How to Create Reports with Siren’s LLM Capabilities

Summary

Siren Investigate offers powerful tools that transform how organizations analyze and report on complex data relationships. This video byte features Davide Paoletti, PhD, a Data Scientist at Siren, demonstrating how to quickly generate comprehensive reports from link analysis results using Siren’s Large Language Model (LLM) capabilities. The demonstration builds upon previous video bytes, showing the complete workflow from search to final report generation.

Problem

Investigators often struggle with translating complex network visualizations into clear, shareable reports for colleagues and stakeholders. Traditional methods require manually reviewing each record and connection, then writing detailed summaries – a process that can be extremely time-consuming. This bottleneck can delay critical information sharing and slow down collaborative investigative efforts across teams.

Solution Implementation

The Siren interface allows users to access previous search results and investigations stored in their collections. Davide demonstrates this by opening an existing investigation about a specific vehicle license plate directly into a graph visualization.

A simple search for a vehicle plate number connects to a previous investigation, which can be opened immediately as a network graph. Instead of manually examining each record and connection, users can generate comprehensive reports with a single click using Siren’s AI assistant.

“I could lose time and view each record and see what is related to, but I don’t have much time. So what I’m going to do, I’m simply going to ask for Siren to generate a report.”

Results

The LLM-powered report generation delivers remarkable efficiency improvements for investigators:

• Comprehensive Coverage: The AI-generated report automatically includes all individuals involved, event details, timelines, and content from both structured and unstructured data sources.
• Dark Web Integration: The system automatically incorporates findings from dark web and social media sources into the report, providing a more complete intelligence picture.
• Time Efficiency: What would typically require hours of manual analysis and documentation is reduced to seconds, allowing investigators to focus on higher-value tasks.
• Seamless Sharing: Finished reports can be immediately downloaded and shared with colleagues, streamlining collaboration across teams.

Conclusions

Siren’s LLM-powered reporting represents a significant advancement in transforming complex network analyses into actionable intelligence documents. By automating the labor-intensive process of report creation, Siren allows investigators to focus on analysis rather than documentation. The seamless workflow from search to visualization to automated reporting creates a complete investigative experience that dramatically improves efficiency and knowledge sharing across organizations.