Heureka Shared Intelligence Part 2
Our last post discussed Heureka’s concept of shared intelligence focused on risk information flowing from the Heureka platform to outside vendors such as Relativity, ServiceNow, or iCONECT. This week we touch on the future by discussing intelligence flowing from outside systems like review or cyber-security platforms back into the Heureka platform. We refer to this as reverse intelligence.
Review, ad infinitum
A frustrating issue which has existed for ages revolves around the loss of intelligence on documents traveling through the legal review process. All of the time, money, and more importantly intelligence gained when reviewing documents is essentially thrown away once a matter is closed. So, documents previously tagged as Privileged, Confidential, etc. are potentially re-collected, re-processed and reviewed time and time again with little chance of the intelligence being gained from previous reviews. I guess we could call this process wasted intelligence!
The main reason for this? Traditional systems have near-zero access to custodian endpoints after an initial collection is completed. Once data is collected (or over-collected) to a static data set, any notion of further interacting with custodian information is usually forgotten. Heureka is quickly and efficiently rewriting the rules of the game by giving users the ability to access custodian endpoints at any stage of the process with complete control over the data workflow.
Heureka is constantly enhancing the ability for intelligence to flow from the traditional “Review” side of the EDRM model back toward the endpoint “Identification” side. Imagine in the not-so-distant future of having the ability to view how a single document was tagged throughout its lifespan, regardless of the system it was reviewed in. We are inching closer and closer toward this type of workflow enabling intelligence gained elsewhere to be retained and searched upon using the Heureka platform.
Reverse intelligence does not start and end with human-gained intelligence. Machine learning and AI can also participate when added into the workflow. For example, if machine learning is used on document sets within Relativity, it’s possible to use intelligence gained at the document-level on files that were not previously collected.
In the real world
Forms of reverse intelligence are already being used by Heureka clients giving them the ability to rapidly search their endpoints by file name or hash value based on findings from reviewed documents. Cyber-security experts have used gained intelligence to search for file-based indicators of compromise supplied by outside sources such as FBI Flash reports or Alien Vault’s Open Threat Exchange (OTX). These are just a few examples of reverse intelligence workflow already in place.
The future holds tremendous promise when it comes to shared platform intelligence and Heureka is concentrating efforts and building methods and workflows to ensure that data intelligence does not begin and end in one platform or version of software.