Overview

The AI Insights tab provides automated analysis and recommendations powered by artificial intelligence. This feature helps analysts quickly understand alert context, identify patterns, and make informed decisions about alert handling.

Analysis Categories

Threat Assessment

  • Risk scoring
  • Severity recommendations
  • Impact analysis
  • Confidence rating

Pattern Recognition

  • Similar past alerts
  • Known attack patterns
  • Anomaly detection
  • Behavioral analysis

Context Enhancement

  • Related external threats
  • Industry context
  • Historical perspective
  • Environmental factors

AI Capabilities

Natural Language Processing

  • Description analysis
  • Context extraction
  • Entity recognition
  • Relationship mapping

Machine Learning Models

  • Pattern detection
  • Anomaly identification
  • Risk prediction
  • Similarity scoring

Automated Enrichment

  • Threat intelligence correlation
  • OSINT integration
  • Historical data analysis
  • Environmental context

Insights Display

Summary View

  • Key findings
  • Risk assessment
  • Recommended actions
  • Critical observations

Detailed Analysis

  • In-depth explanations
  • Supporting evidence
  • Confidence levels
  • Alternative interpretations

Recommendations

  • Next steps
  • Investigation paths
  • Mitigation strategies
  • Resource allocation

Interactive Features

Insight Exploration

  1. Expand detailed analysis
  2. View supporting evidence
  3. Access related data
  4. Track insight history

Feedback Loop

  • Mark insights helpful/unhelpful
  • Add analyst notes
  • Provide context
  • Report inaccuracies

Custom Analysis

  • Request specific analysis
  • Focus on particular aspects
  • Adjust analysis parameters
  • Save analysis preferences

Best Practices

  1. Analysis Review

    • Validate AI findings
    • Cross-reference data
    • Document disagreements
    • Track accuracy
  2. Investigation Flow

    • Start with summary
    • Explore key findings
    • Validate conclusions
    • Document decisions
  3. Feedback Quality

    • Provide specific feedback
    • Note false positives
    • Suggest improvements
    • Share context

Model Training

Data Sources

  • Historical alerts
  • Analyst feedback
  • External threats
  • Industry data

Training Process

  • Continuous learning
  • Feedback incorporation
  • Model updates
  • Performance monitoring

Next Steps