Buying AI tools for R&D: Avoiding delays due to privacy and security assessments

Preparing internal stakeholders for new AI tools – what steps do I need to take?

The sooner IT and security teams are brought in, the easier it will be to get these tools in the hands of your teams. If these stakeholders aren’t involved until too late, it often adds weeks or months to the procurement process – costing R&D teams valuable time where they could be reaping the benefits of AI-assisted research.

Start the internal conversations now

Discuss your AI needs with stakeholders inside your organization as soon as possible – this will position them as partners in the quest to find the right solution, not roadblocks.

Who should you engage in these discussions?

Work with your IT, technology and security teams and learn about the steps to procuring an external AI tool. What are the potential blockers, and what are the red flags that might make procuring a product a non-starter?

What are your internal AI governance requirements?

Become familiar with your organization’s guidance on AI tool procurement and usage. This will enable you to ensure solutions are compliant before investing too much time.

What security and IT reviews should you expect?

Becoming familiar with the necessary security and IT reviews will allow you to start the process sooner, and provide the vendor with the information they need to ensure the product is best positioned for success.

What happens if you take too long to involve internal stakeholders?

If these teams are not consulted early, AI tool procurement can drag on and become a long, arduous process. Unexpected reviews and security checks can add significant time to the procurement process and cost your teams valuable time using AI tools to accelerate research.

What are the privacy and security questions that shape every AI evaluation in R&D?

“Will this tool see or track my research activity?”

A lack of data privacy risks exposure of your R&D team’s research intent, strategy and any potential discoveries. This could be done through search tracking, individual user-level activity records or surveillance by internal or external actors.

What should you look for? Ensure users' searches are private and only accessible to them, with fully encrypted, private and secure usage.

“Are AI models being trained with my data or search activity?”

Many generalist AI tools are trained through user activity – this includes inputs, queries and other activity. While this is common, this is a non-starter for an R&D team looking to use AI to maintain a competitive advantage.

What should you look for? Ask whether sessions are secure, with no training based on user activity. Searches, history and uploaded documents should not be used to train any models.

“What happens to the content I upload into the tool?”

R&D teams need to be able to upload documents and other proprietary data to make the most of any research tool. However, they need to be sure this content remains secure – there should be no fears of storage, reuse or leakage, ensuring your data remains your data.

What should you look for? All uploaded documents should be encrypted, session-specific and only visible to the uploader. Once users delete an uploaded document, the vendor does not retain it in any form.

“Can anyone else, internal or external, see my work on the platform?”

R&D teams need to be able to research with freedom, without exposing their methodology to other parties, such as colleagues, IT teams and external vendors with access to systems.

What should you look for? Data should be user-isolated, not visible to other users or vendors and only sharable if the user chooses to share it.

“How reliable is the underlying content fueling the AI tool’s responses?”

Most AI shows sources, but in order to see real impact in R&D, researchers need confidence in where the responses are coming from. This can mean all the difference when it comes to risk management and helps avoid making key innovation decisions based on bad data.

What should you look for? R&D teams should prefer AI tools that use only licensed, vetted, peer-reviewed sources. Responses should include traceable references so researchers can know for sure where the outputs came from.

Security, quality and explainability — combined

LeapSpace, from Elsevier, is a research-grade AI workspace built for high-stakes research. LeapSpace addresses many of the above concerns, with the goal of creating a tool that R&D teams can use every day, with confidence that their data and search activity is protected.

• In LeapSpace, your activity is private, secure and encrypted. We never use your data to train any LLMs.

• LeapSpace runs on a private infrastructure, not a shared AI ecosystem. This ensures high privacy and security standards, with secure user sessions and strict data governance.

• LeapSpace’s privacy and security guardrails are designed to help protect customer IP and support data privacy.

• LeapSpace’s closed, curated data ecosystem pulls from only trusted scientific content – not unvetted web sources.

• LeapSpace supports secure, scalable use across enterprise research environments. R&D teams gain deep, critical insights from trusted scientific content, while helping organizations keep their IP and data to themselves.

Click here for more on Elsevier’s approach to safeguarding user information.