Big Tech Firms Agree That AI Standards Are Needed for Data

Three of the biggest names in tech have come together to figure out some standards for data governance as AI technology continues to evolve.

Microsoft, IBM, and Cisco are among the names who have backed a committee, which is trying to standardize data provenance protocols.

This touches upon everything from the copyright issues that have plagued AI ventures to privacy and authenticity.

What Are the Tech Giants Hoping to Achieve?

The trio have put their backing behind the OASIS Data Provenance Standards Technical Committee.

This is the brainchild of OASIS Open, a global open source and standards organization, and the Data & Trust Alliance, which describes itself as “a consortium dedicated to developing data and AI practices that create business value and earn trust.”

Their hope is that by bringing key players to the table, they can agree what they describe in a blog post as “a standardized metadata framework for tracking data origins, transformations, and compliance, helping businesses establish clearer governance practices.”

What Are the Next Steps?

This is all about transparency. The framework being worked upon will hopefully provide some guardrails for companies as they move from talking about standards to actual implementation.

As the Oasis team says in its blog post, the standards will ideally enable data producers “to deliver clear and consistent data lineage information.” This means that they will be in a better position to ensure that they are data compliant and that they are not heading into dangerous waters in terms of data privacy, security, and intellectual property rights.

Questionable AI Data Usage

Interestingly, the standards also talk about “data acquirers” having “transparency around the data they aim to acquire and a mechanism to determine whether to trust and use the data on offer.” This is particularly pertinent if companies are working with AI ventures, building their own systems on LLMs provided by others.

Microsoft works closely with OpenAI, for example, and has recently been involved in a spat with Chinese upstart, DeepSeek, which it accused of “improperly” obtaining OpenAI data to train its model.

IBM currently uses an open-source foundation for its platform, watsonx, but has written a detailed blog post about the benefits of open-source versus proprietary AI LLMs. In this, it talks specifically about the dangers of “incomplete, contradictory, or inaccurate data.” But it also nods to the issues of ensuring “training data was gathered with accountability” and that this data harvesting is “compliant with laws and regulations.”

Here, however, lies the issue, as AI frameworks are still being churned out in the USat quite a clip. Worse, there has been huge friction between those arguing for safety nets and those pushing for innovation – notably OpenAI. Also notably, OpenAI is not at this table.