Insider threat prevention teams have a highly-specialized and critical job. Unfortunately, most of the technology they need to be effective has failed them thanks to:
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- Incomplete data silos producing piecemeal information that analysts must sift through after premature alerts lacking context.
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- Weak threat detections sending analysts down investigatory rabbit holes that turnout to be nothing case worthy.
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- Lack of context causing analysts to waste time with non-risky anomalies instead of true insider threat indicators.
Less Time Chasing is More Time Preventing
Unleash the human potential of your insider threat program, empowered by machine augmentation
Confirm Real Threat Indicators
Confirm insider threats before data exfiltration. Cross-validate relevant behavioral, identity, access, security and HR data with behavioral ML models to build defensible cases of high-fidelity indicators.
Collaborate More Effectively
Focus more time on collaborating confidently with HR, legal and business stakeholders empowered by defensible and cohesive case data.
Customize to Your Program
Embrace the flexibility to customize security analytics aligned to YOUR program. Ingest the right data, develop bespoke Machine Learning detections and create playbooks for your workflows.
Detect and Prevent True Insider Threats
Right Context. Right Time. Right Action.
The Gurucul converged security analytics platform is purpose-built to help organizations effectively manage the entire insider threat lifecycle.
Breaking down data silos is critical for insider threat teams. Converge the telemetry of user behavior, identity and access, security events and HR/business application data from any environment and any data lake. With Gurucul you get full insider threat visibility in days, not months, with streamlined data ingestion, parsing and normalization.
Continuous and adaptive behavioral baselines allow you to quickly identify and validate risk that deviates from normal behavior. Gurucul combines advanced Identity Access and User & Entity Behavior Analytics with hundreds of pre-built and time-tested Machine Learning models to confirm actual insider threats, not just anomalies, saving your analysts time to focus on what matters most.
Context is king for Insider Threat teams who straddle the line of protecting their organization and adhering to privacy regulations. Gurucul delivers contextualized threat detections via enrichment from systems like firewalls, network and endpoint telemetry. By chaining different categories of analytics together to correlate data about identity, user, network, and security alerts you’re able to build defensible, accurate cases.
Armed with contextual Insider Threat detections you can work effectively with your HR, legal and business counterparts to monitor and mitigate true threats. Identify insider threat attacks that span multiple events and anomalies. All evidence linked to one identity can be combined into a single case, prioritized based on business risk and made available to your cross-functional counterparts via on-demand dashboards.
The Gurucul Difference: Converged Security Analytics and Machine Learning Detections
Monitor, detect and mitigate threats from malicious and compromised insiders faster with Gurucul’s insider threat solution, which converges the telemetry of UEBA, Identity & Access, HR application data and traditional security alerts.
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- Establish accurate, continuous and adaptive behavioral baselines
- Cross-validate behavioral anomalies against identity & access analytics, security event alerts and employee sentiment data from HR applications for higher fidelity detections
- Leverage the largest library of pre-packaged ML models (2500+) including unsupervised, supervised, and deep learning algorithms
“This tool helps drive innovative business flow while helping protect essential Company data.”
Security Analyst, Energy and Utilities company, Gurucul Gartner Peer Insights Review
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We Cover Every Insider Threat Use Case
Privileged Access Misuse
Detect misuse of privileged access and proactively protect systems and data from potential threats.
Data Exfiltration
Detect unauthorized movement of sensitive data leaving the network and enforce rules to prevent exfiltration.
Account Compromise
Protect against unauthorized access by detecting unusual account login patterns.
Flight Risk Users
Predict employees at risk of leaving and deploy proactive watchlist or DLP controls.
Remote Access Monitoring
Detect suspicious connections or unusual behavior patterns from remote workers or third parties.
FAQs
What is considered an insider threat?
What is the most common form of insider threat?
What are the signs of an insider threat?
How does an insider threat occur?
An insider threat occurs when an individual with authorized access to an organization’s resources, systems, or information misuses or abuses that access for malicious purposes. There are various ways in which insider threats can manifest. One common scenario is intentional actions, where an insider deliberately compromises security by stealing sensitive data, engaging in fraud, or sabotaging systems. Unintentional actions can also contribute to insider threats, such as employees falling victim to social engineering attacks or inadvertently causing security breaches due to negligence or lack of awareness. Insider threats can be facilitated through unauthorized access privileges, weak security controls, inadequate monitoring, or compromised credentials.
Helping Insider Threat Teams Detect Real Threats
Gurucul gives security teams a contextual view, linking behavior baselines from disparate systems including HR records, accounts, activity, events, access repositories, and security alerts.
