Insider Threat

Predict and Stop Abnormal User Behaviour Associated with Sabotage, Data Theft or Misuse

.

Download Whitepaper – “Uncover Insider Threats through Predictive Security Analytics”

Insider threat is the biggest cyber security problem for organizations because insiders tend to cause the most damage. They are also harder to detect and prevent in comparison to attacks from the outside. They have keys to the kingdom. They know where the sensitive company/customer data is and they have access to it. That means insiders know exactly where to strike if they decide to take action.

The Insider Threat by the Numbers

According to the 2021 Insider Threat Report produced by Cybersecurity Insiders, the insider threat is substantial:

Virtually all organizations feel vulnerable to insider attacks (98%)
82% of organizations find it difficult to determine the actual damage of an insider attack
49% of organizations can’t detect insider threats or can only detect them after the data has left the organization
Only 11% of organizations consider their monitoring, detecting, and response to insider threats extremely effective

The Insider Threat by the Numbers-(2021 Insider Threat Survey Report)

Real-time Threat Detection with Behaviour Analytics

Lakshminarayanan Kaliyaperumal, VP & Head – Cyber Security Technology & Operations at Infosys Ltd

Automate Security Controls Using Machine Learning

Drive Front Line Security Controls with UEBA & Identity Analytics

Kurt Lieber, CISO, Aetna

Gurucul Predicts, Detects, and Stops Insider Threats in Real-Time

The most effective way to detect insider threats, without generating high volumes of false positive alerts, is to create time-based behavioural baselines and continuously learn what is acceptable behaviour in order to detect anomalies. This requires monitoring and analyzing massive amounts of data from a myriad of sources using advanced machine learning models and data science to pinpoint privilege abuse. This is what Gurucul does, in real time, with exceptional accuracy.

Predict

Suspicious Behaviour

HR violations, events, behaviour traits
Off-hour access to sensitive data or systems
Excessive access to job or competitor sites
Disgruntled behaviour / Sentiment Analysis
Privileged access escalation
Access from unusual or unseen devices

Detect

Information Collection

Excessive data downloads
Unusual data traversing
Unauthorized access to sensitive data or systems
Unusual access to systems and data by authorized users
Suspicious physical locations access / wonderer / social engineering

Prevent

Data Exfiltration

Unusual emails sent to self or personal domains
Emails to unusual external domains (media, competitors, malicious / suspicious domains)
Uploads to personal cloud storage sites
Suspicious print activity
Data transfers to unmanaged removable media