User and entity behavior analytics (UEBA) platforms differ most in where their data comes from and how they score risk. SIEM-based UEBA, such as Exabeam, Gurucul and Securonix, layers analytics on top of third-party log data. Data-centric tools, such as Cyberhaven, Nightfall and Microsoft Purview, focus on data movement and offer lighter behavioral analytics. Purpose-built platforms, such as DTEX, collect behavioral data directly from the endpoint and combine UEBA, user activity monitoring (UAM) and data loss prevention (DLP) in one product.
The right choice depends on three things: whether you already run a SIEM, whether your priority is detecting insider intent or tracking data movement, and how much tuning effort your team can sustain.
UEBA platforms at a glance
| Vendor | Approach | UEBA depth | UAM | DLP | Data collection |
|---|---|---|---|---|---|
| DTEX | Insider risk management: behavioral intelligence, UAM and DLP in one platform | User behavior timelines, aggregate risk scoring, intent context | Yes | File lineage, inferred data sensitivity, forensics | Agent-based, continuous |
| Splunk | SIEM plus UEBA | Behavioral baselines, entity risk scoring, anomaly detection | No | No native support | Agentless, log telemetry |
| Exabeam | SIEM plus UEBA | Risk scoring, user timelines, anomaly detection | No | No native support | Agentless, log telemetry |
| Gurucul | SIEM plus UEBA | Contextual user timelines, dynamic risk scores | No | No native support | Agentless, log telemetry |
| Securonix | SIEM plus UEBA | Contextual risk scores for users and entities, baselines, anomaly detection | No | No content classification | Agentless, log telemetry |
| Cyberhaven | Data detection and response, DLP | Limited, with user timelines and risk scoring | No | Lineage, classification, content analysis | Agent-based |
| Microsoft Purview | Data governance | Some insider risk policies, timelines, risk scores | No | Lineage, classification, encryption and access controls | Agent-based (Defender) |
| Proofpoint ITM | Behavioral recording, activity timelines, threat monitoring | yes | Yes endpoint DLP | Classification, redaction | Agent-based, policy-based collection |
How UEBA approaches differ
SIEM-based UEBA
SIEM-based UEBA adds behavior analytics to a central log platform. It gives security teams one place to manage alerts, and it works well when a team already runs the SIEM. The common limits are:
- Data quality. It depends on third-party, low-level system data. Incomplete logs or inaccurate timestamps can cause false positives and missed detections.
- Ingestion effort. Diverse formats need parsing and normalization before events can be correlated.
- Ongoing tuning. Detection accuracy needs continuous adjustment.
- Baselines and scoring. Baselines flag “not normal” behavior, which does not by itself separate malicious activity from unusual but harmless activity. Risk scores can be one dimensional.
Data-centric tools
Data detection and response and cloud DLP tools track what happens to files and sensitive data. They are strong on lineage, classification and content inspection. Their behavioral analytics are typically lighter, so they answer “what happened to this data” more readily than “why is this person behaving this way.”
Purpose-built behavioral platforms
A purpose-built platform collects behavioral data at the endpoint, on and off the network, and builds risk models from it. DTEX takes this approach. It collects continuously rather than on triggers, aggregates activity into a single risk model per user, and is designed to work without manual tuning. When DTEX is deployed alongside a SIEM, it can add behavioral context to the SIEM’s alerts.
What to compare when choosing UEBA software
- Data source and quality. Does the platform rely on third-party logs, or does it collect its own behavioral data? Ask how much parsing and normalization is required.
- Baselines. Does the product only separate usual from unusual, or does it identify activity associated with data loss, such as reconnaissance, obfuscation and circumvention?
- Risk scoring. Is the score one dimensional, or does it use context to separate malicious, careless and compromised users?
- Tuning effort. How often will your team adjust rules and thresholds?
- Insider risk coverage. Does the platform include UAM and DLP, or do you need to integrate separate tools?
- AI usage visibility. Can it see GenAI tools, prompts and agents, or only activity within its own ecosystem?
- Deployment and endpoint impact. What is collected per user, and what is the performance cost?
- Pricing basis. Pricing models differ (per endpoint or per data volume), so compare total cost at your scale.
DTEX
DTEX is an insider risk management platform that combines UEBA, UAM and risk-adaptive DLP through one lightweight endpoint collector, with no separate UEBA add-on or DLP product to integrate. It collects high-fidelity metadata from data, machines, applications and people, builds behavior timelines, and applies multidimensional risk scores that look for early indicators of intent. DTEX covers AI tools, prompts and agents in addition to human activity.
- Best for: organizations that need to detect insider risk early, reduce false positives, and monitor human and AI activity in one platform.
- Consider: DTEX is agent-based, so it requires an endpoint deployment.
- Learn more: DTEX UEBA. A case study shows how a government agency reduced alert fatigue with behavioral context.
Splunk
Splunk provides SIEM with behavioral analytics that uses log and event telemetry to identify anomalous activity and build behavioral baselines.
- Best for: teams already using Splunk that want UEBA integrated into their existing security operations and SIEM workflows.
- Consider: behavioral analytics relies on the breadth and quality of ingested telemetry, and Splunk does not provide native UAM or DLP.
Exabeam
Exabeam combines SIEM with UEBA, risk scoring, user timelines and anomaly detection. It uses log telemetry rather than endpoint agents.
- Best for: teams standardizing on a SIEM-centered security operations workflow.
- Consider: it has no native DLP and relies on the quality of ingested log data.
Gurucul
Gurucul offers SIEM plus UEBA with contextual user timelines and dynamic risk scores, using log telemetry and events.
- Best for: teams that want UEBA tied to a broader security analytics platform.
- Consider: no native DLP, and no explicit prompt-level monitoring of GenAI use.
Securonix
Securonix provides SIEM plus UEBA with contextual risk scores, baselines and anomaly detection for users and entities.
- Best for: teams that want a cloud SIEM with built-in UEBA.
- Consider: no content classification, and GenAI monitoring requires its own GenAI agents within the Securonix ecosystem.
Cyberhaven
Cyberhaven focuses on data detection and response and DLP, with lineage, content analysis and classification. It includes user timelines and risk scoring but limited UEBA.
- Best for: teams whose main goal is tracking data movement and enforcing data policies.
- Consider: limited behavioral analytics and no UAM.
Microsoft Purview
Purview provides data governance, classification, and some insider risk policies, with user timelines and risk scores, drawing on Microsoft Defender and Microsoft 365 signals.
- Best for: Microsoft-centric environments that want governance and data protection in the same ecosystem.
- Consider: it can generate a large volume of signals with limited context, and it is endpoint focused rather than user focused.
Proofpoint ITM
Proofpoint ITM combines insider threat management and user activity monitoring and endpoint DLP, using an endpoint agent to provide behavioral context around risky actions.
- Best for: organizations who already have Proofpoint products and are interested in monitoring users through screen recording and keystroke capture.
- Consider: monitoring depth is policy-focused rather than based on the continuous behavioral telemetry model.
Which UEBA platform is best for your situation?
- You already run a SIEM and want analytics in the same console: SIEM-based UEBA from Exabeam, Gurucul or Securonix is a natural fit. Consider adding DTEX alongside the SIEM for deeper behavioral context.
- You need to detect insider intent early and cut false positives: a purpose-built platform such as DTEX.
- Your priority is data movement and classification: Cyberhaven, Nightfall or Purview.
- You need UEBA, UAM and DLP without integrating three products: DTEX.
- You need visibility into AI tool and agent usage: confirm each vendor’s GenAI coverage. DTEX covers AI tools, prompts and agents.
Frequently asked questions
UEBA (user and entity behavior analytics) software builds a baseline of normal activity for users and devices, then flags behavior that deviates from it or matches known risk patterns, so security teams can find insider threats and compromised accounts earlier.
They differ mainly in data source (third-party logs or direct endpoint collection), baseline depth, risk scoring approach, tuning effort, and whether they include UAM and DLP.
SIEM-based UEBA analyzes logs ingested into a SIEM and typically needs parsing, normalization and continuous tuning. DTEX collects behavioral data directly from the endpoint, continuously, and uses aggregate risk scores designed to avoid manual tuning. DTEX can also integrate with a SIEM to add behavioral context.
Anomaly detection alone flags anything unusual, and low-quality or incomplete data adds noise. Platforms that add context about intent, such as reconnaissance or obfuscation, and score risk across multiple dimensions, can separate risky activity from harmless variation.
DLP controls how data moves. UEBA explains the behavior around that movement, including early signs of intent. Many organizations use both, and some platforms, including DTEX, combine them.
Some can. Coverage varies, so ask whether a vendor monitors AI tool usage, prompts and agents outside its own ecosystem. DTEX covers AI tools, prompts and agents.
DTEX is designed to collect minimal telemetry, approximately 3 to 5 MB per user per day, with near zero impact on endpoint or network performance.
Compare data quality, baseline depth, risk scoring, tuning effort, insider risk coverage, AI usage visibility, endpoint impact and pricing basis, then run a proof of concept against your own use cases.
Find the signal in the noise without looking so hard. Request a demo to see how the DTEX Platform detects human, data and AI risk through behavioral intelligence. For a deeper look at the SIEM comparison, read the DTEX vs SIEM-based UEBA solution brief
