User and Entity Behavior Analytics RFI/RFP questionnaire — 0-Doubt
User and Entity Behavior Analytics evaluation questionnaire
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.
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1. Weight what matters
26 criteria
baselineWhat baseline-building methodology does the platform use to establish 'normal' behavior per user/entity (statistical, machine-learning peer-group comparison, or both), and what is the minimum observation period before the platform produces reliable alerts?
baselineDescribe the false-positive rate for anomaly alerts at default sensitivity and the tuning workflow available to reduce alert fatigue, with a customer-referenced before/after alert-volume figure.
baselineWhat entity types beyond individual human users does the platform baseline and monitor — service accounts, devices, applications — and does correlating anomalies across multiple entity types (e.g., a user and a device acting anomalously together) improve detection confidence?
baselineHow does the platform distinguish malicious insider activity from compromised-credential (external attacker using valid credentials) activity, given both present as 'anomalous behavior from a valid account' — or does it not attempt this distinction?
baselineDetail data source breadth feeding the behavioral models — identity/authentication logs, network traffic, endpoint telemetry, cloud activity, SaaS app logs — and whether adding more data sources measurably improves detection accuracy with evidence.
baselineIs risk scoring per-entity cumulative over time (a rising risk score across multiple minor anomalies) or reset per-event, and how is a cumulative risk score presented to analysts to prioritize investigation?
baselineExplain integration with SOAR/SIEM for automated response to high-confidence anomalies (e.g., auto-triggering step-up authentication or session termination) versus alert-only output requiring manual analyst action for every detection.
baselineHow does the platform handle legitimate but unusual behavior (e.g., a role change, a new project, working from a new location during travel) to avoid persistent false alarms as an employee's normal pattern genuinely evolves?
baselineWhat is the pricing model — per user/entity monitored, per data source, or a flat enterprise tier — and how does cost scale as the organization's monitored-entity count (including non-human entities) grows significantly?
baselineFor a confirmed high-risk-score account, what investigation workflow does the platform support to quickly determine whether it's a genuine threat or a false alarm, and what is a customer-referenced average time-to-resolution for a flagged high-risk case?
baselineWhat compliance-evidence generation exists showing UEBA monitoring was active and effective (for a SOC 2 or similar audit), and is this a built-in exportable report or something the security team must assemble manually?
baselineDetail historical trend reporting on program effectiveness (confirmed-threat rate, false-positive-rate trend, alert-volume trend) over time, suitable for demonstrating to leadership that the program delivers real value.
baselineWho within the security team gets access to individual per-employee risk scores and behavioral findings, and is there a strict, documented access model given the sensitivity and potential employment consequences of this data?
baselineHow does this platform relate to the customer's existing insider-threat-detection tooling — is UEBA a genuinely distinct, broader behavioral-analytics layer, or does it substantially duplicate detection capability an insider-threat-detection platform already provides?
baselineDoes the platform support genuinely isolated, per-subsidiary behavioral baselines for a multi-entity organization with different normal-behavior patterns and risk tolerances, or is there only one flat, shared baseline across the whole organization?
baselineWhat is a customer-referenced onboarding timeline from deployment to the platform producing genuinely reliable, low-false-positive baselines across a large organization, and how does that timeline scale with organization size?
baselineUtilize identity enrichment and correlation for improved event context and detection fidelity.
baselineHighlight support for machine learning analytics, UEBA, threat intelligence ingestion, and open API integration to accommodate evolving needs.
baselineWhat are your analytic and correlation capabilities? Describe the continuum of automated processing and categorizing of threats within your system, and the validation tools/processes utilized.
baselineDoes your solution analyze and correlate data to identify security events and classify events according to severity? Are you able to correlate events across clients/endpoints? Can you correlate events by identity (user)?
baselineShould offer machine learning (ML) or AI-based behavioral analytics to detect anomalies.
baselineThe SOC solution must provide central management of all components and administrative functions from a single web-based user interface for SIEM, NBAD, and UBA; SIEM solution should correlate events and flows together to generate incidents.
baselineSolution should provide a UBA dashboard highlighting risky users based on objective composite risk scoring, with organization-defined risk thresholds; solution should collect user data from Directory Services, IAM, VPN, Proxy, O365, and incorporate baseline behavioural models covering data exfiltration, malicious users, illicit behaviour, and compromised credentials.
baselineContractor shall evaluate and implement emerging tools designated by the organization that utilize AIOps and behavioral analytics to automate incident containment and prevent attacks before they manifest, and support adoption of machine learning-based security analytics to identify patterns indicative of advanced persistent threats (APTs).
baselineSolution should detect multiple event types (inappropriate application use, fraud, advanced low-and-slow threats, APTs), collect Layer 7 network flow data from switches/routers/firewalls/DHCP, correlate global threat intelligence feeds against collected logs to identify IOC activity, and analyze user activity patterns for anomaly/behavioral detection across applications, hosts, users and network.
baselineProvide user behaviour analysis to identify potential internal and external threats; assist the Bank's active participation in cyber drills conducted under CERT-In/IDRBT; coordinate with internal/external contact groups to monitor, analyze and escalate security incidents, developing protect/detect/respond/recover capability for cyber-attacks.
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Platform baseline
neutral · staff-reviewed
RFIWhat baseline-building methodology does the platform use to establish 'normal' behavior per user/entity (statistical, machine-learning peer-group comparison, or both), and what is the minimum observation period before the platform produces reliable alerts?Answer key — what a strong answer shows
Look for a stated minimum baseline period (typically weeks) and be skeptical of claims of immediate, day-one accurate detection — behavioral baselines genuinely need observation time.
RFPDescribe the false-positive rate for anomaly alerts at default sensitivity and the tuning workflow available to reduce alert fatigue, with a customer-referenced before/after alert-volume figure.Answer key — what a strong answer shows
UEBA is notorious for alert fatigue if baselines are noisy; look for a concrete before/after alert-volume figure from a real deployment, not a generic 'reduces false positives' claim.
RFIWhat entity types beyond individual human users does the platform baseline and monitor — service accounts, devices, applications — and does correlating anomalies across multiple entity types (e.g., a user and a device acting anomalously together) improve detection confidence?Answer key — what a strong answer shows
Cross-entity correlation (user + device + app anomalies together) produces higher-confidence detections than single-entity-type analysis alone; ask for a concrete example of a cross-entity detection.
RFIHow does the platform distinguish malicious insider activity from compromised-credential (external attacker using valid credentials) activity, given both present as 'anomalous behavior from a valid account' — or does it not attempt this distinction?Answer key — what a strong answer shows
These two scenarios require different response playbooks; a vendor that can articulate how it differentiates (or honestly says it can't and leaves that to the analyst) is more credible than one claiming perfect automatic attribution.
RFPDetail data source breadth feeding the behavioral models — identity/authentication logs, network traffic, endpoint telemetry, cloud activity, SaaS app logs — and whether adding more data sources measurably improves detection accuracy with evidence.
From other buyers
crowdsourced · anonymized
💬
No buyer-contributed criteria yet
Verified buyers can suggest criteria (anonymized before pooling).
Answer key — what a strong answer shows
Broader data source integration generally improves UEBA accuracy but adds cost/complexity; ask the vendor to show evidence (not just claim) that additional sources meaningfully move detection rates.
RFIIs risk scoring per-entity cumulative over time (a rising risk score across multiple minor anomalies) or reset per-event, and how is a cumulative risk score presented to analysts to prioritize investigation?Answer key — what a strong answer shows
Cumulative risk scoring surfaces slow-burn insider threats or low-and-slow attacks that no single event would trigger; a purely per-event model may miss these patterns entirely.
RFPExplain integration with SOAR/SIEM for automated response to high-confidence anomalies (e.g., auto-triggering step-up authentication or session termination) versus alert-only output requiring manual analyst action for every detection.Answer key — what a strong answer shows
Automated response for high-confidence detections reduces mean-time-to-contain; ask specifically which response actions are automatable versus which always require manual analyst approval.
RFIHow does the platform handle legitimate but unusual behavior (e.g., a role change, a new project, working from a new location during travel) to avoid persistent false alarms as an employee's normal pattern genuinely evolves?Answer key — what a strong answer shows
Look for adaptive re-baselining or explicit context input (HR system integration for role changes) rather than a static baseline that keeps flagging the same legitimately-changed behavior indefinitely.
RFIWhat is the pricing model — per user/entity monitored, per data source, or a flat enterprise tier — and how does cost scale as the organization's monitored-entity count (including non-human entities) grows significantly?Answer key — what a strong answer shows
Look for transparent, predictable scaling economics; a vendor unable to project cost at meaningfully higher entity count creates real budget risk for a growing monitored population.
RFPFor a confirmed high-risk-score account, what investigation workflow does the platform support to quickly determine whether it's a genuine threat or a false alarm, and what is a customer-referenced average time-to-resolution for a flagged high-risk case?Answer key — what a strong answer shows
Strong answers describe a real, efficient investigation workflow with a concrete time-to-resolution figure, not just confirmation that risk scoring exists.
RFIWhat compliance-evidence generation exists showing UEBA monitoring was active and effective (for a SOC 2 or similar audit), and is this a built-in exportable report or something the security team must assemble manually?Answer key — what a strong answer shows
Native compliance-evidence generation is materially more valuable than raw alert logs requiring manual compilation for every audit cycle.
RFPDetail historical trend reporting on program effectiveness (confirmed-threat rate, false-positive-rate trend, alert-volume trend) over time, suitable for demonstrating to leadership that the program delivers real value.Answer key — what a strong answer shows
Trend-over-time program-effectiveness reporting is a distinct capability from individual alert investigation — confirm this exists as a maintained, exportable report.
RFIWho within the security team gets access to individual per-employee risk scores and behavioral findings, and is there a strict, documented access model given the sensitivity and potential employment consequences of this data?Answer key — what a strong answer shows
Per-employee behavioral risk data is uniquely sensitive with real employment consequences if misused or leaked internally — ask for a specific, strict access-control model, not just a general RBAC claim.
RFIHow does this platform relate to the customer's existing insider-threat-detection tooling — is UEBA a genuinely distinct, broader behavioral-analytics layer, or does it substantially duplicate detection capability an insider-threat-detection platform already provides?Answer key — what a strong answer shows
This is a real, common buyer question given the significant functional overlap between UEBA and insider-threat-detection products — a vendor should give an honest answer about the boundary and complementarity.
RFPDoes the platform support genuinely isolated, per-subsidiary behavioral baselines for a multi-entity organization with different normal-behavior patterns and risk tolerances, or is there only one flat, shared baseline across the whole organization?Answer key — what a strong answer shows
Genuine multi-entity isolation is materially more useful for a diversified organization than one shared baseline forcing a one-size-fits-all behavioral model.
RFIWhat is a customer-referenced onboarding timeline from deployment to the platform producing genuinely reliable, low-false-positive baselines across a large organization, and how does that timeline scale with organization size?Answer key — what a strong answer shows
Strong answers give a concrete, customer-validated timeline that accounts for realistic organization size, not just a small-pilot baseline-establishment figure.
RFPUtilize identity enrichment and correlation for improved event context and detection fidelity.
RFPHighlight support for machine learning analytics, UEBA, threat intelligence ingestion, and open API integration to accommodate evolving needs.
RFPWhat are your analytic and correlation capabilities? Describe the continuum of automated processing and categorizing of threats within your system, and the validation tools/processes utilized.
RFPDoes your solution analyze and correlate data to identify security events and classify events according to severity? Are you able to correlate events across clients/endpoints? Can you correlate events by identity (user)?
RFPShould offer machine learning (ML) or AI-based behavioral analytics to detect anomalies.
RFPThe SOC solution must provide central management of all components and administrative functions from a single web-based user interface for SIEM, NBAD, and UBA; SIEM solution should correlate events and flows together to generate incidents.
RFPSolution should provide a UBA dashboard highlighting risky users based on objective composite risk scoring, with organization-defined risk thresholds; solution should collect user data from Directory Services, IAM, VPN, Proxy, O365, and incorporate baseline behavioural models covering data exfiltration, malicious users, illicit behaviour, and compromised credentials.
RFPContractor shall evaluate and implement emerging tools designated by the organization that utilize AIOps and behavioral analytics to automate incident containment and prevent attacks before they manifest, and support adoption of machine learning-based security analytics to identify patterns indicative of advanced persistent threats (APTs).
RFPSolution should detect multiple event types (inappropriate application use, fraud, advanced low-and-slow threats, APTs), collect Layer 7 network flow data from switches/routers/firewalls/DHCP, correlate global threat intelligence feeds against collected logs to identify IOC activity, and analyze user activity patterns for anomaly/behavioral detection across applications, hosts, users and network.
RFPProvide user behaviour analysis to identify potential internal and external threats; assist the Bank's active participation in cyber drills conducted under CERT-In/IDRBT; coordinate with internal/external contact groups to monitor, analyze and escalate security incidents, developing protect/detect/respond/recover capability for cyber-attacks.
