What makes an insider threat so pernicious is that the threat actor is already sitting inside the network and wearing an employee badge—if not literally, then at least figuratively. This actor can be a true insider – employee, contractor, temporary worker, business partner or vendor – or an external intruder who is mimicking an employee through subversion of a legitimate set of access credentials.
Detecting such threats and mitigating the risk requires specialized technology that differs from the usual approach of detecting external threats. The insider is beyond detection by firewalls, intrusion detection/prevention systems, proxy servers, and other common technologies intended to stop threats at a perimeter or other checkpoint. Rather, organizations need a focused insider threat program to tackle the unique risks posed by insiders.
Insiders already have a certain level of access to perform specific activities on a network. What tends to distinguish them is their motivation for going rogue. Nevertheless, their activities follow certain patterns that make them detectable with the right technology.
Detecting insider threats can be challenging because insiders often have legitimate access to systems and data. However, certain behaviors and patterns can serve as indicators of potential insider threats.
Additional insider threat indicators are excessive use of privileges, unauthorized use of credentials, data exfiltration, logins from multiple locations, unapproved software installations, violations of policies, excessive data printing, and inconsistent work patterns.
Insider threat techniques encompass a range of tactics that individuals within an organization might use to carry out malicious activities or compromise security. These techniques can vary based on the threat actor’s intent, skills, access, and motivations. Here are some main insider threat techniques.
Additional techniques include social engineering, abuse of privileges, credential sharing or theft, insider collaboration, disguising malicious activities, data staging, misuse of administrative tools, data concealment, and privilege misuse.
Given that an insider usually has access privileges that are a normal part of their work processes, the key to detecting an insider threat is to monitor for risky and anomalous behaviors, determining their severity, and predicting whether they could cause damage or whether malicious activity is about to occur or is currently taking place.
User and Entity Behavior Analytics (UEBA) is often advocated as the best means to detect nefarious activity by internal actors. UEBA involves keeping track of what users are doing and looking for behaviors that are outside the range of normal activities. This, then, is combined with in-depth intelligence about a user’s identity attributes and the privileges he has on the network. This approach involves analyzing the access rights and entitlements a person has; the activities he has been performing across multiple accounts, both now and in the past; and the typical activities that members of his peer groups are doing. It takes a combination of the right data sources, sophisticated machine learning, and perceptive data science to pinpoint truly aberrant actions that are good indicators of misuse of assigned privileges.
Predictive analytics adds an extra layer of sophistication to insider threat detection by leveraging advanced data analysis techniques to identify suspicious behaviors and patterns. This proactive approach is essential for mitigating the risks associated with insider threats and protecting an organization’s sensitive information and assets.
The Gurucul Insider Threat Solution utilizes predictive analytics to enable organizations to identify suspicious patterns and behaviors that might indicate potential security breaches or malicious activities carried out by individuals within the organization. The platform provides crucial capabilities, including:
A critical piece of our overall converged security framework is effective mitigation of the insider threat.
Adam Lee, VP & Chief Security Officer, Dominion Energy
The most effective way to pinpoint the presence of insider threats, without creating a lot of false positive alerts, is to overlay user activities with user identity intelligence, cluster identities into dynamic peer groups, create time-based behavioral baselines, and continuously learn what is acceptable behavior in order to spot the unacceptable behavior. It takes a combination of the right data sources, sophisticated machine learning, and predictive analytics to pinpoint truly aberrant actions that are good indicators of misuse of assigned privileges.
What are insider threats?
Insider threats refer to security risks that originate from individuals within an organization, such as employees, contractors, partners, or other trusted entities who have authorized access to the organization’s systems, data, and resources. These individuals exploit their insider status to cause harm to the organization by stealing sensitive information, committing fraud, disrupting operations, or otherwise compromising security. Insider threats can be intentional or unintentional and can have severe consequences for an organization’s data integrity, reputation, and overall security posture.
How does predictive security analytics help detect insider threats?
Predictive security analytics plays a crucial role in detecting insider threats by leveraging advanced data analysis techniques to identify patterns, anomalies, and behaviors that might indicate potential security risks originating from within the organization. The techniques include behavioral analysis, anomaly detection, contextual analysis, correlation of data, machine learning and artificial intelligence, real-time monitoring, risk scoring, and adaptive learning.
What data is used in predictive security analytics?
Predictive security analytics relies on a wide range of data from various sources within an organization to effectively detect and respond to security threats, including insider threats. The data used in predictive security analytics includes user activity data, access logs, authentication data, identity and access management data, network traffic data, endpoint data, behavioral analytics data, geolocation data, contextual data, external threat intelligence data, and more.
How do I implement predictive security analytics in my organization?
Gurucul’s purpose-built cloud-native Security Analytics and Operations Platform provides a consolidated set of capabilities to automate tasks such as data collection and correlation as well as threat detection, investigation, and response (TDIR). The platform is optimized to ingest as much data as possible, applying a wide area of analytics and using true ML/AI to adapt to and learn newer threats, including insider threats.
About The Author
Vikram Mathu, VP Customer Success, Gurucul
Vikram Mathu is a technology leader with 20+ years of experience in Cyber security, Customer Success, Product delivery and management, Infrastructure management, Identity & Access Management. He is a strategic thinker and planner, skilled in the design, implementation and management of highly effective product development, security architectures. Vikram possesses outstanding leadership and team building strengths that generate optimum productivity and performance excellence from organizational staff. He is committed to achieving corporate objectives with a history of successful delivery of projects and services. Specialties: Customer Success, Cyber Security, Identity & Access Management, Infrastructure Management.