Integration Architecture
Integration Patterns
| Pattern | Description | Latency | Complexity |
|---|---|---|---|
| Query translation | NL → SIEM query language | Low | Medium |
| Result summarization | SIEM results → NL summary | Medium | Low |
| Interactive analysis | Conversational investigation | Medium | High |
| Automated enrichment | LLM-powered alert context | Low | Medium |
| Anomaly explanation | LLM interprets anomalies | Medium | Medium |
Architecture Components
| Component | Function | Implementation |
|---|---|---|
| Query interface | Accept natural language | Chat UI, API |
| Query translator | NL → SPL/KQL/EQL | LLM with examples |
| SIEM connector | Execute queries | Platform SDK/API |
| Result processor | Parse, summarize results | LLM + formatting |
| Context manager | Maintain investigation state | Memory system |
Platform-Specific Integration
Splunk Integration
| Capability | Implementation | Considerations |
|---|---|---|
| SPL generation | Few-shot prompting with SPL examples | Validate syntax before execution |
| Search execution | Splunk SDK, REST API | Rate limits, job management |
| Result parsing | JSON processing | Handle large result sets |
| Dashboard integration | Custom Splunk app | UI/UX considerations |
Elastic/OpenSearch Integration
| Capability | Implementation | Considerations |
|---|---|---|
| EQL/KQL generation | Query DSL examples in prompt | Complex query validation |
| Search execution | Elasticsearch client | Scroll API for large results |
| Aggregation interpretation | LLM explains aggregations | Statistical accuracy |
| Kibana integration | Custom plugin or external app | Authentication flow |
Microsoft Sentinel Integration
| Capability | Implementation | Considerations |
|---|---|---|
| KQL generation | Azure OpenAI integration | Native Copilot features |
| Incident enrichment | Logic Apps + LLM | Workflow automation |
| Hunting queries | NL → KQL translation | Query optimization |
| Workbook integration | Custom workbooks | Visualization |
Query Translation
Translation Approach
| Step | Process | Quality Control |
|---|---|---|
| 1. Intent extraction | Understand analyst goal | Clarification prompts |
| 2. Entity identification | Extract search targets | Entity validation |
| 3. Query generation | Produce SIEM query | Syntax validation |
| 4. Query explanation | Explain generated query | Analyst review |
| 5. Execution | Run validated query | Error handling |
Few-Shot Examples
| Query Type | Natural Language | Generated Query Pattern |
|---|---|---|
| Time-based | ”Failed logins last 24 hours” | Time filter + event filter |
| Entity search | ”Activity from IP 10.0.0.1” | Source/dest IP filter |
| Aggregation | ”Top 10 users by login failures” | Stats/aggregation |
| Correlation | ”Processes spawned after phishing email” | Join/correlation |
Result Processing
Summarization Strategies
| Strategy | Use Case | Token Efficiency |
|---|---|---|
| Top-N results | Large result sets | High |
| Statistical summary | Aggregation results | Very High |
| Anomaly highlighting | Pattern detection | High |
| Timeline construction | Temporal analysis | Medium |
| Full detail | Small result sets | Low |
Result Presentation
| Format | Best For | Implementation |
|---|---|---|
| Natural language summary | Quick understanding | LLM summarization |
| Structured table | Detailed review | Formatted output |
| Timeline view | Temporal analysis | Chronological ordering |
| Graph/relationship | Entity connections | Visualization |
Security Considerations
| Concern | Mitigation |
|---|---|
| Query injection | Validate generated queries, parameterization |
| Data exposure | Respect SIEM RBAC in LLM responses |
| Credential handling | Secure credential storage, rotation |
| Audit logging | Log all LLM-generated queries |
| Rate limiting | Prevent SIEM overload |
Quality and Evaluation
| Metric | Description | Target |
|---|---|---|
| Query accuracy | Valid, executable queries | > 95% |
| Intent match | Query matches analyst intent | > 90% |
| Result relevance | Useful results returned | > 85% |
| Summarization quality | Accurate, complete summaries | Expert review |
Anti-Patterns to Avoid
- Unbounded queries — LLM-generated queries without limits can overload SIEM. Always add time bounds and result limits.
- Skipping validation — Execute generated queries without syntax checking. Validate before execution.
- Ignoring RBAC — LLM responses must respect analyst permissions. Filter results appropriately.
- Over-automation — Some queries need human review. Implement approval workflows for sensitive searches.