Can AI Safeguard Open-Source Repositories from Insider Threats?

Open-source software (OSS) powers much of today's technology, but its open and decentralized structure poses a unique cybersecurity challenge—particularly when it comes to insider threats. With the right access, insiders or third-party contributors can introduce risks, whether intentional or not. At Moon Tiger, we're developing an AI-driven approach to monitor, detect, and mitigate these threats. Here’s a deeper look at our solution in progress.

Identifying Insider Threat Indicators

The first critical step in Moon Tiger’s approach is to define insider threat indicators specific to code repositories. These include:

• Unusual Code Access: Monitoring repository access patterns to detect anomalies, such as contributors interacting with unfamiliar files or directories.
• Data Exfiltration Patterns: Detecting large data transfers or cloning activity, especially outside standard working hours.
• Code Manipulation in Sensitive Files: Tracking alterations to high-risk files, such as those modifying access controls or handling sensitive data.
• Anomalous IP Addresses or Locations: Flagging repository access from unusual or unverified locations.
• Collaborative Anomalies: Detecting unconventional patterns in team collaborations, such as recent joiners making broad changes in sensitive repositories.

Data Collection and Preparation

Our model relies on extensive data from developer interactions and repository histories to identify patterns. Using APIs from platforms like GitHub and GitLab, we pull commit histories, access logs, and behavioral metadata to build a comprehensive data set. Key data sources include:

• Commit Histories: Capturing metadata such as author, time, affected files, and lines changed.
• Access Logs: Tracking cloning, forking, and pushing activities, with geolocation data to detect potential IP anomalies.
• User Metadata: Including user roles, permission levels, and typical access patterns for refined behavioral baselines.
• Historical Insider Threat Incidents: Labeling past incidents helps refine the model to recognize risky patterns in real-world scenarios.

Feature Engineering for Anomaly Detection

With raw data in place, the next stage is engineering features that help identify deviations from normal patterns:

• Behavioral Baselines: Calculating norms for each user, such as usual files accessed, typical commit frequency, and average data download volume.
• Time and Location Patterns: Analyzing access across times of day and geographic locations to identify abnormal behavior.
• Volume and Speed of Access: Detecting excessive data access or rapid interaction sequences that might suggest exfiltration.

Multi-Model Approach to Threat Detection

Moon Tiger’s approach involves a hybrid model setup, combining unsupervised and supervised machine learning:

• Unsupervised Learning (Anomaly Detection): Models like Isolation Forests and DBSCAN identify outliers without requiring labeled data. This setup is valuable for flagging uncommon but extreme behaviors.
• Supervised Learning (Insider Threat Prediction): Using labeled threat data, classification models (e.g., Random Forest, XGBoost) predict insider threats based on previously observed patterns.
• Deep Learning for Sequence Analysis: Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks detect changes in user behavior sequences, capturing complex behavioral shifts over time.

Real-Time Monitoring and Alerting

For real-time threat detection, we’re implementing a monitoring system that connects directly to repository activity:

• Stream Processing with Kafka or Kinesis: Handling live data streams for scalable, continuous monitoring.
• Real-Time Inference: Deploying models that run anomaly detection on each event, with thresholds to trigger alerts according to severity (e.g., “High” for possible exfiltration, “Low” for unusual repository access).

The system flags and logs anomalies for investigation, minimizing false positives through alert prioritization.

User Interface for Security Teams

The final layer is a user-friendly dashboard for cybersecurity teams:

• Visualizations of Anomaly Scores: Displaying patterns over time, access locations, and data volumes.
• Alert Details: Offering insight into user activity, access times, and behavioral summaries for each alert.
• Behavioral History: Providing access to a user’s behavioral history to contextualize alerts and facilitate decision-making.

Feedback Loops for Continuous Model Improvement

To maintain detection accuracy, we integrate regular feedback loops:

• Analyst Feedback: Security analysts can label cases for model retraining, improving supervised learning accuracy.
• Updating Baselines: Behavioral baselines are periodically updated to reflect evolving project and team norms, reducing false positives.