Accurately prevent insider threats

Challenges with insider threats
Too many alerts with little value
Existing tools and services fail to understand business context and adapt to a remote first and cloud first company, creating noisy alerts for common user behaviors, overwhelming analysts.
Frequently missed threat signals
Rule-based and UEBA systems fail to detect subtle, multi-step insider activity, especially across cloud apps, roles, and data stores.
Fragmented investigations
Logs, permissions, HR related information live in separate silos, forcing analysts to piece together the attack narrative of an insider threat from scratch.
Slow response gaps
Actions like revoking access, rotating keys and documenting steps require manual console work, giving malicious insiders time to move, escalate, or cover their tracks.
How Exaforce empowers your SOC for insider threats
AI that understands user behavior and business context to empower your SOC or our service to detect, investigate, and remediate insider risks.
Exaforce makes insider threats visible with full context and fewer false alarms.


Fewer false positives, more real findings
Exabots learn your organization's normal work patterns to eliminate false positives from legitimate business activities, reducing alert noise while preserving genuine insider threats.


Detects subtle insider behaviors
Anomaly detection blends time of day, peer group baselines, business context, and watchlists, highlighting when sensitive actions actually matter.


Contextual insider threat hunting
Combines cloud logs, permissions, and HR data into one insider threat narrative, cutting hours of manual work and instantly revealing the full story from compromise to exfiltration.


Faster, smarter containment
Exaforce streamlines critical actions like revoking access, rotating keys, and documenting steps, executed automatically or with analyst oversight, cutting response times and stopping insiders before they escalate or hide.
Frequently asked questions
Sophisticated insiders avoid detection by distributing malicious activities over time, accessing data gradually, or blending actions with legitimate work. Traditional rule-based systems miss these campaigns by evaluating individual actions in isolation. Exaforce detects multi-step insider threats by tracking behavioral patterns over extended periods including escalating data access volumes, expanding resource scope beyond role requirements, or changing access times, correlating reconnaissance activities like unusual queries or system exploration with subsequent data access or exfiltration attempts, identifying data staging behaviors where sensitive information is collected, consolidated, then moved to external destinations, and applying watchlists and risk scoring that continuously evaluate user behavior against evolving threat indicators. The Knowledge Model connects these distributed signals into coherent attack narratives revealing insider campaigns that unfold across weeks or months.
Traditional UEBA tools were designed for on-premises environments with human users and struggle with modern cloud-first, remote-first organizations. Exaforce addresses UEBA limitations through cloud-native architecture that understands distributed work patterns, remote access, and SaaS-centric operations, multi-dimensional anomaly detection evaluating time, peer group, resource type, access volume, and data sensitivity simultaneously rather than single-metric baselines, continuous adaptation to changing work patterns within hours rather than requiring months of manual tuning, and unified telemetry across endpoints, cloud, SaaS, and identity systems rather than fragmented user activity logs.
Generic insider threat tools lack understanding of organizational structure, project work, or legitimate business processes, generating false positives for normal activities. Exaforce incorporates business context through natural language inputs, connections to knowledge management software, and integration with HR systems to understand role changes, terminations, performance issues, or organizational transitions, project-based access patterns that explain temporary elevated permissions or unusual data access, approved contractor and third-party access that differs from employee baselines, maintenance windows and operational activities that involve privileged actions, and organizational hierarchy to evaluate whether access patterns align with reporting relationships and job functions. This business-aware detection enables Exabots to distinguish high-risk insider activity like pre-termination data theft from legitimate business needs requiring sensitive access.
Traditional insider threat tools generate overwhelming alert volumes by flagging routine activities in remote-first and cloud-first organizations, such as accessing sensitive data for legitimate projects, working unusual hours across time zones, or downloading files for authorized business purposes. Exaforce eliminates this noise through multi-model AI that works in concert. The Semantic Model resolves user identities, resource relationships, and access permissions across all systems to understand what the user legitimately can and should access based on their role and project assignments. The Behavioral Model learns individual and peer group work patterns including typical access times, resource usage, and data handling behaviors to identify genuine anomalies. The Knowledge Model then synthesizes these inputs with business context rules for expected activities like contractor access, project-based data needs, or role transitions, reasoning over combined signals to alert only when multiple anomalous behaviors cluster together in threat-relevant patterns. Instead of alerting on individual deviations from baseline, this multi-model approach evaluates comprehensive context to distinguish genuine insider threats from benign work pattern variations, reducing false positives by 70-80%.
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