Confidentiality Agreements for Artificial Intelligence Projects

Why Traditional Agreements Fall Short for AI

For decades, standard confidentiality agreements protected customer lists, proprietary source code, and financial statements through well-established contractual provisions. However, the introduction of artificial intelligence into corporate workflows changes the basic assumptions about how information moves and transforms within an organization.

In a traditional software project, data is stored and read. In an artificial intelligence project, data is processed, transformed, and interacts with complex algorithmic architectures that can retain subtle business patterns. If a master agreement merely prohibits disclosure to third parties without explicitly addressing the use of information to optimize or retrain models, the company risks having its intellectual property embedded in mathematical parameters and weights that fall beyond the reach of conventional audits.

For leadership teams at midsize and large companies, this scenario calls for modernizing the legal framework before any development or technical consulting engagement begins. A confidentiality agreement for artificial intelligence initiatives should not be treated as routine administrative paperwork, but as a technical and operational safeguard tailored to the machine learning lifecycle.

Defining the Scope: What Constitutes Confidential Information in AI

The first common mistake in these negotiations is defining confidential information solely as the raw datasets transferred to vendors or consultants. In modern projects, strategic value often lies not only in the original databases, but also in intermediate artifacts and the contextual logic built around the systems.

A robust agreement must explicitly address the various forms sensitive information takes throughout experimentation and production deployment.

• Input data and training datasets: operational databases, transaction histories, unstructured documents, and associated metadata.
• Context engineering and operational instructions: specialized prompt chains, reasoning templates, and business rules embedded in workflows.
• Derived artifacts and embeddings: vector representations, vector databases, and intermediate matrices that encode the organization's internal knowledge.
• Outputs and performance metrics: synthetic data generated during testing, evaluation reports, and comparative analyses of accuracy on business tasks.

The Essential Clause: A Strict Prohibition on Retraining

The most critical issue when working with consultants, service providers, or external platforms is what happens to the processed data. Many platforms and off-the-shelf service providers reserve the right in their default terms of service to use user interactions to improve their own foundation models or shared architectures.

In a bilateral confidentiality agreement for artificial intelligence projects, the company must require an explicit, unambiguous prohibition, without exceptions, on using corporate data, prompts, and system outputs to train, retrain, fine-tune, or improve the provider's own models or those of third parties. If a model absorbs confidential information into its latent space, reversing that process is practically impossible from a technical standpoint.

For this reason, at 1labs we recommend that corporate leaders require contractual guarantees that execution environments will be isolated, ensuring that inference runs on dedicated instances or under zero-data-retention agreements when external application programming interfaces are used.

Technical Chain of Custody and Subprocessors

Artificial intelligence projects rarely operate in a vacuum. They typically involve cloud infrastructure providers, managed vector database services, and connection gateways. As a result, confidentiality depends not only on the consulting team engaged for the project, but also on the entire underlying technology supply chain.

The contract must precisely define which third parties are authorized to receive or route confidential information. It is essential to include a process requiring advance notice and approval before the provider integrates any external service or subprocessor not identified in the original project scope.

The agreement must also require every third party involved to adhere to confidentiality obligations and technical security standards at least as stringent as those in the primary agreement. This prevents information leaks through supporting components that often fall outside routine legal oversight.

The Challenge of Returning and Destroying Information

Conventional contractual provisions typically conclude with an obligation to return or destroy all confidential material at the end of the business relationship. In the context of artificial intelligence, this concept becomes significantly more complex and requires specific definitions to avoid legal gaps.

If company-specific models were trained or hyperparameters were tuned using sensitive data during the project, simply deleting the primary data tables is insufficient. Model checkpoints, inference logs, and processing buffers may contain fragments from which corporate information can be reconstructed.

The agreement must require certified destruction or complete handover of all derived artifacts, including dedicated model weights, temporary vector databases, and debugging logs. It must also define clear technical verification procedures to ensure development environments are fully sanitized when the consulting engagement ends.

Aligning Legal Judgment with Technical Reality

One of the biggest challenges for midsize and large organizations in Latin America and Spanish-speaking markets is the communication gap between technology teams and legal departments. Corporate attorneys may not fully understand the nuances of algorithmic inference, while engineering teams tend to underestimate the implications of contractual obligations.

Developing an effective confidentiality agreement for artificial intelligence requires ongoing cross-functional collaboration. Business leaders must help their legal advisors understand the project's technical architecture: which data moves through the system, where it is temporarily stored, and which software components process it at each stage of the lifecycle.

Given the differences in data privacy and corporate protection regulations across countries in the region, these technical guidelines must always be supported by advice from legal professionals with expertise in each relevant jurisdiction.

Frequently asked questions