From the trigger point for audits, investigations, and litigation to the conclusion of cases and matters with the defensible disposition of data, there are countless ways data discovery and legal discovery professionals approach and administer the discipline of eDiscovery. Based on an aggregation of research from leading eDiscovery educators, developers, and providers, the following eDiscovery Processes and Tasks listing may be helpful as a planning tool for guiding business and technology discussions and decisions related to the conduct of cybersecurity, information governance, and legal discovery-related eDiscovery projects. The processes and tasks highlighted in this listing are not all-inclusive and represent only one of the myriads of approaches to eDiscovery.
The workflow of the electronic discovery process includes initiation, legal hold, collection, ingestion, processing, analytics, predictive coding (technology-assisted review), review, production/export, and data disposition.
Initiation #
The initiation stage involves preliminary planning, project organization, project scoping, and the creation of a statement of work (SOW) and project estimates. The client reviews the SOW and estimates, and upon acceptance, the SOW is executed.
Legal Hold #
The legal hold stage involves scoping the legal hold, identifying and preserving electronic data in accordance with the legal hold plan, issuing legal hold notifications, and documenting the legal hold process.
Collection #
The collection stage involves scoping the collection, identifying the collection assets, developing a collection plan, conducting onsite collection, documenting and certifying the collection, and shipping/transferring the collection.
Ingestion #
The ingestion stage involves processing the received electronic data, conducting an ESI reception audit, creating a chain of custody, reviewing the collection log and notes, and preparing the electronic data for processing.
Processing #
The processing stage involves processing the electronic data according to specification, reporting the processing status to the client, identifying any exception or hold files (EHF), and completing the processing of the electronic data.
Analytics #
The analytics stage involves analyzing the electronic data, preparing an analytics repository, conducting an exploratory analysis, conducting a preliminary filtering hit report, and filtering the electronic data according to specification.
Predictive Coding #
The predictive coding (technology-assisted review) stage involves planning the predictive coding specification, moving the electronic data into a predictive coding application, conducting the predictive coding, and reporting the results of the predictive coding.
Review #
The review stage involves planning and preparing the review technology and staffing, conducting the review, and reporting the results of the review.
Production/Export #
The production/export stage involves planning and executing the production/export of the electronic data, conducting quality control (QC), and delivering the electronic data according to specification.
Data Disposition #
The data disposition stage involves planning the data disposition, evaluating the active discovery, making a decision to conclude the project, and disposing of the data in accordance with the client’s guidance.
The electronic discovery process involves a structured and systematic approach to managing the vast amounts of electronic data involved in modern litigation, ensuring that all relevant information is identified and reviewed in an efficient and effective manner.
The Workstream of eDiscovery: Process and Task Listing #
eDiscovery Processes and Tasks Checklist
eDiscovery Processes and Tasks Through The Lens of Generative Artificial Intelligence
This update also includes subjective assessments of Generative AI (GAI) productivity enhancements, aiming to provide a model for considering the holistic impact of GAI on eDiscovery tasks and processes over the next 12 months. These evaluations offer potential insights into productivity gains that can be achieved through the strategic application of GAI technologies.
GAI Enablement Model Background Notes:
- Categories: All 102 tasks are categorized into collection, processing, or review workstreams.
- Task Numbers: Each task in the workflow is assigned a specific tracking number.
- Process: Eleven processes are identified as subcategory organization areas for all tasks.
- Tasks: Detailed descriptions of specific workflow tasks for each process.
- GAI Enablement Rating: A subjective rating assessing the potential near-term (<12 months) productivity impact of GAI on specific tasks, ranging from 0 (No AI Enhancement) to 3 (AI LLM and Prompt Engineer Overview Enhancement).
- GAI Productivity Multiplier: Estimates of the short-term (<12 months) productivity impact of GAI on specific tasks, measured as Level 0 (0% increase in productivity), Level 1 (15% increase in productivity), and Level 2 (30% increase in productivity).
- Industry % of eDiscovery Spend: Estimated industry spend on specified categories. Provided as context to potential GAI impact.
- Industry eDiscovery Spend in 2024: Estimated industry spend on specified categories. Provided as context to potential GAI impact.
By integrating these GAI productivity assessments, this revised listing underscores the transformative potential of AI technologies within the eDiscovery field. It serves as a foundational guide for legal and IT professionals to explore and evaluate how AI can be effectively integrated to enhance operational efficiencies and meet strategic goals in a rapidly evolving digital landscape.
eDiscovery Processes and Tasks Checklist – AI Enablement Model
It’s important to emphasize that while this framework serves only as a model, it might help structure thought about the economics and efficiency of GAI-enabled eDiscovery.
News Sources
- EDRM – The Electronic Discovery Reference Model
- e-Discovery Team – Ralph Losey
- Defining Cyber Discovery? A Definition and Framework
- Buyers Guide Archive – Andrew Haslam’s eDisclosure Systems Buyers Guide
- Artificial Intelligence Cybersecurity Challenges — ENISA
- Continuous Active Learning for TAR (PDF)
- Even FLOE? A Strategic Framework for Considering AI in eDiscovery
Assisted by GAI and LLM Technologies
Additional Reading
- A Running List: Top 100+ eDiscovery Providers
- The Next Five Years in eDiscovery: Market Size Forecast for 2023-2028
Source: ComplexDiscovery OÜ
