Detecting Insider Threats: The Critical Role of Predictive Security Analytics

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.

Types of Insider Threats

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.

  • Malicious Insiders – These are individuals within the organization who intentionally misuse their access for personal gain, to harm the company, or to steal sensitive information..
  • Careless Users – These individuals unintentionally cause a security breach due to ignorance or carelessness.
  • Negligent Users – These employees may not act out of maliciousness, but they circumvent the normal security protocols to make their job easier; for example, turning off a personal firewall.
  • Compromised Credentials – Legitimate credentials or access to systems are stolen or otherwise compromised by an external actor.
  • Privilege Abuse – Employees with elevated access privileges might abuse their permissions to access sensitive information that they don’t have a legitimate need for.
  • Insider Collaboration – An employee might collaborate with an external threat actor to steal data or facilitate an attack from within the organization.

Insider Threat Personas Stopping Attacks Before They Happen

Common Indicators of an Insider Threat

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.

  • Unusual Access Patterns – Insiders may exhibit unusual patterns of accessing systems, networks, or data, such as accessing sensitive information at odd hours or accessing systems outside their normal job responsibilities.
  • Excessive Data Access or Downloads – An insider planning to steal sensitive data might access or download a larger amount of data than their typical usage, especially if the data is unrelated to their role.
  • Unauthorized Access – Insiders might attempt to access systems, databases, or files that are outside their authorized scope.
  • Frequent Failed Access Attempts – Repeated failed attempts to access certain systems or data could indicate an insider trying to bypass security measures or escalate their privileges.
  • Accessing Restricted Areas – It could be a sign of an insider threat if an employee suddenly starts accessing areas or systems they have no legitimate reason to access.
  • Copying or Transferring Data – Insiders might copy sensitive data to external storage devices, cloud services, or personal email accounts in preparation for unauthorized use or distribution.

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.

What is an Insider Threat

Main Insider Threat Techniques

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.

  • Data Theft and Exfiltration – Insiders can steal sensitive data, intellectual property, customer information, or proprietary designs. This data is often transferred to external storage devices, cloud services, or personal email accounts.
  • Unauthorized Access and Privilege Escalation – Insiders might exploit their legitimate access to escalate their privileges, gaining access to systems, data, or areas outside their authorized scope.
  • Data Modification or Destruction – Insiders might intentionally modify or delete critical data, databases, or files, causing disruption to business operations or data integrity.
  • Backdoors and Persistence Mechanisms – Insiders can create backdoors or install persistent malware on systems, allowing them continued access even after their official access is revoked.
  • Bypassing Security Controls – Insiders might attempt to disable or bypass security controls, such as firewalls or intrusion detection systems, to avoid detection.

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.

How to Detect an Insider Threat

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.

What is UEBA and How Does It Work?


Why Predictive Analytics via Machine Learning is Necessary in Detecting Insider Threats

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.

Uncover Insider Threats Through Predictive Security Analytics

How Gurucul Helps with Insider Threat Detection

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:

  • Early Detection – Gurucul helps identify unusual or anomalous activities that might not be immediately obvious through traditional security measures. By analyzing historical data and current behaviors, organizations can detect deviations from normal patterns and take proactive measures to prevent potential threats.
  • Behavioral Analysis – Gurucul creates baseline profiles of normal user (and entity) behaviors, including comparison to peer groups. When someone’s actions deviate significantly from this baseline, it can raise red flags.
  • Data Aggregation – Insider threats can be challenging to detect because they often involve aggregating data from multiple sources to identify a pattern. Gurucul aggregates and analyzes data from various systems, such as access logs, HR systems, directory services, and more, to identify correlations and anomalies that might indicate malicious intent.
  • Real-time Monitoring – Gurucul works in real-time, continuously monitoring activities across various systems. This allows organizations to detect and respond to insider threats as they happen, minimizing potential damage.
  • Advanced Threat Detection – Gurucul’s machine learning and AI-driven predictive analytics can detect subtle patterns that might go unnoticed by traditional rule-based security systems. These technologies can identify complex behaviors and relationships that could indicate a potential threat.
  • Reduction of False Positives – Gurucul reduces false positive alerts by considering contextual information and understanding the evolving nature of user behaviors. This allows security teams to focus on genuine threats rather than spending time investigating benign activities.
  • Adaptive Learning – Gurucul’s platform learns from new data and adapts to changes in user behavior over time. This is essential as insider threat behaviors can evolve and become more sophisticated.
  • Risk Prioritization – Gurucul assigns risk scores to different users based on their activities. This helps security teams prioritize investigations and responses, focusing on higher-risk individuals.

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.


About The Author

Vikram MathuVikram 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.


Frequently Asked Questions

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.