AI Integration in Incident Investigation and Triage Workflow

AI-Assisted Incident Investigation and Triage

1. Incident Detection

1.1. Monitoring Systems

Utilize AI-driven monitoring tools to continuously analyze network traffic and system logs for anomalies.

• Example Tools: Darktrace, Vectra AI

1.2. Alert Generation

Implement machine learning algorithms to automatically generate alerts based on predefined thresholds and behavioral patterns.

• Example Tools: Splunk, IBM QRadar

2. Incident Triage

2.1. Initial Assessment

Use AI to prioritize incidents based on severity, potential impact, and exploitability.

• Example Tools: ServiceNow, PagerDuty

2.2. Contextual Analysis

Leverage AI to gather contextual information from various sources, including threat intelligence feeds and historical incident data.

• Example Tools: Recorded Future, ThreatConnect

3. Investigation

3.1. Data Collection

Employ AI tools to automate the collection of relevant data from affected systems for deeper analysis.

• Example Tools: CrowdStrike, Carbon Black

3.2. Root Cause Analysis

Utilize AI algorithms to identify patterns and correlations in the data that may indicate the root cause of the incident.

• Example Tools: Exabeam, Sumo Logic

4. Response and Remediation

4.1. Automated Response

Implement AI-driven automated response mechanisms to contain incidents quickly and effectively.

• Example Tools: Palo Alto Networks Cortex XSOAR, Demisto

4.2. Manual Intervention

Facilitate human intervention where necessary, supported by AI-generated recommendations for remediation actions.

• Example Tools: Microsoft Sentinel, Cisco SecureX

5. Post-Incident Review

5.1. Reporting

Utilize AI tools to generate comprehensive incident reports that include insights and lessons learned.

• Example Tools: LogRhythm, RSA NetWitness

5.2. Continuous Improvement

Analyze incident data using AI to identify trends and improve future incident response strategies.

• Example Tools: Cybereason, SentinelOne