ATT&CK, and can it show a genuine coverage-gap heatmap (which techniques have zero, partial, or full detection coverage) rather than just a marketing claim of "ATT&CK aligned"?weight 3
Answer key — what a strong answer shows
A real, queryable coverage-gap view is materially more useful and credible than an unsubstantiated alignment claim.
baselineRFPExplain how the platform supports purple-teaming/detection validation — can a simulated attack (atomic test, adversary emulation) be run and automatically checked against whether the expected detection actually fired, with results tracked over time?weight 3
Answer key — what a strong answer shows
Automated, tracked detection-validation against simulated attacks is a strong, verifiable maturity signal — probe for a real customer example, not just theoretical capability.
baselineRFIWhat is the process for detection content lifecycle management — deprecating noisy or outdated rules, tracking rule performance/false-positive rate over time, and attributing ownership of each rule to a specific engineer or team?weight 3
Answer key — what a strong answer shows
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.
baselineRFPDescribe how the platform integrates with and normalizes data from multiple detection sources (SIEM, EDR, cloud logs) into a common schema so detection rules can be written once and deployed across sources, versus requiring separate rule syntax per tool.weight 3
Answer key — what a strong answer shows
A common schema/write-once-deploy-everywhere model is materially stronger than maintaining separate, duplicated rule logic per underlying tool.
baselineRFIHow does the platform help detection engineers stay current with emerging threats — curated/community rule feeds, threat-intel-driven rule suggestions — and how quickly can a rule be authored and deployed in response to a newly disclosed technique or active campaign?weight 3
Answer key — what a strong answer shows
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.
baselineRFPExplain collaboration and review workflow for detection content — peer review/approval before production deployment, and audit trail of who changed what rule and why — and whether this integrates with existing engineering workflows (e.g., pull requests) rather than being a bespoke approval system.weight 3
Answer key — what a strong answer shows
Integration with existing PR-based engineering workflows is stronger than a bespoke, siloed approval system detection engineers have to learn separately.
baselineRFIWhat is the pricing model — per detection rule, per data source, or a flat platform tier — and how does cost scale as the organization's rule library and data-source count both grow?weight 3
Answer key — what a strong answer shows
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.
baselineRFPWhen a detection rule fires during a genuine, active incident, is there a fast-track escalation path distinct from routine alert triage, and how does the platform help an analyst quickly determine 'is this the real thing' versus a lower-priority test/tuning-phase alert?weight 3
Answer key — what a strong answer shows
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.
baselineRFIWhat compliance-evidence generation exists showing detection-coverage evidence (e.g., 'we actively monitor for X technique') for an audit or regulatory requirement, and is this a built-in exportable report or something the security team must assemble manually?weight 3
Answer key — what a strong answer shows
Native compliance-evidence generation from real detection coverage is materially more valuable than raw rule lists requiring manual compilation for every audit cycle.
baselineRFPDetail historical trend reporting on detection-program maturity (ATT&CK coverage-percentage trend, rule false-positive-rate trend) over time, suitable for demonstrating program progress to leadership.weight 3
Answer key — what a strong answer shows
Trend-over-time reporting is a distinct capability from a point-in-time coverage heatmap — confirm this exists as a maintained, exportable report.
baselineRFIWho within the organization gets access to the detection-rule library and logic itself, and is there role-based access control given that a complete view of what is and isn't being detected is itself sensitive information (revealing SOC blind spots to an insider threat)?weight 3
Answer key — what a strong answer shows
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.
baselineRFIHow does this platform relate to rule-authoring capability the customer's existing SIEM/XDR already has natively — is this a genuinely distinct, complementary detection-engineering layer, or does it substantially duplicate what the SIEM's own rule engine already provides?weight 3
Answer key — what a strong answer shows
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.
baselineRFPIs there a community or marketplace for sharing detection content across organizations, and how vetted/maintained is that shared content versus being unmaintained community contributions that could embed outdated or incorrect assumptions?weight 3
Answer key — what a strong answer shows
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.
baselineRFIWhat is a customer-referenced onboarding timeline for migrating an existing detection-rule library (from a legacy SIEM or a spreadsheet-tracked rule set) into the platform, and how much of that migration can be automated versus requiring manual rule-by-rule rewriting?weight 3
Answer key — what a strong answer shows
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.
baselineRFPDevelop, implement, and continuously refine detection rules, alert thresholds, and automated response playbooks tailored to the agency's environment and risk profile (Detection Engineering and Tuning).weight 3
baselineRFPCustomize and tune analytics rules to reflect the agency's environment, risk profile, and operational priorities, including suppression logic, false positive reduction, and alert prioritization (Detection Tuning and Rule Development).weight 3
baselineRFPHow are you developing detections that exceed commodity threat intelligence?weight 3
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