Insider threats remain one of the most challenging security risks organizations face. Unlike external attackers who must breach perimeters, insiders already possess legitimate access to critical systems and data. They understand security controls, know where valuable assets reside, and can operate under the radar of traditional rule-based detection systems for extended periods.
User and Entity Behavior Analytics (UEBA) can detect insider threats, credential misuse, and lateral movement across hybrid and cloud environments, but only when behavioral baselines are comprehensive, accurate, and immediately actionable. This is where Sumo Logic’s historic baselining fundamentally changes how organizations approach insider threat detection.
The baseline problem in traditional UEBA
UEBA works by establishing what “normal” looks like for each user and entity, then flagging deviations that could indicate compromise or malicious intent.
UEBA establishes “normal” baselines for users and entities using machine learning, but traditional implementations face a fundamental limitation: they need time to learn.
Most UEBA systems require weeks or even months of observation before they can confidently establish behavioral baselines. During this learning period, detection capabilities remain limited, leaving organizations vulnerable. Even after the learning period, many systems rely on short rolling windows of recent activity, often just a few weeks, creating several problems:
- Seasonal variations in business activity skew baselines.
- Legitimate but infrequent behaviors trigger false positives.
- Sophisticated insiders who gradually escalate activities can “train” the system to accept increasingly risky behavior as normal.
Security teams either suffer through excessive false positives or risk missing genuine threats because baselines fail to capture the full spectrum of normal behavior.
How Sumo Logic’s historic baselining changes detection
Sumo Logic’s Cloud SIEM addresses these fundamental challenges through historic baselining, analyzing up to 90 days of existing log data to build comprehensive behavioral profiles immediately. Rather than forcing security teams to wait weeks for behavioral models to mature, the platform leverages historical activity data already stored in your environment.
This approach transforms the UEBA value proposition. Detections become effective in minutes rather than months. Baselines capture seasonal patterns, business cycles, and the full range of legitimate but infrequent behaviors. And security teams can deploy new behavioral detection rules without waiting through extended tuning periods. Below are several use cases where historic baselining makes it easy to detect and prevent suspicious activity.
Detecting account compromise
Account compromise represents one of the most critical UEBA-enabled SIEM use cases. UEBA can detect account compromise by modeling deviations from established user activity patterns, but the quality of those patterns depends entirely on the baseline depth.
Historic baselining excels at detecting account compromise through:
- Behavioral baselining: Establishes individualized patterns for login times, locations, and access methods across 90 days.
- Anomalous access detection: Flags deviations from historical patterns in real time.
- Lateral movement mapping: Correlates identity, endpoint, and network logs to detect post-compromise activity.
- Cross-telemetry correlation: Validates anomalies across multiple data sources to reduce false positives.
When a user suddenly accesses administrative tools they’ve never touched in 90 days, the system recognizes this as anomalous, even if the access occurred during normal business hours using legitimate credentials.
Insider threat and privilege abuse detection
Modern UEBA-enabled SIEM platforms detect insider threats through file access pattern analysis, mass-download monitoring, privilege escalation tracking, and peer-group baselining. Historic baselining maintains context across extended timeframes, enabling detection of subtle privilege escalation that traditional systems miss.
Here are some examples of potential insider threats:
- Employee begins accessing file types from departments they’ve never interacted with historically.
- A user who typically downloads 10 documents per week suddenly downloads 15 per day.
- Gradual requests for elevated privileges that individually appear innocuous but form a concerning pattern.
- Anomalous admin actions outside historical usage patterns.
The 90-day baseline window captures enough history to distinguish between legitimate variations and genuinely suspicious behavior, dramatically reducing false positives while improving detection fidelity.
Data exfiltration via novel channels
Data exfiltration by insiders rarely happens in obvious bulk transfers. Instead, sophisticated insiders exfiltrate data gradually by copying files to personal cloud storage, forwarding documents to personal email, or making incremental downloads that stay below automated threshold alerts.
UEBA can detect low-and-slow exfiltration patterns by establishing what normal data access and transfer volumes look like for each user over extended periods.
Advanced detection capabilities include:
- Cross-source correlation: Combines signals from email, cloud storage, endpoints, and network traffic.
- Anomaly-based scoring: Identifies deviations from established access and transfer patterns.
- Narrative timelines: Build comprehensive incident stories showing how data moved through the environment.
- Creative channel detection: Spots exfiltration via cloud sharing, unsanctioned SaaS, and encrypted channels.
Baselines capture context about what types of data users typically access, enabling detection of lateral data access that signals reconnaissance or preparation for theft.
Insider credential sharing and collusion detection
UEBA detects credential sharing and account misuse by establishing detailed patterns around how specific accounts behave—not just what they access, but when, from where, and through which systems.
Detection techniques include:
- Cross-entity correlation: Links unusual patterns across multiple user accounts.
- Device and account sharing patterns: Identifies when multiple users access from the same device.
- Peer-group deviation analysis: Compares behavior against similar roles to spot anomalies.
- Access pattern profiling: Establishes when and how each account typically operates.
The system can identify shared admin terminals, multiple logins from a single host in rapid succession, rapid privilege switches between accounts, and geographic or temporal access patterns inconsistent with known user locations.
Risk-based alert prioritization for SOC efficiency
Dynamic threshold tuning adapts detection sensitivity to reduce false positives over time, but historic baselining enables risk-based scoring from day one. Risk-based alerting combines UEBA anomaly signals based on 90-day behavioral baselines, threat intelligence correlation, asset criticality data, and historical incident context.
Operational gains include:
- Improved mean time to investigate through reduced false positives
- Reduced analyst fatigue from high-confidence alerts
- Streamlined escalation via automated incident timelines
- Better resource allocation, focusing experts on sophisticated threats
Automated response and SOAR integration
UEBA often integrates with security orchestration, automation, and response (SOAR) to automate responses, like isolating endpoints or revoking credentials. When historic baselining detects high-confidence insider threat signals, automated playbooks can isolate compromised endpoints, revoke suspicious credentials, create enriched incident cases with historical context, launch investigation playbooks, and escalate to analysts with all relevant context pre-assembled.
The combination of historic baselining and SOAR integration fundamentally changes incident response timelines, enabling security teams to contain insider threats in minutes rather than hours or days.
Final thoughts
Insider threats will remain a persistent challenge as long as organizations grant employees and partners access to sensitive systems and data. The question is not whether you’ll face insider risk, but whether you’ll detect and respond to it effectively before significant damage occurs.
UEBA puts the odds in your favor. By leveraging existing historical data to build rich behavioral baselines immediately, Sumo Logic Cloud SIEM enables security teams to deploy effective insider threat detection without waiting through extended learning periods. Per-entity baselines capture individualized behavior patterns with precision. Percentile-based outlier detection adapts to your environment automatically. And First Seen rules identify genuinely novel behaviors worth investigating.
For security leaders evaluating UEBA-enabled SIEM platforms, establishing behavioral baselines from historical data is key for effective insider threat detection in modern hybrid cloud environments.
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