Automated Incident Triage with AI Integration for Efficiency

Automated Incident Triage and Prioritization

1. Incident Detection

1.1 Input Sources

Utilize AI-driven monitoring tools to gather data from various sources, including:

• Network Traffic Analysis
• Endpoint Detection and Response (EDR)
• Security Information and Event Management (SIEM)

1.2 Tools

Examples of tools include:

• Splunk (for SIEM)
• CrowdStrike (for EDR)
• Darktrace (for network traffic analysis)

2. Data Aggregation

2.1 Centralized Data Collection

Aggregate logs and alerts from multiple sources into a centralized platform for analysis.

2.2 Tools

AI tools such as:

• IBM QRadar
• Elastic Security

3. Automated Triage

3.1 AI-Driven Analysis

Implement machine learning algorithms to analyze incoming data and prioritize incidents based on severity and impact.

3.2 Risk Scoring

Utilize AI models to assign risk scores to incidents, considering factors such as:

• Threat Intelligence Feeds
• Historical Incident Data
• Vulnerability Assessments

3.3 Tools

Examples of AI-driven products include:

• ServiceNow Security Operations
• Palo Alto Networks Cortex XSOAR

4. Incident Prioritization

4.1 Classification

Classify incidents into categories based on predefined criteria, such as:

• Data Breach
• Malware Infection
• Phishing Attempt

4.2 Automation of Incident Response

Use AI to automate the response process for low-priority incidents, allowing human analysts to focus on high-priority threats.

4.3 Tools

Consider using:

• IBM Resilient
• Fortinet FortiSOAR

5. Reporting and Feedback Loop

5.1 Incident Reporting

Generate automated reports on incident handling and response outcomes for review and compliance.

5.2 Continuous Improvement

Utilize feedback from incident resolution to improve AI models and enhance future incident detection and prioritization.