Mastering Ediscovery: Your Guide to Rules, Workflows, and Advanced Tools
In this section, you’ll learn about the discovery phase of litigation, which has evolved from its origins in English common law into the modern field of ediscovery. The chapter explains how the proliferation of ESI has transformed the process, expanding it beyond litigation to include use cases like regulatory investigations and internal audits. You’ll also be introduced to key ediscovery terms, such as ESI, legal holds, and metadata.
In this section, you’ll learn about the importance of data preservation and legal holds, which are critical, proactive measures that directly impact the success of a legal case. The chapter explains that the duty to preserve data is triggered when litigation is “reasonably foreseeable”, and it outlines several events that can trigger this duty, such as a demand letter or an internal investigation.
This chapter explores the importance of cooperation in ediscovery through the use of the meet and confer process and ESI protocols. It outlines how the meet and confer process, mandated by FRCP Rule 26(f), provides a structured environment for parties to proactively address potential ediscovery challenges and negotiate terms for the exchange of electronically stored information.
Chapter Four
In this section, you’ll learn why document review is the most discussed and most expensive phase of ediscovery, as it involves making critical legal decisions like responsiveness and privilege determinations under the pressure of massive ESI volume. The chapter details the key challenges, including managing human error, protecting privilege, and navigating diverse regulatory compliance issues.
Chapter Five
In this section, you’ll learn about Early Case Assessment (ECA) as a best practice for managing discovery. The chapter defines ECA as a strategic process for rapidly investigating and analyzing evidence to evaluate risks, identify key issues, and develop an informed case strategy. You will also learn the difference between ECA and Early Data Assessment (EDA), as defined by The Sedona Conference Glossary.
Chapter Six
In this section, you’ll learn that Predictive Coding, also known as TAR, is an essential ediscovery tool that uses supervised machine learning to efficiently sort and prioritize documents based on relevance. The chapter explains the core TAR 2.0 (Continuous Active Learning) workflow, which is preferred for its efficiency over older TAR 1.0 methods, as it eliminates a fixed training phase.
Chapter Seven
In this section, you’ll learn that choosing the right ediscovery software is essential for managing modern legal matters, which are complicated by the explosion of data from sources like Slack and Teams, and stricter regulations like GDPR and CCPA. The chapter details how AI, including predictive coding (TAR), has become a core tool for review efficiency.
In this section, you will learn how generative AI is transforming ediscovery by automating tasks like document review acceleration, summarization, and privilege logging. You’ll learn about a range of real-world use cases, and best practices for responsible deployment, with an emphasis on the need for prompt engineering, strict security, and using QC checkpoints to validate AI outputs.
In this section, you will learn that managing ediscovery is more challenging than ever due to Big Data—characterized by the “Three V’s” of volume, velocity, and variety—necessitating scalable technology for defensibility. The chapter addresses how mobile device data and the rise of personal-device use create privacy and collection hurdles. It details the significant complexity introduced by Enterprise Solutions and Collaboration Apps (like Teams), particularly concerning hyperlinked files and the lack of traditional attachments.
In this section, you’ll learn that ediscovery is rapidly evolving, driven by seven key trends that will shape future best practices. These include the expansion of ediscovery use cases beyond litigation, the proliferation of emerging data sources (like IoT and GenAI), and the increasing automation via Agentic AI. The chapter emphasizes the need for strict compliance with data privacy and cross-border transfer rules, which is fueling the consolidation of ediscovery, IG, and compliance functions.
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