Strategic Protection of AI Models as Trade Secrets

Nov 27, 2025

Gabriel Espinoza

Artificial Intelligence has become one of the most valuable assets in the tech world. A trained model concentrates data, algorithms, methodologies, and strategic decisions that may represent years of experience and development effort. Protecting it properly is essential to preserving its value.

This article explains why AI models should be registered as Trade Secrets, how to do it without exposing sensitive information, which file formats to use, and when Copyright protection is appropriate.

Why register an AI model as a Trade Secret?

AI models are highly strategic assets. Registering them as Trade Secrets allows you to:

• Maintain a competitive advantage in innovation-driven markets.
• Prevent unauthorized use, copying, or reverse engineering.
• Protect code, datasets, methodology, and prompts without making them public.
• Ensure ownership and creation date with verifiable evidence.

At Enotar.io, everything is registered through local encryption and blockchain proofs, guaranteeing total privacy.

Copyright vs. Trade Secret: What should you use for an AI model?

Although both mechanisms protect intellectual property, they serve different purposes.

Private AI models → Trade Secret

AI models operate internally and do not reveal their logic to end users.
Their value depends on the confidentiality of:

• algorithms,
• code,
• datasets,
• internal processes.

Registering these elements under Copyright would require making their content public, eliminating any competitive advantage.

This is why AI models should be registered as Trade Secrets, not Copyright.

Public or auditable code → Copyright

Copyright is appropriate when code will be:

• public,
• user-accessible,
• part of an open library,
• or visible through the front end.

In these cases, registering it as a literary work is correct.

Quick comparison

What components of an AI model can be registered as Trade Secrets?

Registering an AI model as a Trade Secret means protecting all components containing strategic knowledge:

1. Model source code

Scripts, notebooks, and programs defining the model’s logic and architecture.
Category: Code

2. Datasets and training materials

CSV files, JSON, images, audio, and other training assets.
Category: Database

3. Methodology and prompts

Rules, processes, criteria, workflows, and technical documentation.
Category: Process or Strategy

4. Trained model version

Weights, parameters, and configuration of the final model.
Category: Code or Database

Recommended file formats for registering AI models

Using the right formats ensures file integrity, verifiability, and stability.

Why must files be non-editable?

Files must be:

• Exported or final, not live projects.
• Static, so they do not change in different environments.
• Self-contained, without external dependencies.

This ensures the blockchain record reflects exactly what existed at the time of registration.

1. Model source code

Recommended formats:

• .py — Python scripts
• .ipynb — Jupyter notebooks
• .js, .ts — JavaScript/TypeScript code
• .zip — Full folder compressed
• .txt — Small configs or parameters

Recommendation: Export and compress everything in a .zip file.

2. Trained models (final versions)

TensorFlow / Keras

• .h5, .keras, .pb

PyTorch

• .pt, .pth, .bin

HuggingFace Transformers

• pytorch_model.bin, tf_model.h5, config.json

Scikit-learn / XGBoost / LightGBM

• .pkl, .joblib, .json

3. Datasets and training material

Recommended formats:

• .csv, .json, .txt
• .zip (large collections)
• Images: .jpg, .png, .webp
• Audio: .wav, .mp3, .flac
• .parquet for large datasets

4. Documentation and prompts

Recommended formats:

• .pdf — final, non-editable
• .md — technical documentation
• .txt — prompts and rules
• .zip — full documentation pack

Best practices for preparing files

• Use descriptive names:
  vision_model_v3_2025-01-10.pt
• Include version numbers:
  v1.0, v1.3-final
• Prefer static, exported formats.
• Avoid temporary or environment-dependent files.

Advanced protection with Enotar.io: registration without revealing content

One of Enotar.io’s greatest strengths is that you don’t need to upload your files:

• They are encrypted locally on your device.
• Only a cryptographic hash is registered.
• No one (not even Enotar.io) can see your content.

This allows protecting AI models without exposing code, datasets, methodology, or trained versions.

Collaborators and NDAs: protecting access to your AI model

AI projects often involve developers, data scientists, technicians, and external partners—each representing a confidentiality risk.

NDAs are essential.

NDAs as appendices

Enotar.io allows registering NDAs as appendices, which enables:

• Documenting who accessed the model.
• Maintaining blockchain-backed confidentiality agreements.
• Keeping a complete access and responsibility history.
• Adding new NDAs as your project evolves.

Conclusion

Registering an AI model as a Trade Secret is the correct strategy to protect its value, innovation, and the technical knowledge behind it. It preserves confidentiality, proves authorship, and strengthens your competitive edge without exposing code or datasets.

With Enotar.io, the process is simple, secure, and fully private: your files remain on your device, encrypted, and your creation is protected forever.

Protect your AI models today: your secrets are your advantage.