Accurately prevent insider threats

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.

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%.

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.