AI Features in Case Management
This guide covers the AI-powered features available in the case management system, designed to enhance investigation efficiency and decision-making.
Overview
AI features provide automated analysis, insights, and recommendations to help analysts work more effectively:
![AI Features Overview] Screenshot showing the AI features dashboard
AI Insights Tab
Automated Analysis
The AI Insights tab provides:
-
Case Summary:
- Key findings
- Risk assessment
- Recommended actions
- Similar cases
-
Pattern Detection:
- Behavioral patterns
- Attack techniques
- Anomaly detection
- Trend analysis
![AI Insights Interface] Screenshot of the AI Insights tab showing analysis results
Key Features
1. Similar Case Detection
Automatically identifies related cases:
- Pattern matching
- Behavioral similarity
- Shared indicators
- Historical correlation
2. Threat Analysis
AI-powered threat assessment:
- Risk scoring
- Impact analysis
- Threat actor attribution
- Attack pattern matching
3. Recommendation Engine
Provides actionable recommendations:
- Next steps
- Investigation paths
- Mitigation strategies
- Resource allocation
4. Natural Language Processing
Advanced text analysis:
- Content summarization
- Entity extraction
- Relationship mapping
- Sentiment analysis
Using AI Features
Accessing AI Insights
- Open a case
- Navigate to AI Insights tab
- View automated analysis
- Explore recommendations
Interpreting Results
Understanding AI outputs:
- Confidence scores
- Supporting evidence
- Related findings
- Action priorities
![AI Results Interpretation] Screenshot showing how to interpret AI analysis results
Configuration Options
AI Feature Settings
Configure AI behavior:
- Analysis frequency
- Confidence thresholds
- Data sources
- Integration points
Model Selection
Choose AI models for:
- Pattern recognition
- Text analysis
- Risk assessment
- Recommendation generation
![AI Configuration] Screenshot of AI feature configuration options
Integration Features
External AI Services
Integration with:
- OpenAI services
- Custom ML models
- Third-party AI tools
- Threat intelligence platforms
Data Sources
AI analysis uses:
- Case history
- Alert data
- Threat intelligence
- External feeds
Best Practices
1. Data Quality
Ensure quality inputs:
- Complete case documentation
- Accurate metadata
- Relevant observables
- Clear descriptions
2. AI Assistance
Effective use of AI:
- Verify AI findings
- Combine with human analysis
- Document AI insights
- Provide feedback
3. Continuous Learning
Improve AI performance:
- Regular model updates
- Feedback integration
- Performance monitoring
- Training data updates
Privacy and Security
Data Protection
AI feature security:
- Data encryption
- Access controls
- Audit logging
- Privacy compliance
Ethical Considerations
Responsible AI use:
- Bias prevention
- Decision transparency
- Human oversight
- Ethical guidelines
![Privacy Settings] Screenshot showing AI privacy and security settings
Performance Metrics
AI Effectiveness
Track AI performance:
- Accuracy rates
- Time savings
- False positive rates
- User adoption
Impact Analysis
Measure business impact:
- Resolution time
- Decision quality
- Resource efficiency
- Cost savings
Troubleshooting
Common Issues
Address AI-related problems:
-
Analysis Delays:
- Check data sources
- Verify API access
- Monitor system resources
-
Accuracy Issues:
- Review training data
- Adjust thresholds
- Update models
- Gather feedback
![Troubleshooting Guide] Screenshot showing AI troubleshooting interface
Future Developments
Upcoming AI features:
- Advanced analytics
- Predictive modeling
- Automated reporting
- Enhanced visualization
For more information about working with cases, see Working with Cases.