Bitsight GIA Update: How Gen-AI and LLMs Get You Faster (and Better) Entity Mapping

Bitsight’s mission to keep evolving the capability of our data engine through AI enhancements hit a new milestone today. The latest addition is a new entity mapping capability added to Bitsight AI and the data engine, which uses GenAI agents to create more complete and consistent sets of identifiers for organizations scanned and added to Bitsight’s entity inventory. 

The improvement will make it easier for all Bitsight customers to swiftly identify and differentiate between all of the organizations associated with assets found within their extended digital footprint. The AI will help ensure access to a reliable set of the organization’s profiles that includes key entity data such as name, logo, description, and industry.   

What is entity mapping in Bitsight GIA?

As a quick refresher, Bitsight’s data engine’s two main modules are Bitsight Groma and Bitsight Graph of Internet Assets (GIA). Bitsight Groma is a proprietary internet scanning engine that operates at a global scale. Meantime, Bitsight GIA is an asset and entity relationship mapping service that helps companies understand more about the internet-connected assets and entities in their digital ecosystem based on extensive source data, including Domain and IP WHOIS and DNS details, SSL/TLS certificates, website data, and other corporate identifiers.

Entity mapping, as part of Bitsight GIA technology, is the process of discovering internet assets, assigning them to organizations, and establishing relationships between related organizations, while unambiguously identifying these organizations. These relationships are displayed in the Bitsight tree for each organization.  

Prior to this update, entity mapping still required significant intervention from our human analysts, especially to ensure each entity had accurate descriptive data. Our recent work in training and testing the efficacy of GenAI to do this work found that we could automate this portion of entity mapping to produce faster, more thorough, and more consistent results. In fact, our analysis of the AI-generated descriptors indicates a 97% accuracy rate vs. human creation, while doing so in a matter of seconds. This is a win for our customers because they get access to new entities faster, and it enables us to task our team of researchers with more complex and high-value deep research work.

How AI-driven entity mapping feeds Bitsight Ratings Trees

The AI improvements made to our entity mapping will greatly boost the discoverability and searchability of entity inventory for customers. They simplify and accelerate the process of identifying an organization with this data stream and make the entity information more uniform for every entry because we’ve increased the fidelity of data conventions for all of the fields in entity entries. In addition, the improvements also help deliver newly requested entities into customers’ hands.

One of the most valuable benefits of the consistency and automation of entity mapping that’s now powered by Bitsight AI is that it helps provide a higher fidelity of data for Bitsight’s Rating Tree. Ratings Tree is a proprietary way of visualizing an organization’s hierarchical structure to better understand how the security performance of different entities in its subsidiary and third-party and fourth-party ecosystem contribute to the organization’s overall security rating.  

In its 2025 Leadership Compass on Attack Surface Management, KuppingerCole recently granted Bitsight with an Overall Leadership rating for a range of reasons, and one of the strengths called out in this report was the unique nature of the Bitsight Ratings Tree. The addition of AI-driven entity mapping will only serve to strengthen this capability. 

Bitsight’s commitment to AI enhancements

Incremental steps like these help improve the capabilities of the Bitsight platform powered by our data engine, with more to come. These milestones also offer regular reminders to our customers of the ongoing behind-the-scenes work put in by our data science and product engineers to innovate with new use cases for trustworthy AI.

To learn more about AI-driven entity mapping, check out our whitepaper, “​​A Data-Driven Approach to Asset Discovery and Risk Measurement.”