Detecting Insider Threats: The Critical Role of Predictive Security Analytics

What makes an insider threat behavior 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 insider 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 Insider Threats – 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.

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 
• Excessive Data Access or Downloads 
• Unauthorized Access.
• Frequent Failed Access Attempts 
• Accessing Restricted Areas 
• Copying or Transferring Data

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.

Main Insider Threat Techniques

Insider threat techniques encompass a range of tactics that individuals within an organization might use to carry out malicious insider threat 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 
• Unauthorized Access and Privilege Escalation
• Data Modification or Destruction 
• Backdoors and Persistence Mechanisms
• Bypassing Security Controls 
• Stealing Trade Secrets

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

User and Entity Behavior Analytics (UEBA) is often advocated as the best means to detect nefarious activity by internal actors. Gurucul provides a comprehensive solution that sets a high industry standard. Our advanced UEBA platform uses machine learning and artificial intelligence to detect unusual behavior patterns that could signal insider threat behavior. 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.

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

Predictive analytics enhances insider threat behavior detection by using advanced data analysis to spot suspicious behaviors and patterns, helping to mitigate risks of insider threats and protect sensitive information.

How Gurucul Helps with Insider Threat Detection

Gurucul’s insider threat solutions leverage predictive analytics to spot suspicious activities and behaviors that may signal security breaches or malicious activities to prevent a data breach. Key capabilities include: Key capabilities include:

• Early Detection: Identifies unusual activities by analyzing historical and real-time data, allowing proactive threat prevention.
• Behavioral Analysis: Establishes baseline user behavior and flags deviations, comparing actions to peer groups.
• Data Aggregation: Combines data from multiple sources, like access logs and HR systems, to detect patterns indicating malicious intent.
• Real-time Monitoring: Continuously tracks activities across systems to enable instant threat response.
• Advanced Threat Detection: Uses AI and machine learning to identify subtle behaviors missed by traditional systems.
• Reduction of False Positives: Considers context to minimize false alarms, letting teams focus on genuine threats.
• Adaptive Learning: Continuously evolves with new data, adjusting to changing user behaviors.
• Risk Prioritization: Assigns risk scores based on user activity, helping teams prioritize high-risk investigations.

A critical piece of our overall converged security framework is effective mitigation of the insider threat.
Adam Lee, VP & Chief Security Officer, Dominion Energy

Conclusion

Insider threats pose a risk and unique challenge to organizations because the attackers are already within the system. Detecting these threats requires sophisticated tools like UEBA and predictive analytics to monitor insider activity, identify deviations from normal behavior, and detect potential insider threats early.

By building an insider threat security program with tools like Gurucul’s insider threat solution, organizations can protect themselves from malicious insider threats and prevent data breaches by proactively identifying risky behaviors and mitigating potential risks before they escalate.