Protect your neural network weights, architectures, and hyperparameters with NDAs designed to prevent reverse engineering, distillation, and unauthorized fine-tuning.
Create or analyze model weight protection agreements
Create a customized NDA for sharing model weights with partners, customers, or researchers while protecting your AI IP.
Create NDAReceived model access terms? Analyze them for model-unfriendly restrictions and get negotiation guidance.
Analyze NDAKey model components that require confidentiality protection
The learned parameters that make your model work.
The structural innovations in your model design.
The training configurations that achieve best results.
Critical provisions for protecting AI model intellectual property
Prohibits analyzing model behavior to reconstruct weights, architecture, or training procedures through outputs or gradients.
Critical ProtectionPrevents using model outputs to train competing models, including knowledge distillation and behavioral cloning techniques.
Prevents CopyingControls whether and how the receiving party can fine-tune the model, and who owns resulting derivative models.
Define DerivativesProhibits systematic querying to extract training data, model capabilities, or architectural details through API access.
Block ExtractionRestricts copying, transferring, or exporting model weights outside approved environments and use cases.
Standard ProvisionClarifies who owns improvements, adaptations, and discoveries made while using the model under license.
Negotiate CarefullyHow model weights NDAs apply in common AI situations
An AI company licenses their model to an enterprise customer for on-premise deployment. The NDA prevents the customer from extracting weights, fine-tuning for resale, or sharing with competitors.
Tip: Include technical access controls (encryption, secure enclaves) alongside legal restrictions.A company shares model weights with academic researchers for evaluation. The NDA allows publication of benchmarks but prohibits sharing weights or training competing models.
Tip: Define exactly what can be published. Allow methodology discussion but protect specific configurations.A partner fine-tunes your base model for their vertical. The NDA addresses whether they own the fine-tuned weights and what happens at partnership termination.
Tip: Consider joint ownership of improvements or license-back arrangements for valuable adaptations.Customers can download model weights for offline use. The NDA must prevent redistribution while allowing legitimate local deployment.
Tip: Include technical fingerprinting requirements and audit rights to detect unauthorized copies.What to include when sharing model weights
Don't grant broad "any purpose" rights. Specify exact use cases, deployment environments, and prohibited applications for your model.
Require specific security measures: encrypted storage, secure enclaves, access logging, and periodic security audits.
Be explicit about fine-tuned models, LoRA adapters, and other derivatives. Who owns them? Can they be distributed? Do your restrictions flow through?
Require deletion of all copies upon termination, including derivatives, cached versions, and backups. Verify with certification.
Include non-compete provisions preventing use of your model to develop competing products or train replacement models.