Threat hunters are constantly on the lookout for external threats. However, we must not forget the potential danger hiding within our own organizations: insider threats. According to studies, insiders are responsible for 22% of security incidents. On average, a data breach caused by insiders results in a cost of $11.5 million, which is 21% higher than the average cost of $9.48 million for data breaches (Ref: https://www.idwatchdog.com/insider-threats-and-data-breaches)
This guide walks through the complexities of insider threat hunting, spotlighting how those trusted individuals can inadvertently or deliberately become a danger, and laying out a roadmap for detection, response, and continuous safeguarding against these hidden dangers.
Who are insiders, and what risks do they pose?
Insiders are individuals who are allowed to handle the critical assets, sensitive data, systems of a business, or any other critical information. These individuals work as employees, contractors, experts, temporary workers, or even trusted business partners. With access to all critical and sensitive information, these insiders contribute to successful cyberattacks which affect their organization.
Organizations need to understand the impacts of insider threats in order to develop effective strategies. These can be anything from accidental data leaks to deliberate theft or sabotage. The effects on an organization can be devastating, including financial losses, reputational damage and legal consequences.
Another study by the Ponemon Institute in 2022 reveals that insider threat incidents have increased by 44% over the past two years.
Ref: https://www.proofpoint.com/us/resources/threat-reports/cost-of-insider-threats
What are the types of insiders?
Insiders can be classified into various types based on their relationship with the organization. These classifications help in understanding the different roles and perspectives of insiders. Some common types of insiders include:
1. Malicious insiders: These individuals intentionally misuse their access to commit fraud. They may steal sensitive information or cause harm to the organization.
2. Negligent insiders: These are individuals who unintentionally cause security breaches or data losses.
3. Collaborators: These are individuals who work with external attackers to gain unauthorized access to the organization's systems or data.
Spotting the red flags of an insider threat: When it comes to identifying warning signs, we can separate them into two categories:
Technological Red Flags of Insider Threats
On the other hand, technological red flags are anomalies or suspicious activities within an organization's IT infrastructure. These signs are often detected through various cybersecurity tools and systems. Here are some technological red flags to watch out for:
- Abnormal system access or network anomalies.
- Inconsistent data flow or transfer.
- Unauthorized use of removable media or access to sensitive data.
- Malware or other malicious software capable of compromising data or systems.
Remember, these are just indicators, not definitive proof of wrongdoing. But by keeping an eye out for these red flags, we can proactively identify potential insider threats and take action before any damage is done.
Tools and techniques to identify insider threats
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Security Information and Event Management (SIEM)
Robust Security Information and Event Management (SIEM) solutions like Splunk ingest and correlate security event data across hybrid infrastructure to detect anomalies in access patterns, data usage trends and system behaviors that may indicate insider threat activities using analytics dashboards, pattern recognition and behavioral profiling techniques.
User and Entity Behavior Analysis (UEBA)
User and Entity Behavior Analysis (UEBA) tools establish peer-group activity baselines, leveraging machine learning to model normal behavior and detect outliers indicative of users operating outside expected parameters across vectors like authentication, data access and privileged actions that warrant insider threat investigations.
Threat Intelligence Platforms
Threat intel feeds provide real-time Indicators of compromise (IOCs) mapped to suspicious internal security events as well as broader ecosystem awareness of compromised credentials, vulnerabilities and known bad actors that help identify lateral movement, data exfiltration and collusion tied to potential insider incidents.
Network Monitoring Tools
Solutions like Intrusion Detection System (IDS), Intrusion Prevention System (IPS), and net flow monitors provide visibility into internal lateral movement, malware execution, command & control signaling, and data extraction heuristics aligned to insider threat response. Integrating network forensics with user behavior analytics strengthens detection.
Endpoint Detection and Response (EDR) Solutions
Endpoint Detection and Response (EDR) solutions use real-time endpoint processes monitoring combined with AV heuristics and ML driven behavioral modeling to rapidly surfaced and help contain compromised or rogue insiders in the environment through isolating devices to prevent data loss.
The combined strengths of continuous security analytics and monitoring with the risk-awareness of threat intel builds a robust capability to identify insider threats through technical means.
How can Splunk be leveraged for insider threat hunting?
Splunk is a leading security information and event management (SIEM) platform designed to provide real-time analysis and correlation of security, infrastructure, and application data. It can be a powerful tool for proactively hunting for insider threats. Here is an overview of how its capabilities can be mapped to insider threat hunting processes:
Data Ingestion
Splunk provides extensive data ingestion from sources like directory services, human resources systems, VPN and endpoint activity logs allowing collection of user behavior audit trails required for insider threat analytics. Security teams can ingest rich relevant data pertaining to access patterns, policy violations, and privileged actions providing the foundation for hunting.
Alerting & Monitoring
Configuring Splunk's real-time correlation searches and machine learning driven anomaly detection models enables automated alerting when user activity appears suspicious based on thresholds, behavioral baselines or other statistical methods. This allows proactive monitoring beyond relying on human observation alone. Dashboards provide visibility into overall access patterns, incidents and hunting metrics.
User Behavior Analytics
Leveraging its analytics, Splunk allows security teams to baseline expected peer-group user behavior, analyzing trends over time to spot anomalies in credential usage, data access and policy violations. Models can be tuned to balance insider risk detection with false positives for the organization. Suspicious deviations trigger alerts for further investigation.
Data Correlation & Analysis
Splunk provides security analysts with lookup-based "pivot" investigation capabilities to easily correlate suspicious user audit trails across HR, authentication, cloud and endpoint data sets. Analysts can visualize timelines, analyze access correlations, and develop risk scoring models to uncover hard-to-detect threats.
Forensic Investigation
Finally, Splunk allows exporting filtered event results, dashboards and visualizations to preserve case evidence and conduct root cause analysis on confirmed threats. Its collaborative features help document suspicions, findings and recommended actions across security, IT and HR teams involved in insider threat response.
To learn more, take a look at our Splunk offerings
Challenges of spotting an Insider Threat
Insiders are well acquainted with the organization's network settings, security policies, and procedures. They possess knowledge about vulnerabilities, gaps, or other shortcomings that could be exploited. So, finding and stopping insider threats is no easy task. Here are some common challenges in spotting insider threats:
- Volume and Complexity of Data: First off, we're dealing with a staggering amount of data. But it's not just the volume that's overwhelming; it's the complexity too. We're talking about multiple sources of data, ranging from emails to log files.
- Lack of visibility: Limited historical insider threat examples to detail malicious user actions restricts baseline behavioral profiles and machine learning models needed to surface concerning activity outliers.
- Distinguishing Malicious from Benign Behavior: Not every unusual activity is a threat. It's a challenge to differentiate between a malicious insider and an employee who's just deviating from their usual behavior.
- False positives or negatives: The impact of false positives or negatives can be significant. A false positive could damage an innocent employee's reputation, while a false negative could let a real threat slip through unnoticed.
- Lack of Standardized Framework: Newer insider threat programs often lack access governance capabilities tracking privileged credential usage, authentication levels and cloud application permissions needed to fully analyze risk.
Mitigating insider threats poses complex challenges due to inherent trust and access advantages malicious actors hold, but an analytics-focused, collaborative methodology centered on prevention and timely response allows organizations to significantly diminish risk. Combining policy constraints, behavior monitoring and cross-group visibility counters the asymmetrical detection blind spots insiders can otherwise exploit.
Best practices for superior protection
In addition to the key components of insider threat detection, there are several best practices that organizations can follow to provide superior protection against insider threats. These include:
- Implement Zero Trust: Adopt zero trust approaches with ubiquitous Multi Factor Authentication (MFA) adoption, micro-segmentation, device trust scoring and encrypted traffic inspection to deny unfettered lateral movement.
- Restrict access & entitlements: Impose stringent least-privilege controls, revalidate employee access entitlements often and monitor privileged credential usage to mitigate insider threat risk.
- Prioritize User Behavior Analytics: Make robust user activity monitoring with peer analytics, risk-based alerting and visibility a primary safeguard against insider threats.
- Refine Machine Learning models: Continuously tune insider threat machine learning to balance improved detection rates and false positives through quality assurance testing and new training data sets.
- Formalize Incident Response: Develop specialized incident response playbooks for security operations when insider threat model thresholds are breached, defining containment, investigation and remediation protocols.
Conclusion
Insider threats pose a risk for organizations; therefore, it is crucial for them to have an insider threat detection program in place. By adopting a zero-trust model, limiting access to data, monitoring user behavior closely, and establishing communication channels, organizations can greatly enhance their protection against insider threats.
