AI Driven User and Entity Behavior Analytics Workflow Guide

User and Entity Behavior Analytics (UEBA) Workflow

1. Data Collection

1.1 Identify Data Sources

Collect data from various sources such as:

• Network logs
• User activity logs
• Authentication records
• Endpoint data

1.2 Implement Data Ingestion Tools

Utilize tools for data ingestion, such as:

• Apache Kafka
• Logstash
• Fluentd

2. Data Preprocessing

2.1 Data Cleaning

Ensure data quality by removing duplicates, correcting errors, and standardizing formats.

2.2 Data Normalization

Normalize data to facilitate analysis, ensuring consistency across datasets.

3. Behavior Analysis

3.1 Establish Baselines

Utilize AI algorithms to establish normal behavior patterns for users and entities.

3.2 Anomaly Detection

Implement machine learning models to detect deviations from established baselines.

• Example Tools:
  • Darktrace
  • Exabeam
  • IBM QRadar

4. Risk Assessment

4.1 Risk Scoring

Assign risk scores to detected anomalies based on severity and potential impact.

4.2 Prioritization

Prioritize incidents for further investigation based on risk scores.

5. Incident Response

5.1 Alert Generation

Automatically generate alerts for security teams when high-risk anomalies are detected.

5.2 Investigation and Remediation

Utilize AI-driven investigation tools to analyze incidents and recommend remediation steps.

• Example Tools:
  • CrowdStrike
  • Palo Alto Networks Cortex XDR

6. Continuous Improvement

6.1 Feedback Loop

Incorporate findings from incidents to refine AI models and improve detection capabilities.

6.2 Regular Updates

Ensure AI tools are regularly updated with new data and threat intelligence to maintain effectiveness.

7. Reporting and Compliance

7.1 Generate Reports

Automate the generation of compliance and incident reports for stakeholders.

7.2 Audit Trail

Maintain an audit trail of all actions taken during the UEBA process for accountability and compliance.