At the heart of the Web3 revolution and the pursuit of sovereign, privacy-respecting AI, we present the nilGPT Privacy Data Review. This independent evaluation of Nillion Labs’ AI model is built on an exclusive framework and a rigorous audit of publicly available data, reflecting our vision of a future where privacy is treated as a fundamental right.
The scoring system follows a comprehensive guide created specifically for this project, accessible here, and evolves dynamically with innovations and feedback from the decentralized community.
This nilGPT Privacy Data Review reinforces our mission to promote transparency, data protection, and digital sovereignty for AI users. Our goal remains clear: to enlighten and inform, without filter or influence, so we can build together a fairer and more transparent AI ecosystem.
update : 25/09/21
Key Findings from the nilGPT Privacy Data Review
Model
Meta-Llama-3.1-8B currently, with experimental DeepSeek integration; roadmap towards Llama-3.1-70B and Gemma-3-27B-IT in the coming weeks.
Data Collection
Prompts stored: Chat histories are securely stored in nilDB, a decentralized MPC-based storage network, where no data can be reconstructed or leaked even in case of compromise. A
Use for training: No, inputs/outputs are not used to train any AI model, ensuring data confidentiality. A
Account required: Registration via email or wallet. Pseudonymity is possible, but an account is always required. B
Data retention duration: Data is stored in nilDB and fully controlled by the user. Since September 2025, in-app deletion ensures immediate and user-managed data removal. A
User Control
Deletion possible: Yes, in-app deletion is now available (September 2025 release), while export remains limited to manual requests. A
Export possible: Users can request full access and data portability in compliance with GDPR. Although export is handled via support, it guarantees users’ right to recover their data. A
Granularity control: There are no fine-grained controls within the app to choose what is collected or stored. C
Explicit user consent: Obtained where legally required (e.g., marketing), but no strong evidence of explicit, easy consent for all processing activities. C
Transparency
Clear policy: Accessible, detailed, and up-to-date (July 2025). A
Change notification: Users are informed of material changes with updated effective dates. A
Model documentation: Models are listed with references; source code was published on GitHub in September 2025 (NillionNetwork/nilgpt), along with initial technical documentation and a clear roadmap for further releases. A
Privacy by Design
Encryption (core & advanced): Inputs/outputs are encrypted locally in the browser using a user-chosen passphrase (which never leaves the device), then secret-shared across nilDB nodes. Models run within TEEs, and the entire backend operates on nilCC (Nillion’s confidential compute layer). All data is encrypted in transit and at rest. A
Privacy-Enhancing Technologies: MPC for data at rest, TEEs for inference. Differential privacy/federated learning not needed in current model. A
Auditability & Certification: No third-party audits yet. Initial source code was published on GitHub in September 2025, but independent verification is still pending. C
Transparency & Technical Documentation: Architecture and privacy principles are publicly documented, with source code released in September 2025. Documentation covers core privacy and technical measures, with additional details planned. A
User-Configurable Privacy Features: Architecture and privacy principles are described publicly; in-app data deletion is now available (September 2025 release), but fine-grained privacy controls and complete documentation are still missing. C
Hosting & Sovereignty
Sovereignty: B
- The backend of NilGPT is hosted in nilCC, which uses Nillion’s bare metal servers in Virginia.
- nilDB nodes are used for data storage.
- There are 3 nodes used for NilGPT, one managed by Nillion and the other two by external parties.
Legal jurisdiction: Nillion Labs is a company based in Ireland, and the data collected by NilGPT is subject to Irish (EU) law and the General Data Protection Regulation (GDPR). A
Local option: It is not possible to host NilGPT locally, as it is only accessible via a web application. D
Big Tech dependence: NilGPT does not rely on public cloud infrastructure. In fact, nilCC, which hosts the backend and runs AI models, runs on bare metal, while nilDB nodes can be hosted anywhere without compromising user data security and confidentiality. A
Open Source
Publicly available model: The nilGPT application code was released in September 2025 on GitHub (NillionNetwork/nilgpt). The first version is fully accessible to the public, marking the beginning of its open-source journey. A
Clear open source license: The models operate under specific licenses (Llama, DeepSeek). The application’s license was partially disclosed with the September 2025 open-source release, but a complete open source license for all modules is still pending. B
Inference code available: Models are open source. Initial inference code was published on GitHub in September 2025, but it remains partial and lacks full documentation and audits. B
Remarks
NilGPT, developed by Nillion Labs, implements advanced privacy engineering, with user data split and stored using MPC, encrypted, and processed within secure Trusted Execution Environments (TEEs). The platform operates independently of Big Tech cloud infrastructure, relying instead on sovereign bare-metal hosting. Its policies are detailed, transparent, and regularly updated. The models in use are open source with specific licenses (Llama, DeepSeek), and NilGPT’s own application code was open-sourced in September 2025, marking the beginning of its licensing and documentation process. As of September 2025, both in-app, self-service data deletion and a first public open-source release have been delivered. Further audits and comprehensive technical documentation are still pending, but these milestones already strengthen NilGPT’s privacy and sovereignty profile. Upcoming audits and full documentation, combined with its existing robust architecture, are expected to further consolidate this profile. A re-evaluation is planned once these changes are implemented.
nilGPT Privacy Data Review : Overall Score
78.3/100
- Data Collection : 20 + 20 + 15 + 20 = 75
- User Control : 20 + 20 + 5 + 5 = 50
- Transparency : 20 + 20 + 20 = 60
- Privacy by Design : 20 + 20 + 5 + 20 + 5 = 70
- Hosting & Sovereignty : 15 + 20 + 0 + 20 = 55
- Open Source : 20 + 15 + 15 = 45
Total : 75 + 50 + 60 + 70 + 55 + 50 = 360
23 x 20 = 460
360 / 460 × 100 = 78.3
This evaluation is provided for informational purposes only and reflects a subjective analysis based on publicly available data at the time of publication. We do not guarantee absolute accuracy and disclaim all liability for errors or misinterpretations. Any disputes must be submitted in writing to futurofintenet@proton.me
For full methodology, see our complete scoring guide here: LLM Privacy Rating Guide
