Today's eDiscovery AI Brief
Eleven sources scanned every morning. Scored out of 100: Recency 50, Impact 15, Source 10, and Topic fit 25, with noise penalties for vendor or event-heavy items.
In a remarkably short span, courts across the country have begun answering a question that did not exist a few years ago: when a litigant types case-related information into a publicly available generative artificial intelligence (AI) platform, are those interactions protected from discovery? Beginning with United States v..
This week’s kitchen sink for July 31, 2026 (with meme from Gates Dogfish) discusses a bad news week for Anthropic, border searches under duress & more!
Artificial intelligence is no longer a theoretical issue in litigation. Law firms are developing AI policies, courts have issued guidance on attorney use of generative AI, and litigants are increasingly encountering AI-generated work during discovery.... By: DOAR
Sixty-eight percent of breached organizations had no AI governance policy in place, and five of the six governance controls measured in both years lost adoption. The composition behind that headline figure is where the story is.
Most organizations now have some form of internal guidance on how employees may use artificial intelligence tools – but many of those documents would not hold up in litigation, a regulatory examination or a vendor audit. This alert outlines the structural and substantive elements that separate an enforceable corporate AI use.
Shark week, er, eDiscovery Case Week, continues today! In the case In re Class Action Settlement Administration Litigation, District of Columbia District Judge John D. Bates stated “The Court agrees with plaintiffs” on several disputes between the parties, including whether Rule 502(b) ought to apply to waiver of privilege,.
For any public sector office beginning to think seriously about AI for data review, the most important thing to understand is that you do not need to have everything figured out before you start.
Matter planning
A focused planning board for scope, sampling, model controls, validation, and the people who need to explain the workflow later.
Jump into the same curated resource categories available in the full library: guides, case law, vendor documentation, research, and practitioner references.
A high-level primer for understanding what Technology-Assisted Review is, how the workflow moves from goals to validation, and how prediction scores and metrics should be interpreted before relying on TAR in a real matter.
6 resources→Use these after the primer when you want the primary overview materials behind the TAR workflow, including EDRM, FJC, and Grossman/Cormack resources.
4 resources→Neutral or semi-neutral references users can rely on when learning TAR or building defensible workflows.
4 resources→Workflow-focused resources for connecting AI assistants to eDiscovery context, MCP servers, and legal plugin patterns while keeping human review, read-only defaults, and non-legal-advice boundaries visible.
3 resources→Resources for teams using generative AI, analytics, classifiers, or AI-assisted review features in litigation workflows.
4 resources→Key decisions and legal themes users should understand before proposing or challenging TAR.
2 resources→Practical TAR explainers, workflow notes, and analysis that add context after the news brief and resource library.
Model access alone is not enough. MCP and legal plugins connect an AI assistant to the matter context, permissions, and workflows your team already relies on.
Read MoreMachine learning has quietly rewritten the rules of document review. Here's what every legal professional needs to know before their next large-scale matter.
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