Elsevier has a long history as a trusted source for curated, peer-reviewed scientific content with domain-specific knowledge.

• Learn more about how we ensure Responsible AI use and protect user data privacy, including our Five Responsible AI Principles to drive responsible, ethical and appropriate use

For each use case and solution, we select the most appropriate large language model from a carefully chosen range of leading providers — including OpenAI, Anthropic and others — hosted securely on cloud services from Microsoft Azure or AWS. We tailor model selection to meet the specific needs of the task, ensuring both performance and safety.

At Elsevier, we recognize that the proper handling of personal data is very important to our customers and the communities we serve. As such, we are committed to behaving with integrity and responsibility regarding data privacy.

• All user inputs and data are treated in line with our Privacy Policy and Responsible AI principles

We treat personal data in line with applicable privacy laws, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). We also take further steps to ensure we meet the privacy expectations of our users and the scientific community.

• Read more about our privacy principles

No. Our enterprise agreements with AWS, Microsoft Azure, OpenAI, and Anthropic include zero-retention contracts, so your prompts and documents are never used for large language model development. Elsevier also does not use customer data in our private cloud environments for this purpose.

Our use of third-party LLMs is private, with no data shared for public model improvement. We do not review individual or organizational search prompts—only aggregated, anonymized patterns to enhance system performance and relevance.

With Elsevier AI solutions, your firm benefits from enhanced data privacy and enterprise-grade safeguards.

• A user’s prompt/ask/document is sent securely using TLS 1.2 or higher to the trusted Elsevier environment. The prompt is parsed for intent and broken down into separate prompts by an embeddings model to retrieve information from our content store.

• The prompt, along with content response, is then sent using TLS 1.2 or higher to our foundational model providers within the trusted Elsevier environment.

• A grounded, generated response is then presented to the user in the Elsevier AI solution.

• User prompts and responses in their conversation history are secured in encrypted databases with AES-256 level encryption.

• Our architecture and associated contracts preclude third-party model providers from logging or training models based on users’ prompts.

• Elsevier strictly controls what content is shared with, retained by, or used for training by vendors. Neither Microsoft (Azure) nor AWS (Bedrock) retain Elsevier’s content or customer prompts for training or storage.

• User prompts remain private; only aggregated, anonymized insights are used by Elsevier to improve the service.

• Data security and encryption: Your data is securely stored and encrypted at rest using AES-256 within the trusted Elsevier environment. Your data is encrypted in transit using TLS 1.2 or higher. Please see Encryption Standards policy for more details.

• Elsevier has zero-retention contracts in place with our foundation model providers. This ensures that your prompts and documents are never stored or used to train any large language models (LLMs). By using Elsevier’s AI solutions, your organization benefits from our enhanced data privacy and enterprise-grade safeguards.

• All Elsevier AI services, including our product environments, are hosted in leading cloud data centers provided by Amazon Web Services (AWS) or Microsoft Azure. Services may be hosted in Europe or the US based on application and regulatory requirements.

• We protect your data wherever it goes. At rest, it’s locked down with Advanced Encryption Standard (AES)-256 encryption. When your data is in transit, we use TLS 1.2 or higher, which not only encrypts data but also authenticates the server and verifies data integrity.

• We use industry best practices such as web application firewalls, application and infrastructure vulnerability scanning, secure code reviews, bug bounties, and other preventive, detective, and response controls to protect our systems and your data from attackers.

• Our architecture and associated contracts preclude third-party model providers from logging or training models based on users’ conversations.

• All cross-border transfers of personal data are subject to appropriate safeguards compliant with the GDPR, including the EU Standard Contractual Clauses. Customer personal data is not transferred to China.

Elsevier advances sustainability through our products, research advocacy and social responsibility programs. We take specific steps to reduce the environmental impact of our AI tools, including:

• Using a multi-model approach, which allows us to apply smaller, more energy-efficient models for less intensive tasks, reducing overall energy consumption

• Leveraging Microsoft Azure and AWS data centers powered by green electricity

• Maintaining a robust data governance program to minimize unnecessary data storage and processing, supporting energy efficiency

As part of RELX, Elsevier prioritizes environmental responsibility by reducing our carbon footprint and advancing sustainable practices. We align with the UN Sustainable Development Goals, especially Climate Action and Responsible Consumption, and are committed to shaping a sustainable future. Learn more about our environmental efforts across our environmental efforts across RELX.

Elsevier’s responsible AI use is guided by our Responsible AI Principles, which are integrated throughout the development lifecycle of our solutions. Learn more about Elsevier Responsible AI Principles.