AI Features in Case Management

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Overview

AI features provide automated analysis, insights, and recommendations to help analysts work more effectively: ![AI Features Overview] Screenshot showing the AI features dashboard

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AI Insights Tab

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Automated Analysis

The AI Insights tab provides:

1. Case Summary:
   • Key findings
   • Risk assessment
   • Recommended actions
   • Similar cases
2. Pattern Detection:
   • Behavioral patterns
   • Attack techniques
   • Anomaly detection
   • Trend analysis

![AI Insights Interface] Screenshot of the AI Insights tab showing analysis results

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Key Features

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1. Similar Case Detection

Automatically identifies related cases:

• Pattern matching
• Behavioral similarity
• Shared indicators
• Historical correlation

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2. Threat Analysis

AI-powered threat assessment:

• Risk scoring
• Impact analysis
• Threat actor attribution
• Attack pattern matching

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3. Recommendation Engine

Provides actionable recommendations:

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

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4. Natural Language Processing

Advanced text analysis:

• Content summarization
• Entity extraction
• Relationship mapping
• Sentiment analysis

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Using AI Features

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Accessing AI Insights

1. Open a case
2. Navigate to AI Insights tab
3. View automated analysis
4. Explore recommendations

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Interpreting Results

Understanding AI outputs:

• Confidence scores
• Supporting evidence
• Related findings
• Action priorities

![AI Results Interpretation] Screenshot showing how to interpret AI analysis results

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Configuration Options

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AI Feature Settings

Configure AI behavior:

• Analysis frequency
• Confidence thresholds
• Data sources
• Integration points

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Model Selection

Choose AI models for:

• Pattern recognition
• Text analysis
• Risk assessment
• Recommendation generation

![AI Configuration] Screenshot of AI feature configuration options

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Integration Features

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External AI Services

Integration with:

• OpenAI services
• Custom ML models
• Third-party AI tools
• Threat intelligence platforms

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Data Sources

AI analysis uses:

• Case history
• Alert data
• Threat intelligence
• External feeds

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Best Practices

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1. Data Quality

Ensure quality inputs:

• Complete case documentation
• Accurate metadata
• Relevant observables
• Clear descriptions

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2. AI Assistance

Effective use of AI:

• Verify AI findings
• Combine with human analysis
• Document AI insights
• Provide feedback

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3. Continuous Learning

Improve AI performance:

• Regular model updates
• Feedback integration
• Performance monitoring
• Training data updates

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Privacy and Security

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Data Protection

AI feature security:

• Data encryption
• Access controls
• Audit logging
• Privacy compliance

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Ethical Considerations

Responsible AI use:

• Bias prevention
• Decision transparency
• Human oversight
• Ethical guidelines

![Privacy Settings] Screenshot showing AI privacy and security settings

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Performance Metrics

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AI Effectiveness

Track AI performance:

• Accuracy rates
• Time savings
• False positive rates
• User adoption

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Impact Analysis

Measure business impact:

• Resolution time
• Decision quality
• Resource efficiency
• Cost savings

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Troubleshooting

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Common Issues

Address AI-related problems:

1. Analysis Delays:
   • Check data sources
   • Verify API access
   • Monitor system resources
2. Accuracy Issues:
   • Review training data
   • Adjust thresholds
   • Update models
   • Gather feedback

![Troubleshooting Guide] Screenshot showing AI troubleshooting interface

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Future Developments

Upcoming AI features:

• Advanced analytics
• Predictive modeling
• Automated reporting
• Enhanced visualization

For more information about working with cases, see Working with Cases.