The Dangers of AI in Data Rooms

Artificial intelligence is rapidly being introduced into nearly every facet of business software, including virtual data rooms and document-sharing platforms. AI-powered summaries, previews, insights, and analytics are increasingly marketed as must-have features for founders, and investors.

But when it comes to highly confidential documents, AI introduces a set of risks that are often poorly explained, vaguely documented, and rarely discussed.

This article explores the potential dangers of AI in data rooms and why Orangedox has made a deliberate decision to avoid AI entirely when it comes to handling our customers confidential documents and data.

Why AI in Data Rooms Deserves Scrutiny

Data rooms exist for one reason: to securely share confidential information. Including documents such as

Financial statements

1. M&A documents
2. Contracts and legal agreements
3. Cap tables
4. Intellectual property
5. Strategic plans

For data rooms, confidentiality is not a “nice to have”, it’s the fundamental reason for their existence.

AI systems do not function without access to data, for AI features to work whether it’s summarizing documents, generating previews, extracting metrics, or providing insights, the underlying documents must be processed, parsed, and transmitted offsite.

That simple fact creates risk.

The Core Risk - AI Requires Access to Your Documents

Unlike traditional software features, AI is not passive. It does not simply display files as they are stored. Instead, AI systems typically:

1. Read document contents
2. Parse text and structure
3. Send data to models for processing
4. Return generated outputs

This means confidential documents are actively handled by systems beyond basic storage and access controls. In a data room context, that raises several important questions:

1. Where are documents processed?
2. Are they sent to third-party AI providers?
3. Are they retained after processing?
4. Are they used to improve or train models?
5. Who ultimately has access?

In many cases, customers are unaware their confidential documents are even being shared with a third party AI provider. .

Risk of Unknown Exposure

To be clear there are currently no widely documented cases of a data room leaking confidential documents specifically due to AI features.

However, many of the largest data breaches in history occurred because risks were underestimated or ignored until it was too late. For example, in the article “Researchers Find ChatGPT Vulnerabilities That Let Attackers Trick AI Into Leaking Data” by Ravie Lakshmanan, there’s a team of researchers that found ChatGPT vulnerabilities which allowed users to trick AI into leaking data.

The absence of public AI-related data room breaches does not mean the risk is theoretical or negligible. It means the technology is newer, the market is still adapting, and transparency has not caught up to adoption.

In security-sensitive environments, uncertainty itself is a risk.

AI Adds More Attack Surfaces

Every additional system that interacts with confidential data increases the potential attack surface.

Data rooms focus on:

1. Secure storage
2. Permission-based access
3. Authentication
4. Audit logs

AI introduces:

1. Additional processing layers
2. External APIs
3. Model inference endpoints
4. Data pipelines that are difficult to inspect

Even if each component is required, complexity alone increases the likelihood of misconfiguration, misuse, or unintended exposure.

The Vague Nature of AI Disclosures

One of the most concerning aspects of AI with confidential data rooms is how it’s being communicated (or not communicated) to customers by data room providers.

AI features are often marketed with broad language such as:

1. “AI-powered insights”
2. “Smart summaries”
3. “Automated analysis”

But the underlying mechanics are rarely explained in plain terms. Customers are typically left to interpret dense terms of service that reference:

1. “Third-party service providers”
2. “Data processing for service improvement”
3. “Aggregated or anonymized data”

For organizations sharing highly sensitive documents, this lack of clarity is unacceptable.

Lessons From Real-World AI Security Concerns

Outside of data rooms, there are numerous documented examples of organizations grappling with AI-related security issues, some of these include

1. Companies restricting employee use of generative AI tools
2. Sensitive documents being shared with AI systems without clear data usage guarantees
3. Regulatory bodies questioning how AI providers store and retain data

In the article “Generative AI data violations more than doubled last year” by Emma Woollacott, it shows that the average organization is now clocking up to 223 incidents of users sending sensitive data to AI apps per month, with the figure reaching 2100 in the top 25% of cases.

These incidents highlight a common theme: once data enters an AI system, control over your data becomes next to impossible.  

Convenience vs. Confidentiality

AI features in data rooms are often framed as productivity improvements. Summaries save time. Insights surface patterns. Automation reduces manual work. But in many cases, the actual value delivered is marginal, especially compared to the potential downside. For users reviewing key documents, accuracy, access control, and trust matter far more than speed. A slightly faster summary is not worth compromising confidentiality.

Why Orangedox Chooses Security Over AI Hype

Orangedox has taken a deliberate stance when it comes to AI and its customers' confidential documents. 

Our pledge to our customers is that we won’t integrate AI to scan, process or learn from our customer documents or data. This means we will not be offering features like AI document summaries or AI generated data rooms as the risks far outweigh the benefits.

Transparency as a Differentiator

Many platforms quietly introduce AI features and rely on vague disclosures to justify data handling practices.

Orangedox takes the opposite approach. We are explicit about how customer documents are handled.

1. Files remain stored in Google Drive or Dropbox
2. Access is limited to intended recipients only
3. No third-party AI providers will have access to your confidential documents or data.
4. No AI models (including internal ones) will be trained using your documents or data. 

Why We Don’t Use AI to Code Orangedox

In addition to avoiding AI that processes customer documents, Orangedox also does not rely on AI tools to generate or maintain our product’s source code.

AI coding assistants, tools that automatically generate or suggest code, are being adopted quickly across the software industry. While they can boost developer productivity, research shows that the code they produce often introduces serious security risks:

1. Nearly half of all code generated by AI contains security flaws, even when it looks correct on the surface. In the article “Nearly half of all code generated by AI found to contain security flaws - even big LLMs affected” by Craig Hale of Tech Radar mentions how security shouldn’t be an “afterthought” in order to prevent the accumulation of massive security debt.
2. Academic research from Cornell University also indicates that iterative AI code generation (the process of AI “improving” its own output), can amplify vulnerabilities over time.

Unlike human developers, AI coding tools do not inherently understand a product’s architecture, threat model, or security context. They often replicate patterns from training data, including insecure ones, and can produce unsafe dependencies, injection vulnerabilities, or flawed logic that opens the door to exploitation.

For security-critical products like Orangedox where confidentiality, trust, and control are paramount, adding this type of risk is unacceptable. We choose deliberate engineering practices, human code review, and security-first design principles over AI-generated code.

Security, Compliance, and a Deliberate Product Philosophy

By deliberately avoiding AI-driven document processing, Orangedox helps customers keep their documents secure while reducing unnecessary legal and compliance risk. This simplifies data governance, minimizes exposure to AI security holes, and removes ambiguity around how sensitive information is handled. 

In environments such as M&A, fundraising, and legal due diligence, security and compliance are not optional, but foundational requirements. This focus is intentional. 

Orangedox’s product philosophy is clear:

1. Secure document sharing
2. Strict access control
3. Viewer-level analytics without content mining
4. Trust-first design

We believe that customers sharing confidential information deserve restraint, not experimentation.

The Future of AI in Secure Document Sharing

 “Sensitive Data Is Slipping Into AI Prompts, And Few Workers Realize the Risk” by Irfan Ahmad explains the hidden risks of everyday AI use and highlights that 70% of workers have not received formal training on the safe use of AI tools.  

AI will continue to evolve, and so will governance, regulation, and transparency. There may come a time when AI can be used in document-sharing platforms without introducing unacceptable risk, but that time is not now

Until customers have full clarity, control, and confidence over how AI systems handle their data, caution is the responsible choice.  

Choosing the Right Data Room

When evaluating a data room or document-sharing platform, organizations should ask:

1. Does this platform use AI?
2. Are third-party AI providers involved? and if so how exactly are they going to use my confidential documents?
3. Is my data used for training or retained after processing?
4. Are these practices clearly disclosed?

If the answers are unclear, then the risk of using the platform might outweigh its benefits.

Start your 14-day free trial of Orangedox Virtual Data Rooms and see what Orangedox can do for your business, or you can book a free 1-1 demo today.