AI Driven Security Alert Triage and Escalation Workflow Guide

AI-Driven Security Alert Triage and Escalation

1. Alert Generation

AI systems continuously monitor network activities and generate alerts based on predefined security policies.

• Tools:
  • Darktrace
  • CrowdStrike Falcon

2. Initial Alert Triage

Upon receiving a security alert, AI algorithms assess the severity and relevance of the alert using historical data and threat intelligence.

• Tools:
  • IBM QRadar
  • Splunk Phantom

2.1. Classification of Alerts

Alerts are classified into categories such as false positives, low-risk, medium-risk, and high-risk.

2.2. Contextual Analysis

The AI system correlates alerts with existing incidents and vulnerabilities to provide context.

3. Escalation Procedures

Based on the triage results, alerts are escalated to the appropriate response teams.

• Low-risk Alerts: Logged for future reference.
• Medium-risk Alerts: Assigned to junior analysts for further investigation.
• High-risk Alerts: Escalated to senior security analysts or incident response teams.

3.1. Automated Response Actions

For certain alerts, automated response actions can be initiated to mitigate threats.

• Tools:
  • ServiceNow Security Operations
  • Palo Alto Networks Cortex XSOAR

4. Investigation and Remediation

Security analysts conduct a thorough investigation of escalated alerts, utilizing AI-driven tools for enhanced analysis.

• Tools:
  • Microsoft Sentinel
  • Elastic Security

4.1. Threat Hunting

Analysts employ AI tools to proactively search for potential threats within the network.

4.2. Incident Remediation

Following the analysis, appropriate remediation actions are executed, such as isolation of affected systems or patching vulnerabilities.

5. Reporting and Feedback Loop

Post-incident reports are generated to document findings and enhance future alert triage processes.

• Tools:
  • Tableau
  • Power BI

5.1. Continuous Improvement

Feedback is used to refine AI algorithms and improve the accuracy of future alerts.