AI Driven Insider Threat Monitoring Workflow for Enhanced Security

AI-Driven Insider Threat Monitoring

1. Identification of Insider Threat Indicators

1.1 Data Collection

Gather data from various sources, including:

• User activity logs
• Network traffic analysis
• Access control records

1.2 AI Tool Implementation

Utilize AI-driven tools such as:

• Darktrace – for anomaly detection in user behavior
• Splunk – for real-time data analysis and visualization

2. Risk Assessment

2.1 Behavioral Analysis

Employ machine learning algorithms to analyze user behavior patterns and identify deviations.

2.2 Threat Scoring

Assign risk scores to users based on behavioral anomalies using tools like:

• IBM QRadar – for security intelligence and analytics
• Exabeam – for user and entity behavior analytics (UEBA)

3. Monitoring and Alerting

3.1 Continuous Monitoring

Implement continuous monitoring solutions that leverage AI, such as:

• Microsoft Sentinel – for cloud-native SIEM
• LogRhythm – for security analytics and compliance

3.2 Automated Alert Generation

Set up automated alerts for high-risk activities detected by AI algorithms.

4. Investigation and Response

4.1 Incident Investigation

Utilize AI tools to assist in the investigation process by:

• Correlating events over time
• Providing contextual insights

4.2 Response Protocols

Develop response protocols based on findings, including:

• Immediate user access revocation
• Engagement of cybersecurity teams

5. Reporting and Review

5.1 Reporting Mechanisms

Create comprehensive reports detailing incidents, investigations, and responses using tools like:

• ServiceNow – for incident management and reporting
• Tableau – for data visualization and reporting

5.2 Continuous Improvement

Regularly review and update the monitoring process based on insights gained and evolving threats.