Lavernna is a cybersecurity threat intelligence platform designed for security analysts, threat researchers, and SOC teams in enterprise environments. By automating the collection, enrichment, and analysis of threat data from diverse open sources, it enables faster detection, validation, and investigation of cyber threats. Lavernna integrates AI models, graph-based analysis, and real-time data feeds to streamline threat intelligence operations and reduce the noise from irrelevant or outdated information.

Its core value lies in helping security teams prioritize actionable intelligence, minimize manual research time, and detect early signs of threats through automated contextual analysis. The platform is particularly useful for organizations that rely heavily on open-source intelligence (OSINT) to monitor cyber threat activity across the surface, deep, and dark web.

What are the key features of Lavernna?

Automated threat intelligence collection and enrichment

Lavernna continuously gathers threat data from open sources and enriches it with contextual information for relevance and prioritization.

• Aggregates indicators of compromise (IOCs) from forums, paste sites, social media, and threat feeds.

• Uses machine learning to filter, categorize, and score threat data.

• Cross-references IOCs with known malware campaigns, TTPs (tactics, techniques, and procedures), and threat actor profiles.

• Identifies relationships between data points using graph-based correlation.

This automation accelerates the threat identification process and reduces reliance on manual OSINT collection.

Graph-based analysis for relationship mapping

The platform presents enriched data in a visual graph model that allows analysts to trace threat actor infrastructure and campaign linkages.

• Interactive graphs show connections between domains, IPs, malware samples, and user aliases.

• Helps uncover coordinated activity, reused infrastructure, or shared tools across threat actors.

• Supports pivoting on key entities to explore deeper relationships.

• Enhances investigation depth while maintaining contextual clarity.

This graph-first approach helps security teams quickly see patterns that text-based feeds might miss.

Natural language processing (NLP) for unstructured data extraction

Lavernna applies NLP techniques to extract and structure indicators from unstructured sources like blogs, dark web posts, or news articles.

• Recognizes entities such as file hashes, URLs, threat group names, and vulnerabilities in raw text.

• Tags and indexes intelligence items for fast search and retrieval.

• Supports multilingual source processing, including content from non-English forums and leaks.

By turning noisy, human-written content into structured threat data, it expands the scope of usable OSINT.

Customizable detection and alerting rules

Security teams can define custom detection logic based on their threat models and intelligence needs.

• Create alerts when specific IOCs appear in multiple sources or show a trend over time.

• Set thresholds for activity volume, threat score, or source credibility.

• Enable proactive monitoring of known threat actors or malware families.

• Integrates with SIEMs and TIPs for workflow continuity.

This allows organizations to adapt Lavernna to their internal risk models and operational priorities.

Investigation support and data export options

Lavernna includes tools to assist analysts in creating intelligence reports and collaborating across teams.

• Export enriched threat data in STIX, CSV, or PDF formats.

• Generate timeline views and campaign summaries for reporting.

• Collaborate with other analysts through shared investigations and annotations.

• Supports integration with existing threat intelligence platforms.

These features ensure Lavernna fits into the larger threat analysis and incident response ecosystem.

To summarize

Lavernna offers a focused and automation-driven approach to threat intelligence for cybersecurity teams working with OSINT. Its main advantages include:

• Significant time savings through automated data collection and enrichment.

• Improved detection accuracy via AI-powered prioritization and filtering.

• Rich context and visualization that enhance investigations and reduce noise.

• Flexibility to align with internal threat models and detection strategies.

• Compatibility with existing tools for seamless intelligence workflows.

Lavernna is especially valuable for organizations seeking to operationalize open-source threat intelligence and accelerate cyber threat detection and response.