# AI in eDiscovery: TAR, Privilege Screening, and Defensibility > Legal · AI Course · LEG-4 > Source: https://ibl.ai/solutions/legal/course/ai-in-ediscovery > Last updated: 2026-08-25 **Use AI across the discovery lifecycle while keeping the process defensible — technology-assisted review, privilege screening, validation, and the meet-and-confer record.** ## The Short Answer **TAR has judicial acceptance but generative privilege screening does not yet, so validation and documentation carry the defensibility. ibl.ai runs discovery workloads inside the firm's own environment where you own all the code and the data — so a client's entire document population never transits a third-party platform.** On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network. [Request Access](https://ibl.ai/contact) · [Explore Legal](https://ibl.ai/solutions/legal) ## Course facts - **Level:** Advanced - **Duration:** 6 hours across 8 modules - **Format:** Cohort workshop with review labs - **Modules:** 8 - **Catalog code:** LEG-4 - **Frameworks covered:** FRCP Rule 26, EDRM, ABA Model Rules ## What is this course about? Technology-assisted review has judicial acceptance; generative privilege screening does not yet have the same track record. This course covers where AI changes discovery economics, the validation and sampling that make a process defensible, and how to document methodology so it survives a challenge from opposing counsel. ## Who is this course for? - eDiscovery counsel and litigation support - Litigation partners and associates - Document review managers - In-house litigation counsel ### What do I need before starting? - eDiscovery experience - Familiarity with the EDRM lifecycle ## What will I be able to do afterwards? - Map where AI changes discovery economics across the EDRM lifecycle - Apply technology-assisted review with an appropriate validation protocol - Assess the error profile of generative privilege screening honestly - Prove recall to an opposing party with documented sampling - Disclose AI use appropriately in the discovery protocol ## What does each module cover? ### Module 1 — Where does AI change discovery economics? Mapping the EDRM lifecycle to find where cost actually concentrates. _(40 min)_ **Objectives** - Map cost across the EDRM lifecycle - Identify where AI changes the economics - Set realistic expectations for savings **Topics:** EDRM lifecycle · Cost concentration · AI impact points · Realistic savings **Activity:** Map cost across a real matter's discovery lifecycle. ### Module 2 — How does TAR work and why do courts accept it? Technology-assisted review, the case law supporting it, and the protocols courts have approved. _(50 min)_ **Objectives** - Explain how TAR workflows operate - Summarize the supporting case law - Select a protocol appropriate to the matter **Topics:** TAR workflows · Supporting case law · Protocol selection · Seed set considerations **Activity:** Design a TAR protocol for a matter with stated document volume and issues. ### Module 3 — Can generative AI screen for privilege? The error profile of generative privilege screening, assessed honestly against its track record. _(55 min)_ **Objectives** - Assess generative screening error rates - Compare against traditional and TAR approaches - Determine an appropriate role given the current record **Topics:** Generative screening · Error profile · Comparison · Appropriate role **Activity:** Test generative privilege screening against a known-privileged set and measure errors. ### Module 4 — How do you validate and prove recall? Sampling and validation that produces a number you can defend to an opposing party. _(55 min)_ **Objectives** - Design a statistically valid sampling protocol - Estimate recall with a defensible confidence interval - Document the validation for production **Topics:** Sampling design · Recall estimation · Confidence intervals · Validation documentation **Activity:** Design and execute a validation protocol producing a defensible recall estimate. ### Module 5 — How do you document for defensibility? The record that survives a challenge, built during the process rather than after it. _(45 min)_ **Objectives** - Document methodology contemporaneously - Record decisions and their reasoning - Prepare for a challenge to the process **Topics:** Contemporaneous documentation · Decision records · Challenge preparation · Expert support **Activity:** Build the defensibility record for the Module 4 validation. ### Module 6 — What do you disclose at meet-and-confer? Negotiating the discovery protocol when AI is part of the process. _(45 min)_ **Objectives** - Determine what to disclose about methodology - Negotiate protocol terms covering AI use - Handle an opposing party's objections **Topics:** Methodology disclosure · Protocol negotiation · Objection handling · Transparency limits **Activity:** Negotiate a discovery protocol covering AI use in a role-play. ### Module 7 — How does proportionality apply? Rule 26 proportionality and cost shifting when AI changes what is reasonably accessible. _(40 min)_ **Objectives** - Apply proportionality when AI reduces review cost - Argue cost shifting in an AI context - Anticipate how proportionality arguments change **Topics:** Rule 26 proportionality · Cost shifting · Reasonable accessibility · Changing arguments **Activity:** Draft a proportionality argument accounting for AI-reduced review cost. ### Module 8 — Building a defensible TAR workflow The workshop module: a complete workflow with a validation report. _(55 min)_ **Objectives** - Assemble a complete defensible workflow - Produce the validation report - Prepare the methodology declaration **Topics:** Workflow assembly · Validation report · Methodology declaration · Production readiness **Activity:** Complete the workflow and produce a validation report and methodology declaration. ## What is the capstone project? **Defensible TAR workflow with a validation report.** Design and execute a complete discovery workflow with a TAR protocol, an honest assessment of generative privilege screening, a statistically valid recall estimate, contemporaneous defensibility documentation, and a methodology declaration ready for production. _Deliverable:_ A validated workflow, a recall estimate with confidence interval, and a methodology declaration. ## How are learners assessed? - Recall estimate must carry a defensible confidence interval - Privilege screening error rate measured against a known set - Methodology declaration reviewed by a colleague acting as opposing counsel ## What ships with the course? - **Facilitator guide.** Session-by-session running order, discussion prompts, and the questions that reliably derail a room. - **Learner workbook.** Exercises, checklists, and the templates each module's activity produces. - **Hands-on lab environment.** A sandboxed ibl.ai deployment so exercises run against real agents, not screenshots. - **Assessment bank.** Scenario questions and rubric criteria mapped to each stated learning outcome. - **Source bibliography.** Every primary regulation and standard cited on this page, linked and dated. ## Which AI agents does this course use? - [Discovery Agent](https://ibl.ai/solutions/legal/agent/discovery-agent) - [Case Research Agent](https://ibl.ai/solutions/legal/agent/case-research-agent) - [Compliance Agent](https://ibl.ai/solutions/legal/agent/compliance-agent) - [Knowledge Agent](https://ibl.ai/solutions/legal/agent/knowledge-agent) ## Where does the course material come from? Every module is grounded in primary sources — the regulation, standard, or research itself, not a summary of it. Each was resolved at authoring time. - [EDRM Model](https://edrm.net/edrm-model/) — EDRM. The lifecycle framework the course maps AI onto. - [Federal Rules of Civil Procedure Rule 26](https://www.law.cornell.edu/rules/frcp/rule_26) — Cornell Legal Information Institute. Proportionality and disclosure obligations analyzed in Modules 6 and 7. - [CourtListener](https://www.courtlistener.com/) — Free Law Project. Case law on TAR acceptance researched in Module 2. - [Model Rules of Professional Conduct](https://www.americanbar.org/groups/professional_responsibility/publications/model_rules_of_professional_conduct/) — American Bar Association. Competence and supervision duties across the review process. ## Delivery notes Binding guidance for anyone preparing and delivering this course: - Module 3 must be honest that generative privilege screening lacks TAR's judicial track record. Overstating its acceptance would put firms in a defensibility position they cannot support. - Module 4 needs real statistics, taught properly. eDiscovery validation is frequently done badly and a course that hand-waves the sampling produces indefensible numbers. - Use a synthetic document population with known privilege for Module 3. Measuring error rates requires ground truth that a real matter cannot provide. - Module 6's role-play should include a sophisticated objection. Opposing counsel increasingly challenge AI methodology and participants need to have answered a hard version once. - Have an experienced eDiscovery special master or expert review the course. Defensibility standards are set by practice and case law, not by the technology. ## Why run AI training on a platform you own? - **You own the course, not a licence to it.** Course content, learner data, and the platform run inside your perimeter — you own all the code and the data. - **Model-agnostic delivery.** Run the course's AI components on any LLM — Claude, GPT, Llama, Gemini, Command — and switch anytime. - **No per-seat training licences.** Usage-based or self-hosted, so cost tracks actual use rather than headcount. - **Deploy anywhere.** Cloud, private VPC, on-premise, or fully air-gapped — including for cohorts that cannot use public AI tools. ## Frequently asked questions ### What does the AI in eDiscovery: TAR, Privilege Screening, and Defensibility course cover? Technology-assisted review has judicial acceptance; generative privilege screening does not yet have the same track record. This course covers where AI changes discovery economics, the validation and sampling that make a process defensible, and how to document methodology so it survives a challenge from opposing counsel. It runs 6 hours across 8 modules across 8 modules, at advanced level, and closes with a capstone: Defensible TAR workflow with a validation report. ### Who should take AI in eDiscovery: TAR, Privilege Screening, and Defensibility? It is written for eDiscovery counsel and litigation support, Litigation partners and associates, Document review managers, In-house litigation counsel. Prerequisites: eDiscovery experience; Familiarity with the EDRM lifecycle. ### Can we run this course on our own infrastructure? Yes. ibl.ai is model-agnostic and deploy-anywhere — cloud, private VPC, on-premise, or fully air-gapped — and you own all the code and the data. Cohort data, submissions, and any material learners upload stay inside your perimeter, which matters for legal teams that cannot send work to a public AI tool. ### How do we get access to AI in eDiscovery: TAR, Privilege Screening, and Defensibility? Request access and we will set it up for your cohort — hosted by ibl.ai, or running against your own deployment. Tell us the group size and timing you need, and whether it should run inside your own perimeter. ### How much does AI training for legal cost on ibl.ai? There is no per-seat pricing — you pay for usage or self-host and pay only for the infrastructure, so a 5,000-person rollout does not cost 5,000 licences. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University. ## More Legal courses - [AI and Attorney-Client Privilege: The Architecture Question](https://ibl.ai/solutions/legal/course/ai-and-attorney-client-privilege): Whether sending client material to a third-party AI service waives privilege — the confidentiality analysis, the reasonable-efforts standard, and the deployment that avoids the question. - [Verifying AI Legal Research: Never Cite a Hallucination](https://ibl.ai/solutions/legal/course/verifying-ai-legal-research): A verification protocol for AI-assisted research — why fabricated citations happen, how to catch them every time, and the supervision structure that makes it non-optional. - [Contract Review with AI: Redlining, Risk, and Playbooks](https://ibl.ai/solutions/legal/course/contract-review-with-ai): Encode your firm's negotiating positions into an AI review workflow — clause extraction, deviation detection, risk scoring, and where a partner still reads every word. - [Client Intake and Conflicts Checking with AI](https://ibl.ai/solutions/legal/course/client-intake-conflicts-with-ai): Faster intake without a missed conflict — entity resolution across a matter history, adverse party detection, and why the conflicts decision stays human. - [Ethical AI Use Under the ABA Model Rules](https://ibl.ai/solutions/legal/course/ethical-ai-under-aba-model-rules): A rule-by-rule walk through AI in practice — competence, confidentiality, supervision, fees, and communication — with a firm policy you can adopt. - [Brief Drafting with AI: Structure, Citation, and Review](https://ibl.ai/solutions/legal/course/brief-drafting-with-ai): Where AI genuinely accelerates motion practice — outlining, fact-section assembly, authority organization — and the review gates that keep a filing safe.