Training data clause: Copy, customize, and use instantly
Introduction
A training data clause defines whether, how, and under what conditions any data shared under a contract can be used to train machine learning models, including large language models (LLMs). It protects proprietary or sensitive information, sets clear boundaries, and promotes transparency in AI-related use.
Below are templates for training data clauses tailored to different scenarios. Copy, customize, and insert them into your agreement.
Training data clause with complete prohibition
This version forbids use of data for model training.
The receiving party shall not use, incorporate, or allow the use of any data shared under this agreement for training, fine-tuning, or optimizing any machine learning or AI model, including but not limited to large language models.
Training data clause with opt-in requirement
This version allows training use only with express consent.
No data exchanged under this agreement may be used for model training purposes unless the disclosing party provides prior written consent for each specific dataset and training context.
Training data clause with aggregated data permission
This version allows anonymized, aggregated usage.
Aggregated and anonymized data that cannot be traced back to any individual or proprietary dataset may be used for internal model training or optimization, provided such use is disclosed in advance.
Training data clause with disclosure duty
This version requires transparency.
If any data will be used to train AI models, the party intending to do so must disclose the intended use, model type, purpose of training, and security safeguards to the data originator before use.
Training data clause with limitation to internal models only
This version restricts data use to in-house AI.
Any use of data for model training shall be limited to internally developed and securely hosted models. Use of third-party or cloud-hosted models for training is strictly prohibited without written approval.
Training data clause with license scope restriction
This version limits rights to reuse.
The data shared under this agreement is licensed strictly for the purposes stated herein and may not be reused, repurposed, or incorporated into training data for commercial or non-commercial machine learning models.
Training data clause with time-bound restriction
This version allows training use only after a delay.
Data may not be used for training purposes until [X] months after the termination of this agreement, and only if such use is consistent with any continuing confidentiality or IP obligations.
Training data clause with revocation rights
This version allows withdrawal of permission.
If the disclosing party grants permission for training data use, it reserves the right to revoke such permission at any time with [X] days’ notice, after which all derived models must cease further training on the affected data.
Training data clause with auditability requirement
This version mandates tracking of model inputs.
Parties using data for training purposes must maintain detailed records of what datasets were used, when, for what purpose, and must make such logs available to the disclosing party upon request.
Training data clause with indemnity for misuse
This version includes a protection clause.
The receiving party shall indemnify and hold harmless the disclosing party against any claims, losses, or damages resulting from the unauthorized use of data in training any machine learning or AI system.
Training data clause with human review safeguard
This version requires manual oversight.
Any data used for model training must be manually reviewed for compliance with applicable privacy, IP, and contractual restrictions prior to inclusion in any dataset.
Training data clause with exclusion of sensitive categories
This version prevents inclusion of sensitive info.
Data containing personal, financial, health, biometric, or legally privileged information shall not be used for any training purposes, regardless of anonymization or aggregation status.
Training data clause with open source compatibility check
This version ensures data licensing alignment.
The receiving party must verify that any training data derived from third-party sources is compatible with open-source license obligations and does not violate terms of use.
Training data clause with derivative work restriction
This version prevents model use to create derivative outputs.
Trained models or outputs derived from shared data shall not be used to create derivative works that replicate, reformat, or redistribute the original data.
Training data clause with territorial limitation
This version restricts training to certain jurisdictions.
Any permitted training must occur only in jurisdictions that offer equivalent data protection standards to those of the disclosing party’s principal place of business.
Training data clause with academic research exception
This version allows limited academic use.
Data may be used for non-commercial academic research training purposes only if anonymized and if the research institution agrees in writing to maintain confidentiality and refrain from redistribution.
Training data clause with transparency in public disclosures
This version mandates disclosure in publications.
If any model trained using shared data is described or published, the party must disclose that the training dataset included data covered by this agreement, unless prohibited by confidentiality terms.
Training data clause with no public dataset contribution
This version prevents contribution to shared datasets.
No portion of the data shared under this agreement may be uploaded to, or used in, the creation of publicly accessible datasets intended for training of machine learning models.
Training data clause with access controls for training environments
This version sets access boundaries.
Any training environments where data is processed must be restricted to authorized personnel only, with strong authentication, activity logging, and data usage tracking in place.
Training data clause with retention limit for training sets
This version limits how long training data can be stored.
Any data used for training must be deleted or permanently anonymized after [X] months, unless continued retention is approved in writing by the disclosing party.
Training data clause with no self-improving models
This version bans automatic training from live data.
The receiving party may not use data to support models that retrain or fine-tune themselves in real time based on data submitted during system use.
Training data clause with re-identification prohibition
This version prevents deanonymization.
The receiving party shall not attempt to reverse-engineer, re-identify, or de-pseudonymize any anonymized data used in model training.
Training data clause with ethical use affirmation
This version ties usage to AI ethics principles.
Any model trained using shared data must comply with documented ethical AI principles, including fairness, non-discrimination, explainability, and accountability.
Training data clause with specific model class restriction
This version limits use to certain model types.
The data may only be used to train classification models and shall not be used for generative models, language models, or other unrestricted output systems.
Training data clause with sandboxed experimentation only
This version permits research, not production use.
Use of data for training is limited to sandboxed, non-production environments, and outputs may not be deployed or commercialized without prior written authorization.
Training data clause with model discard condition
This version allows model destruction if terms are breached.
In the event of unauthorized training use, the disclosing party may require the receiving party to delete any trained models, weights, and related artifacts without delay.
Training data clause with consent representation
This version requires proof of consent.
The party providing data for training warrants that it has obtained all necessary consents and rights to allow lawful use of such data in AI model development.
Training data clause with feedback loop exclusion
This version prevents outputs from being reused as inputs.
No outputs generated from trained models using shared data may be reintegrated into the training process as new inputs or feedback.
Training data clause with separate dataset isolation
This version requires partitioning training sets.
Data received under this agreement must be stored and used in a training dataset that is logically and physically isolated from other data sources, including public datasets.
Training data clause with legal review prior to use
This version requires review by legal counsel.
Before using any data for training, the receiving party must submit a summary of intended use for legal review to confirm compliance with this clause.
Training data clause with notification of future use
This version anticipates long-term plans.
If the receiving party intends to use shared data for model training at a later date, it must notify the disclosing party in writing at least [X] days in advance.
Training data clause with clear data labeling protocol
This version mandates labeling of training material.
Any data intended for use in training must be clearly labeled and stored in a designated repository, separated from other categories of data collected under this agreement.
Training data clause with output restriction by data type
This version limits scope of model output.
No model trained on proprietary datasets may be used to generate outputs that resemble or reconstruct original datasets or their structure, schema, or metadata.
Training data clause with licensing attribution clause
This version requires acknowledgement.
If data is used in a permitted training context, the source of such data must be acknowledged in documentation, whitepapers, and other disclosures, unless agreed otherwise.
Training data clause with revenue share trigger
This version shares proceeds from trained models.
If models trained on shared data are commercialized, the disclosing party shall receive [X]% of gross revenue attributable to that model, subject to separate licensing terms.
Training data clause with annual review window
This version allows parties to revisit the clause.
This training data clause shall be reviewed annually by both parties to assess its continued suitability and adjust restrictions or permissions as necessary.
Training data clause with opt-out mechanism for individual records
This version enables granular control.
Individuals whose data is included under this agreement must be provided a clear mechanism to opt out of model training, and such requests must be honored within [X] days.
Training data clause with retention of derived insights
This version permits keeping learnings.
Even if model training is disallowed or terminated, the receiving party may retain general insights or observations learned during permitted exploratory analysis, excluding the raw data.
Training data clause with embargo period
This version delays training until a future date.
Data received under this agreement may not be used for training purposes until [X] days following delivery, during which time the disclosing party may revoke permission.
Training data clause with model deletion on termination
This version enforces model disposal.
Upon termination of this agreement, any AI models trained in whole or in part using shared data must be deleted or decommissioned unless retention is specifically approved.
Training data clause with security certification requirement
This version limits training to certified environments.
Data may only be used for model training in environments certified under recognized standards such as ISO/IEC 27001, SOC 2, or equivalent frameworks.
Training data clause with no cross-training between clients
This version prohibits multi-client datasets.
Under no circumstances shall data from multiple clients be combined to form a unified training dataset unless each client has provided written, specific consent.
Training data clause with privacy-by-design enforcement
This version requires architectural controls.
All models trained on permitted data must incorporate privacy-by-design principles, including minimal data retention, access logging, and secure inference mechanisms.
Training data clause with source attribution restriction
This version prohibits tracing outputs back to source.
No trained model shall produce outputs that directly quote or reproduce identifiable portions of the source data, even if training was permitted.
Training data clause with real-time monitoring obligation
This version ensures oversight during training.
Any live training operation using shared data must be actively monitored in real-time by a qualified operator with authority to halt training upon detecting anomalies or violations.
Training data clause with limited purpose training
This version narrows use cases.
Data may only be used to train models for specific purposes explicitly defined in Schedule A, and shall not be reused for unrelated applications or transferred to other departments.
Training data clause with research-only data license
This version permits use in non-commercial experiments.
Data made available for training purposes shall be licensed strictly for academic or non-commercial research, and shall not be monetized, distributed, or reused outside of the research setting.
Training data clause with third-party audit right
This version allows independent review.
The disclosing party reserves the right to appoint an independent auditor to review the receiving party’s data governance, model training procedures, and compliance with this clause.
Training data clause with training pause protocol
This version enables a hold on model development.
At the disclosing party’s request, the receiving party must immediately pause any ongoing training involving the shared data until a dispute or concern is resolved.
Training data clause with fixed model cap
This version limits how many models can be trained.
No more than [X] distinct machine learning models may be trained using the data shared under this agreement without prior written amendment or addendum.
Training data clause with emergency deletion trigger
This version provides a safety switch.
If the disclosing party identifies unauthorized use or exposure of training data, the receiving party must immediately delete any associated models, training logs, and input datasets.
Training data clause with explainability requirement
This version mandates model interpretability.
All models trained using shared data must be explainable, with documentation sufficient to demonstrate what the model learned and how it processes input data.
Training data clause with restricted access to training logs
This version controls visibility into training data use.
Logs associated with training activities, including prompts, inputs, and tuning configurations, must be retained securely and accessible only to compliance personnel.
Training data clause with separate infrastructure mandate
This version isolates training systems.
Any use of shared data for model training must occur on separate infrastructure from systems handling unrelated workloads or production environments.
Training data clause with non-commercial foundation use
This version permits basic model use, not resale.
Data may be used to train internal foundation models that support business operations, but may not be included in any product, service, or software sold to third parties.
Training data clause with time-stamped usage log
This version promotes traceability.
The receiving party must maintain a time-stamped log of each instance data is loaded into a training environment, including the user ID and purpose of access.
Training data clause with no latent memorization tolerance
This version prohibits data leakage into model outputs.
Models must be evaluated and filtered to prevent memorization or regurgitation of sensitive or proprietary content included in training datasets.
Training data clause with watermarking for AI traceability
This version enables content tracking.
Any data used for training must be digitally watermarked or fingerprinted to allow future tracking of its influence on generated outputs.
Training data clause with chain of custody documentation
This version requires tracking data access history.
The receiving party must maintain a chain-of-custody log documenting all transfers, access events, and transformations applied to data used for training.
Training data clause with ML-specific indemnity
This version provides narrow protection.
The receiving party shall indemnify the disclosing party for any damages, penalties, or legal claims arising from use of training data in AI systems that breach third-party rights or applicable laws.
This article contains general legal information and does not contain legal advice. Cobrief is not a law firm or a substitute for an attorney or law firm. The law is complex and changes often. For legal advice, please ask a lawyer.