Uploading an unpublished cycling dataset, internal process report, or draft patent analysis to an artificial intelligence platform requires careful review. Battery scientists and research and development (R&D) managers need to understand who owns the material, where it is stored, who can access it, and how it moves through external systems.
Wensura publishes specific terms covering uploaded research, encryption, access controls, third-party model processing, and deletion. Those provisions give technical teams a concrete basis for deciding whether the platform fits their organization’s data-governance requirements.
You Retain Ownership of Uploaded Research
You retain full ownership of every document, dataset, and research file uploaded to the platform. Delta3CoreTec LLC, which operates Wensura, does not claim intellectual-property rights over that content.
That provision applies to proprietary material such as electrode formulations, cycling results, process parameters, internal reports, supplier data, and unpublished manuscripts. Uploading the material to Wensura does not transfer its ownership to the platform.
Wensura also gives account holders the right to export uploaded content. Your organization can therefore maintain its own project records and preserve research materials outside the platform rather than relying on Wensura as the only repository.
Ownership does not replace internal authorization. Your team still decides which files may enter an external system based on contracts, research partnerships, export controls, confidentiality agreements, and project-specific policies.
AWS Hosting and Encryption Protect Stored and Transmitted Data
Wensura stores data on Amazon Web Services infrastructure. The platform uses Advanced Encryption Standard with a 256-bit key (AES-256) for stored data and Transport Layer Security 1.3 (TLS 1.3) for information moving between systems.
The two controls protect different stages of the workflow. AES-256 protects files held in storage, while TLS 1.3 protects data during transmission between your browser, Wensura, and its supporting infrastructure.
Wensura also lists regular security audits and penetration testing among its security practices. These measures are intended to identify weaknesses in the platform’s infrastructure and access controls before they affect normal research activity.
These protections provide a defined technical framework for handling uploaded research. They do not remove the need for your organization to review the platform against its own cybersecurity standards and approved-vendor requirements.
Uploaded Documents Remain Isolated
Wensura presents its Data Foundry environment as a place where researchers can upload proprietary PDFs and datasets while keeping that material isolated and encrypted. The uploaded content supports your own knowledge base and research workflow rather than becoming part of a shared collection available to other accounts.
Proprietary research is never shared with other platform users. That separation supports teams working with internal reports, private datasets, prepublication findings, or other materials that should remain confined to authorized workflows.
Document isolation should still be evaluated alongside account permissions. A protected file can only remain appropriately restricted when access settings, credentials, and internal team practices are managed carefully.
Role-Based Access Controls Support Team Management
Wensura includes role-based access controls within its published security framework. Permissions can therefore be assigned according to a person’s responsibilities rather than giving every account holder identical access.
This structure can suit R&D teams with principal investigators, laboratory scientists, data analysts, project managers, and external collaborators. Each role may require a different level of access to research queries, documents, datasets, and shared conversations.
Role-based controls work best when they are paired with regular internal reviews. Your organization remains responsible for credential security, account removal, staff changes, and decisions about which team members should have access to each research project.
Proprietary Research Is Not Used to Train Models
Uploaded proprietary research is not used to train AI models. It also says research documents, datasets, and other files are processed solely to provide the platform’s functions.
That means uploaded material can supply context for the task you request without becoming training data for a future general-purpose model. A document may be processed to answer a question, support an analysis, or create an output tied to your account.
Wensura also collects account and usage information needed to operate the service. Its policy covers functions such as processing queries, maintaining platform security, preventing fraud, improving performance, and analyzing aggregate usage patterns.
The separation between uploaded research and aggregate usage information gives technical teams two categories to examine during governance review. Research content carries project-specific value, while usage data relates to how the platform operates and improves.
Anonymized Queries May Be Processed by External AI Providers
Wensura uses multiple large language models within its research workflow. Its privacy policy states that anonymized query data may be sent to OpenAI, Anthropic, and Google for processing, with personally identifiable information excluded from those requests.
This arrangement means Wensura includes third-party AI processing. Teams with strict supplier, confidentiality, or external-model policies should account for those providers when determining which prompts and research contexts are appropriate for the platform.
A sensible review can examine the information contained in each query rather than focusing only on uploaded files. A prompt may reveal a proprietary project direction, experimental parameter, target material, or technical problem even when it contains no personal information.
Wensura also identifies Amazon Web Services as its hosting provider and Stripe as its payment processor. Information may be disclosed when required by law, regulation, or legal process.
Wensura Defines Its Retention and Deletion Timelines
Wensura retains account data throughout an active subscription and for 90 days afterward. Uploaded research content is deleted within 30 days of account termination unless you request immediate deletion.
The platform may retain anonymized usage analytics indefinitely for product improvement. That category is treated separately from the research documents and datasets associated with a terminated account.
These timelines allow R&D managers to compare Wensura with internal project-closeout procedures. Teams working under contracts, research agreements, or institutional policies can determine whether the standard deletion schedule fits their obligations.
The right to export content also supports orderly offboarding. Before ending an account, your team can preserve approved project records and confirm which uploaded materials should be removed immediately.
Data Security and Scientific Accuracy Require Separate Reviews
Encryption, access controls, and deletion policies address how data is handled. They do not determine whether an AI-generated scientific interpretation is accurate, complete, or suitable for an experiment.
Wensura classifies its AI-generated analyses, summaries, and recommendations as informational outputs. Its terms state that those outputs carry no guarantee of accuracy and should be independently verified.
That separation is especially relevant for experimental design, degradation analysis, materials selection, and process decisions. A securely stored dataset can still produce a flawed conclusion if a query contains weak assumptions or the model misinterprets the evidence.
A structured evaluation should therefore include two review tracks. The data-governance review can assess ownership, encryption, access, external processing, and retention. The scientific review can examine sources, reasoning, reproducibility, and agreement with experimental evidence.
Test Wensura With an Appropriate Research Workflow
Wensura currently offers a 14-day free trial of its Pro plan. Pro includes multi-model review, dataset analysis, automated machine learning, time-series forecasting, source citations, shared conversations, and support for uploaded documents.
A controlled trial can begin with a representative research task that your organization has approved for external processing. Choose a noncritical dataset, a defined document set, or a question with an independently known reference point.
During the trial, record how the material is uploaded and who can access it. Review the information included in each query, the outputs Wensura produces, and the verification required before those outputs enter your research workflow.
That test gives your team direct evidence of how Wensura fits existing technical and governance procedures. It also creates a practical basis for deciding whether the platform can support wider use across battery R&D projects.
Frequently Asked Questions
Who owns documents and datasets uploaded to Wensura?
You retain full ownership of the data, documents, and research uploaded to Wensura. Wensura states that it does not claim intellectual-property rights over your content and allows you to export uploaded material.
Does Wensura use proprietary research to train AI models?
Wensura states that proprietary research is not used to train AI models or shared with other platform users. Wensura processes uploaded files to provide the research and analysis functions requested through your account.
How does Wensura protect uploaded research data?
Wensura uses AWS infrastructure, AES-256 encryption for stored data, and TLS 1.3 for transmitted data. Wensura also lists role-based access controls, regular security audits, and penetration testing among its security practices.
Are Wensura queries sent to external AI providers?
Anonymized query data may be sent by Wensura to OpenAI, Anthropic, and Google for processing. Wensura states that personally identifiable information is excluded from those requests, although teams should still review whether a query contains proprietary technical context.
What happens to uploaded research after a Wensura account ends?
Wensura deletes uploaded research content within 30 days after account termination unless immediate deletion is requested. Wensura retains account data for 90 days after the subscription ends and may retain anonymized usage analytics indefinitely.
Review the Terms and Test a Controlled Workflow
Wensura provides published terms covering research ownership, AWS hosting, encryption, access controls, external model processing, and deletion. Those details give battery scientists and R&D leaders a practical foundation for comparing the platform with internal security and data-governance requirements.
Review Wensura’s current privacy terms, then use the 14-day free trial to test an appropriate document set, dataset, or research question. Evaluate data handling and scientific performance through separate review processes before expanding Wensura across additional battery R&D work.
