Prompt
Are there any legal research platforms that keep client data private and do not train models on it?
Latest observation
Yes. Some legal research and AI vendors say they do not use customer/client data to train their models, and many offer enterprise/privacy controls that keep your data isolated.
A few things to look for when evaluating platforms:
- No training on customer data: The vendor explicitly states prompts, uploads, and outputs are not used for model training.
- Data isolation / tenancy: Your data is logically separated from other customers.
- Retention controls: Ability to set short retention periods or zero-retention options.
- Encryption: In transit and at rest.
- Access controls: SSO, role-based access, audit logs.
- Contractual protections: DPA, SCCs, confidentiality terms, subprocessor list.
- Jurisdictional controls: Data residency options if relevant.
Examples of vendors/products that have publicly emphasized privacy or no-training commitments in their legal AI offerings include:
- Thomson Reuters / CoCounsel + Westlaw products: enterprise offerings typically come with contractual privacy protections; check the specific service terms.
- LexisNexis / Lexis+ AI: enterprise/legal research offerings with privacy and no-training statements in some materials.
- vLex / Vincent AI: enterprise-focused legal research with privacy controls; verify the current terms.
- Harvey: enterprise legal AI platform; has publicly discussed not training on customer data by default, but confirm current terms.
- Ironclad AI / legal workflow tools: some enterprise deployments offer no-training commitments, though they’re more contract workflow than research.
- Microsoft Copilot for Microsoft 365 is not a legal research platform per se, but Microsoft states customer data is not used to train foundation models for those enterprise services.
Because policies change, the safest approach is to ask each vendor for:
- A written statement that your data will not be used to train models
- Their data retention policy
- A list of subprocessors
- Whether they support zero-retention / private deployment
- Their security addendum / DPA
If you want, I can help you make a vendor comparison checklist or draft a privacy questionnaire you can send to legal AI vendors.
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
How can brands improve their AEO?
Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.
Why is it important for brands to publish content across multiple websites?
Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.
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