AI and Law: Privilege, Ethics, and Practical Guardrails
.png)
AI is now a daily part of litigation work, from triaging intake to drafting demand letters and organizing discovery. The upside is obvious: faster first drafts, cleaner issue spotting, and more time for strategy. The downside is just as real: confidentiality risk, privilege waiver arguments, hallucinated citations, and ethics missteps.
Below is a practical, litigation-first framework for using AI responsibly, focusing on privilege, attorney ethics, and guardrails you can actually implement.
Privilege basics: where AI can create risk
Attorney-client privilege and work product protection usually depend on confidentiality and purpose. AI complicates that because your “listener” may be a third-party vendor, and because outputs can blur what is factual, what is legal advice, and what is attorney mental impressions.
Key risk areas to watch:
- Third-party disclosure: Uploading client materials to a tool that stores, trains on, or reuses data can invite an argument that confidentiality was not maintained.
- Over-inclusion: Pasting entire medical records, claim files, or emails into a general-purpose chatbot when only a narrow excerpt was needed.
- Output contamination: Mixing privileged strategy into a broadly shared AI-generated summary can increase accidental dissemination inside a team.
Practically, privilege is often preserved when a vendor functions as an agent supporting legal services under a confidentiality obligation, similar to eDiscovery or cloud storage providers. But the analysis is fact-specific and jurisdiction-dependent, so the safest posture is to treat AI like any other critical vendor: diligence, written terms, and least-necessary disclosure.
Ethics: what rules are most commonly implicated
Most AI issues map to rules you already know, just with a new delivery mechanism.
1) Competence and understanding the tool
Many jurisdictions tie competence to understanding relevant technology (see ABA Model Rule 1.1, Comment 8). With generative AI, “understanding” does not require you to be an engineer, but it does require you to know:
- What the tool is doing at a high level
- What data you are sharing
- Whether outputs require verification (they do)
The ABA’s guidance on generative AI emphasizes these points, including the need for training and oversight. See ABA Formal Opinion 512 (Generative AI).
2) Confidentiality and security controls
Model Rule 1.6 is the center of gravity. Even if a disclosure does not waive privilege, it may still violate confidentiality if the lawyer did not take reasonable precautions.
Reasonable precautions typically look like: access controls, vendor security review, limiting retention, and internal policies for what can be uploaded.
3) Supervision (lawyers and nonlawyers)
If AI is part of your workflow, supervision duties (including Model Rules 5.1 and 5.3) show up fast. “The tool drafted it” is not a defense when a demand letter misstates records or a brief cites cases that do not exist.
A cautionary example is Mata v. Avianca (S.D.N.Y. 2023), where fabricated citations generated with AI contributed to sanctions. Courts have been clear: lawyers must verify. (For background reporting, see Reuters coverage.)
4) Candor to the tribunal and truthfulness
AI can invent facts, overstate medical causation, or misquote records. If you submit AI output without checking it, you risk violating duties of candor and accuracy.
Practical guardrails that work in real litigation workflows
The goal is not “never use AI.” The goal is “use AI in a way that is defensible, repeatable, and reviewable.”
Here is a guardrail set most firms can adopt without slowing down.
| Risk area | What can go wrong | Practical guardrail | What to document |
|---|---|---|---|
| Confidentiality | Client data is stored, reused, or accessed improperly | Use a legal-focused platform or approved vendor, restrict user access, avoid public chatbots for client facts | Vendor terms, security notes, internal policy |
| Privilege | Opposing counsel argues waiver due to third-party disclosure | Treat the AI provider like a litigation vendor, confirm confidentiality and data handling, share only what is necessary | Engagement terms, matter-level approval |
| Hallucinations | Fake citations, wrong dates, misstated records | Mandatory verification step, require record pin-cites, compare output to source docs | QC checklist, reviewer initials |
| Bias and overreach | Inflated liability language, unsupported damages framing | Use neutral prompts, enforce “source-grounded” drafting, require attorney edit pass | Prompt templates, redline history |
| Work product spillage | Strategy appears in a summary circulated too broadly | Separate “internal strategy” outputs from “client-facing” outputs, limit sharing channels | File naming conventions, access logs |
A simple “AI intake to filing” workflow (defensible by design)
- Classify the task: Is this internal strategy, client communication, negotiation (demand), or court filing?
- Minimize the input: Upload only what the task needs. If you are summarizing treatment chronology, you rarely need entire office email threads.
- Constrain the output: Ask for structured, source-grounded work product (chronologies, issue lists, deposition topics, demand letter sections). Require the tool to call out uncertainty.
- Human review is not optional: One reviewer for factual accuracy, one reviewer for tone and legal positioning (these can be the same person in small teams, but it must be explicit).
- Preserve a review trail: Keep the final output, the source set, and a short QC note. This is valuable for both quality and defensibility.
Where a litigation AI platform can fit (without increasing risk)
When legal teams use AI, the safest path is usually a tool designed for litigation workflows, not a general chatbot. A platform like TrialBase AI is built around litigation deliverables such as demand letters, medical summaries, deposition outlines, and trial materials, which makes it easier to standardize inputs, reviews, and team collaboration.
Even with purpose-built software, the core rule stays the same: the attorney owns the work product. AI can accelerate drafting and analysis, but counsel remains responsible for verification, judgment, and ethical compliance.
Frequently Asked Questions
Does using AI waive attorney-client privilege? Not automatically, but it can if confidentiality is not preserved. Treat AI tools like any third-party litigation vendor: limit what you disclose, ensure confidentiality obligations, and follow a consistent policy.
Can I paste medical records into a public AI chatbot? It is risky. Public, general-purpose tools may retain or reuse data depending on settings and terms. Many firms restrict public chatbots for any client-confidential information.
Do I need to disclose AI use to the court? It depends on jurisdiction and context. Some courts have issued local rules or standing orders about AI. Regardless, you must ensure citations and factual assertions are accurate and comply with duties of candor.
How do I reduce hallucinations in legal writing? Use source-grounded prompts, require record citations, and implement a mandatory verification step against the underlying documents before any client-facing or filed work.
CTA: Make AI helpful, not risky
If you want AI to speed up litigation work product without turning every draft into an ethics question, standardization is your friend. TrialBase AI helps legal teams turn case documents into litigation-ready outputs (demand letters, medical summaries, deposition outlines, and more) in minutes, so you can spend less time assembling and more time analyzing.
Explore how it fits your workflow at TrialBase AI.
