AI in virtual data rooms: what the tools do today and what to check

Two years ago, “AI” on a data room website usually meant optical character recognition and a keyword search. Today most large providers describe some form of machine learning or language model inside the product, and buyers are left to work out which claims change how a deal runs and which are a new label on an old feature.

This guide is a practical sorting exercise. It covers the AI jobs that save real time in a data room, where the technology still falls short, which providers list AI features in our directory, and the questions that protect your documents before anyone presses an “ask AI” button.

What does AI actually do inside a data room?

Strip away the marketing and the AI features in deal rooms fall into five jobs. Each maps onto a task that someone on the deal team would otherwise do by hand.

Notice that every row describes a first pass. AI produces a draft index, a candidate answer, a summary or a list of flags. A person still decides what is shown, said or relied on.

Which data room providers list AI features?

In our feature check, five of sixteen providers list AI features: Ellty, Datasite, Ansarada, Drooms and Box. They come from quite different places. Datasite and Ansarada are long-established deal platforms. Drooms is a European provider with a strong real estate base. Box is a general content cloud rather than a dedicated deal room, and has no structured Q&A module in our records. Ellty is a full diligence room in the same class as iDeals and Datasite, with Q&A, permissions, watermarking, rights management and an audit trail, and with AI built into a newer, cleaner interface.

The eleven providers without AI features in our records include several of the strongest names in the market. iDeals and Intralinks, for example, score highly on security and deal features without listing AI. That is a useful reminder: AI is a feature to weigh, not a test that separates good rooms from bad ones. You can check how each provider performs across all five rating pillars in our provider directory, or read the Datasite and Ansarada profiles directly.

Our records note whether AI features are listed, not how capable they are. Two providers that both “have AI” can differ enormously in what it does, so test the specific jobs that matter to your deal.

Where does AI save the most time on a deal?

Building the room

The first days of a sell-side process are spent turning a shared drive dump into an indexed room. Auto-categorization reads each file and proposes where it belongs, flags duplicates and suggests consistent names. On a room of several thousand documents, that is the difference between a week of paralegal time and a morning of review. On a small, tidy room it barely matters.

Finding things during review

Keyword search fails on scanned documents with poor OCR and on concepts phrased in many ways. Semantic search lets a buyer ask for “termination rights on change of control” and find the relevant clauses even when they use different wording. This is where buyers notice AI most.

Answering the question log

In an auction, the same question arrives from several bidders in slightly different forms. Matching tools spot duplicates and point to existing answers; drafting tools propose an answer from documents already in the room. The sell-side expert edits and approves. Speed matters here, because slow answers are one of the main reasons rooms stay open longer than planned.

What can go wrong with AI in a data room?

Deal documents are about as sensitive as corporate data gets, and AI introduces risks that ordinary features do not.

• Confident errors. A summary can omit a critical exception or misread a number. A drafted answer can sound authoritative and be wrong. On a deal, a wrong answer given to a bidder can become a disclosure problem.
• Training on your data. If a provider or its model supplier uses room content to train models, fragments of your documents could influence outputs elsewhere. Most enterprise providers say they do not; get it in writing.
• Processing outside the room’s region. AI features may call a model hosted in another country or by a third party, even when the files themselves are stored in a chosen region.
• Wider visibility than intended. A search or summary feature should respect folder permissions. If it can draw on documents a user cannot open, it leaks information through the back door.
• Unclear records. If an AI-drafted answer is sent, the audit trail should show who approved it.

What should you ask a provider before switching AI on?

The same short list works for any vendor. Send it with your security questionnaire.

1. Confirm what the AI does

   Ask for a list of the AI functions in the product and which ones are on by default. Test each one you plan to use on your own sample documents.

2. Check training and retention

   Get written confirmation that room content is not used to train models, by the provider or any model supplier, and how long prompts and outputs are retained.

3. Check where processing happens

   Ask which third-party model providers are involved, where the processing takes place, and whether that matches the data residency you chose for the room.

4. Check permissions are respected

   Confirm that search, summaries and drafted answers only draw on documents the user is allowed to see, and test it with a restricted bidder account.

5. Check control and logging

   Make sure administrators can switch AI off per room or per user group, and that AI-assisted actions appear in the audit trail with the approving user.

If personal data is in the room, AI processing is still processing under privacy law. The EU General Data Protection Regulation expects you to know which processors and sub-processors handle the data and where, and AI model suppliers are no exception. The EU’s Artificial Intelligence Act adds its own obligations, mostly aimed at higher-risk uses rather than document search and summaries, but it is worth knowing which category a provider places its features in. For a structured way to think about AI risk inside your own organization, the NIST AI Risk Management Framework is a widely used reference.

Does AI make a data room more expensive?

Sometimes. Some providers include AI features in their standard plans; others place them in higher tiers or price them as an add-on. In the market model behind our VDR pricing calculator, requiring AI features adds about 10% to a typical flat monthly or per-user budget, a smaller uplift than single sign-on or redaction. Treat that as a rough guide and ask each provider whether AI is included at the tier you are quoted.

The more important cost question is time. If auto-indexing saves a week of preparation, or faster Q&A takes a few weeks off the process, the saving on monthly room fees and adviser hours can outweigh any uplift.

Who benefits most from AI features?

• Sell-side teams with large, untidy document sets. Indexing and duplicate detection pay off immediately.
• Buyers reviewing several targets at once. Search and summaries help decide where to focus scarce reviewer time.
• Auctions with heavy Q&A volume. Matching and drafting reduce the load on the seller’s experts.
• Cross-border deals. Translation and multilingual search help reviewers working outside their own language.

A focused deal with a few hundred well-organized documents and one buyer gains less. It still benefits from good search, but the core requirements stay the same: permissions, Q&A, watermarking, an audit trail and responsive support.

How should AI weigh in your choice of provider?

As a tie-breaker in most cases, and as a requirement only when your deal has the volume to use it. Start from the fundamentals described in our guide to what a data room provider supplies, shortlist on security and deal features, then compare the AI functions of the finalists on your own documents. A provider whose AI genuinely speeds up indexing and Q&A, while keeping data in your chosen region and out of model training, is worth more than one with a longer list of AI claims you never test.