By 2026, the time when businesses can use AI to make opaque decisions will be over. Regulators in the United States have made it very clear: if your AI uses any data to train its algorithms, you need to tell people where that data comes from and how it was collected.
There are already laws on the books, such as California’s Assembly Bill 2013, that are pushing companies towards full disclosure and transparency about how they construct their generative AI systems. In addition to state legislation, U.S. federal agencies such as the Federal Trade Commission and the U.S. Copyright Office will increase their scrutiny of how organizations use data, their compliance with copyright laws, and their protection of consumers.
For the AI community, this is more than just a change in legal requirements; it represents a new paradigm for designing, documenting, and deploying all AI models.
What is AI Data Transparency?
AI Data Transparency is defined as providing organizations with requirements that identify the key characteristics of data sources used to develop machine-learning models.
- Examples of key characteristics include:
- The sources of your training data (e.g., public data, licensed data, proprietary data)
- The methods used to collect your training data
- If you have included any personal data or copyright-protected data in your training’s datasets.
- Your organization’s procedures for correcting or deleting data.
The bottom-line goal of these requirements will be to foster accountability, explainability, and fairness in AI systems.
California AB 2013: The Game-Changer
California’s AB2013 is one of the biggest events of the 21st century. The primary components of this act include requiring software developers to disclose their data sources for training purposes and requiring companies to provide proof of the data they have used. Failing to comply with AB2013 will result in a cease-and-desist order against the offending company.
In addition, AB2013 has implications for the future of generative AI companies (especially those developing large language models or image-generation systems). Although AB2013 is a California law, it is likely to have a national impact because any company that conducts business in California must comply, regardless of its headquarters location.
Government Regulation of Copyright and Consumer Protection
1. Copyright Law Enforcement
The Copyright Office is looking into how AI can exploit copyrighted material.
The biggest concerns are that AI uses unauthorized copies of books, pictures, and media, doesn’t pay the creator (for example), and doesn’t provide clear visibility into the legal ownership status of AI’s output.
As a result, there is more pressure on AI companies to keep comprehensive records of their training sets.
2. FTC Regulation
The Federal Trade Commission is crucial to preventing companies from illegally using or misusing information and from engaging in unfair marketing practices toward consumers.
The focus of the FTC’s investigation is on:
- Misrepresentation of AI capabilities, failure to disclose data usage, and violations of users’ privacy.
- The FTC will not allow companies to use complexity as an excuse; companies must provide true transparency by law.
NIST AI RMF: The Building Block for Compliance
NIST’s AI RMF 1.0 helps build a framework for data transparency.
Functions performed are:
- Map: Data sources and how they are used
- Measure: Determine bias and quality risks of data
- Manage: Set up controls for governance of the data
By complying with NIST standards, businesses can build a strong foundation for meeting all legal requirements at the federal and state levels.
The “Right to Know” and “Right to Delete”
A major point of debate in 2026 will be user control over data.
Right to Know:
Users may ask whether their data has been used for AI training.
Right to Delete:
Some people may request that their data not be included in training datasets.
This could create some technical difficulties, because for large models, the data is just one of many parts learned as part of the model, so we would expect businesses to establish ways of handling these types of requests as a normal part of their process.
Key Considerations for Businesses
There are several key considerations for businesses to take into account when trying to comply with the new AI data transparency regulations:
1. Risk of Legal Consequences
If you’re unable to provide disclosure of your training data, you may face lawsuits or fines. You may also face an operational shutdown.
2. Operational Complexity
You’ll need the capability to track your data throughout its lifecycle, from ingestion through all steps until it’s deployed into a model.
3. Increased Costs
Meeting compliance standards will require additional investments in tools, legal expertise, and data governance systems.
4. Trust and Brand Reputation
Using AI systems that provide transparency will help to build trust among users, thereby enhancing your brand credibility.
Guidelines for Achieving Compliance in 2026
The new laws can be complex, so it is very important that companies approach compliance proactively. Below are some guidelines for successfully navigating the new regulatory environment:
1. Conduct Data Audits
Document and audit all data sources, their processing, and their use.
2. Establish Data Governance Frameworks
Utilize the standards established by NIST and develop a comprehensive data governance framework.
3. Utilize Compliance Automation Tools
Platforms like Vanta and Secureframe allow you to monitor and manage your compliance obligations.
4. Maintain Transparent Documentation of Training Datasets and Methodologies
Create a record of your organization’s training datasets and the methodologies used for developing your AI models.
5. Establish Processes for Responding to User Requests
Create a method to efficiently respond to user requests for access to their data and to process deletion requests.
Conclusion
Starting in 2026, AI data transparency is mandatory under legal and ethical standards. Laws such as California AB 2013, coupled with increased enforcement by agencies such as the Federal Trade Commission (FTC), require businesses to rethink their data management practices at every stage of the AI lifecycle.
The businesses that act now by implementing a governance framework, investing in compliance tools, and embracing transparency will have an advantage over their competitors and an opportunity to mitigate risk in the AI economy.
