Negotiating AI and Data Clauses with SaaS Vendors

As more legal tech and AI tools arrive in firms, one unglamorous but vital job is negotiating the contracts — especially the bits about AI and data.

Even where vendors present “standard” terms, there is usually room to clarify:

how your data is used;
where and how AI models run; and
what happens if something goes wrong.

This article offers a practical checklist for negotiating AI and data clauses with SaaS vendors, focused on law firms in the UK.

It is not legal advice — you will bring your own expertise to the detail — but it should help you spot the points that matter most.

1. Be clear what the product actually does with your data

Before you dive into clauses, force yourself and the vendor to answer:

What kinds of data will we send you? (client documents, matter metadata, user behaviour, logs?)
Which of that data is used:
- purely for providing the service to us; and
- for “improving the service” or training models?
Are any AI models fine‑tuned or trained on our data, and if so, are they:
- private to us; or
- shared (in some form) across customers?

Push for answers in plain English. If the vendor cannot explain their data flows simply, treat that as a risk signal.

2. Lock down data ownership and permitted use

Contractually, you want something like:

you retain ownership of your data;
the vendor has a defined licence to use it only for:
- providing the service to you; and
- specific, narrowly defined improvement purposes, if you agree to those;
no sale or unrelated licensing of your data.

Watch for broad phrases like:

“including for any internal business purpose”;
“for research and development in connection with our other products”.

Where AI is involved, consider:

whether you are comfortable with your data being used to train general models;
if not, insisting on opt‑out or “no training on our data other than a private model for our tenant”.

3. Clarify AI model choices and locations

Many SaaS tools use third‑party AI platforms under the hood. Ask:

which providers they use (for example, specific cloud AI services);
where those models are hosted (UK, EU, elsewhere);
whether they can change providers without notice, and if so, how they will inform you.

Contract language might cover:

the jurisdictions in which data will be processed;
minimum standards for any sub‑processors providing AI capabilities;
your right to information about significant changes to the AI stack.

This is less about naming specific models forever and more about ensuring transparency and consistent safeguards.

4. Address confidentiality, privilege and client expectations

For law firms, confidentiality and privilege are central. Clauses should reflect that:

your data is subject to professional obligations;
the vendor must treat it at least as strictly as ordinary commercial confidential information (often more so);
staff of the vendor (and its sub‑processors) have appropriate confidentiality obligations.

Consider:

whether any manual access to your data by the vendor is genuinely necessary (for support, monitoring, etc.);
how such access is controlled, logged and limited to specific roles;
whether client or panel terms impose particular requirements (for example, for certain industries or jurisdictions).

Where AI summarisation or drafting is involved, vendors should be able to support your need to explain AI use to clients in clear, factual terms.

5. Nail down security and incident response

AI does not change the fundamentals of SaaS security, but it adds new surfaces (for example, model prompts and outputs). Contracts should cover:

standard security controls (encryption, access management, penetration testing);
logging of access to your data and AI‑related events;
prompt notification and cooperation in case of data incidents, including those involving AI components.

You may also look for:

commitments to regular security audits or certifications;
specific handling of prompt and output logs if they contain personal data;
clear data deletion processes on termination.

6. Understand and limit training and aggregation

Vendors often want to use “aggregated and anonymised” data for analytics or model improvement. That can be acceptable if:

anonymisation is robust, not tokenistic;
aggregation is real, not just a fig‑leaf; and
there is no realistic route back to individual clients or matters.

Clauses might:

restrict re‑identification attempts;
require the vendor not to use example data in marketing or demos without explicit consent;
give you the ability to opt‑out of certain kinds of training, even if it reduces functionality.

The more sensitive your matters, the tighter you will want to be here.

7. Allocate responsibility for AI outputs and errors

No vendor will accept unlimited liability for every possible AI mistake. But you can still:

avoid language that suggests AI outputs are inherently “unreliable junk” for which they bear no responsibility at all;
ensure that obvious system failures (for example, cross‑tenant data leakage, mis‑routing of data, breaches of processing instructions) are treated seriously.

Internally, remember that lawyers remain responsible for advice and submissions, so your own policies and training must reinforce:

verification duties;
limits on reliance; and
supervision requirements.

Contracts set the outer boundaries; firm culture and governance fill in the middle.

8. Plan for exit and data return

Lock‑in is partly legal. Make sure your contracts say what happens if you leave:

can you export your data, including AI‑assisted notes, logs and metadata, in a usable format?
how quickly will data be deleted or archived after termination?
what happens to any private models or configurations tuned on your data?

The goal is that you can move providers without leaving a ghost version of your firm’s history trapped in a black box.

9. Keep a central register of AI‑related vendor positions

Finally, as you negotiate with multiple vendors, build an internal register of:

which products you use that involve AI;
what you agreed around data use, training and locations;
any deviations from your preferred standard positions.

This helps your COLP, COFA, DPO and IT team:

answer questions from clients and regulators (“Where does our data go?”);
spot inconsistencies;
plan future procurement and renewals.

Where OrdoLux fits

OrdoLux is being developed with a strong focus on clear AI and data positions for law firms:

case data sits in a governed environment where AI features operate against your matter records;
AI activity is logged at matter level for supervision and audit;
data use and training arrangements are designed to be explainable to COLPs, COFAs, DPOs and clients.

The aim is that when you discuss OrdoLux internally or with clients, you can describe where data goes and how AI is used without needing a PhD in machine learning.

This article is general information for practitioners — not legal advice on any particular vendor contract or regulatory regime.

Looking for legal case management software?

OrdoLux is legal case management software for UK solicitors, designed to make matter management, documents, time recording and AI assistance feel like one joined‑up system. Learn more on the OrdoLux website.