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Insider Threat

Identify Risky and Suspicious Insider Behaviors and Malicious Threats

CYBER SECURITY CHALLENGES

Detecting Insider Threats is a major cyber security challenge for many organizations. Insiders are already within the perimeter, past the firewalls and remote access controls, which makes them much harder to detect and prevent. When a user inadvertently exposes an organization to security threats, that can be extraordinarily difficult to detect by traditional monitoring.

Worse, a malicious insider already knows exactly where to find the most valuable information so they can freely access or even grant access with little risk of being stopped or caught. In either case, the real challenge is finding and deploying the most effective insider threat solutions. Key to determining the best insider threat detection tools is finding a solution that can identify risky and anomalous behaviors, determine their severity, and predict whether they could cause damage or whether malicious activity is about to occur or is currently taking place.

CAPABILITIES OF GURUCUL INSIDER THREAT SOLUTION

The Gurucul platform is the core of any insider threat prevention program. It monitors an organization’s environment, natively ingests data across multiple data sources, including applications, and analyzes this data using advanced behavioral and insider threat machine learning (ML) models and data science. Gurucul can pinpoint unintended and malicious privilege access abuse, unexpected lateral movement and external communications, and data exfiltration.

Identity-centric Risk Modeling
Gurucul combs through every user, account, and entitlement, and links them together to provide a complete view of every user. The insider threat solution then correlates this human-centric behavioral information with information security data to surface anomalous activity.
Flexible Entity Model
Define your own entity-based risk profiles and monitor beyond users, devices, servers, and machines. For example, a sensitive document can be defined as entity and integrated with Gurucul’s behavioral-based approach with an analysis of the overall risk across multiple telemetry to detect misuse or unauthorized access to the document.
User Behavior Baselining, Analytics and Monitoring
Gurucul creates time-based behavioral baselines and continuously learns what is acceptable behavior to identify anomalous behavior as it continues to zero in on what are actual threats. By unifying collection and analysis of telemetry across the entire security stack and applying ML driven security analytics to collected data, Gurucul provides unprecedented context, behavioral indicators, and timeline views for automating threat assessment, mitigation, and response.
Peer-group Analytics
Gurucul natively supports static and dynamic peer-group definition and analytics. It automatically groups users to create baselines and detect unusual deviations from peer group baselines. It also supports advanced dynamic peer-groups, created on the fly at run time automatically based on feature data analysis and data cardinality.
Sentiment Analysis
As part of an overall insider threat program, Gurucul unifies data feeds from HR applications, social media, emails, website visits, and more to profile a user’s sentiment – to ascertain indicators of discontent prior to departing the organization or attempting to steal data / IP.

BENEFITS OF AN INSIDER THREAT SOLUTION

Detect suspicious behavior immediately
Identify high-risk profiles and threats
Monitor and manage insider risk
Work cross-functionally with the necessary context

Uncover Insider Threats through Predictive Security Analytics

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WHY GURUCUL?

Gurucul offers the largest library of pre-packaged ML models (2500+) including unsupervised, supervised, and deep learning algorithms. The models are pre-tuned to predict and detect insider threats aligned with specific use cases and vertical industries. Gurucul gives security teams a contextual view, linking behavior baselines from disparate systems including HR records, accounts, activity, events, access repositories, and security alerts.

The platform baselines and then analyzes who or what is on the network, what they are doing, what they have access to, and what they are doing with that access. Anomaly detection and predictive risk-scoring algorithms identify abnormal behaviors and activities associated with potential sabotage, data theft, or misuse.

INSIDER THREAT PROTECTION USE CASES

Privileged Access Misuse

Detect users misusing ‘superman’ privileges inappropriately or fraudulently.

Privileged access escalation on own account
Unusual account credential checkouts
Suspicious configurations changes on critical assets like S3 buckets, firewalls, group policies
Account manipulation / password resets
Clear system audit logs

Data Exfiltration

Detect unauthorized movement of Intellectual Property / Customer Data / Sensitive Information outside the corporate environment through various egress channels.

Unusual Documents Printed
Email to competitors or personal Ids
Data upload to personal cloud storage sites
Abnormal data transfer to removable media devices
Unusual movement of data to external unauthorized domains using FTP / SCP / other protocols

Account Compromise

Detect unusual account login patterns and potential compromise.

Suspicious or unusual login patterns from unseen device / segment / geo-location
Credential Stuffing, Brute-force attack
Unusual password resets
Account switching
Spear-Phishing