As AI-generated content becomes more ubiquitous, tech companies are exploring ways to label this content to provide transparency to users. Recently, Meta announced it will begin detecting and tagging images created by AI services, applying invisible metadata markers to indicate the content is not authentic:
"Meta will apply the labels to any content carrying the markers that is posted to its Facebook, Instagram and Threads services, in an effort to signal to users that the images - which in many cases resemble real photos - are actually digital creations,” reported Reuters.
Why does this matter? AI-generated images, audio, video and text have potential to mislead if their artificial origins are not made clear. As Nick Clegg, President of Global Affairs at Meta stated:
"Even though the technology is not yet fully mature, particularly when it comes to audio and video, the hope is that we can create a sense of momentum and incentive for the rest of the industry to follow.”
The Need for Data Context
Meta's move highlights a broader trend - the growing need to treat data as a strategic asset and provide transparency into its origins and intent. In an abstract way, it’s future-proofing the media we create and share to maintain integrity. Recognizing this need, organizations like the Content Authenticity Initiative are coming together to actively pursue an open industry standard for provenance.
Virtru shares this mission, building security and privacy capabilities like Attribute-Based Access Controls (ABAC) and label-based encryption to maintain context on how sensitive data is accessed and used - a sentiment shared by Bill Newhouse, Cybersecurity Engineer at National Institute of Standards and Technology (NIST) National Cybersecurity Center of Excellence (NCCoE): "One should organize one's data so that you can have it work for you, you can protect it, you can share it as you wish... and aim to have control of that process."
Similarly, with data analytics now mission-critical, organizations must have visibility and control to build trust. Metadata like tags and provenance provide this critical context, and create a sort of “future-proofing” of data security and controls.
According to Dana Morris, Virtru’s SVP of Product & Engineering, this emphasis on data tagging and data classification is essential for protecting each piece of data individually, to contribute to a larger zero trust strategy: “It's not that you would throw out any concept of trying to enforce things at the perimeter, the network, or the application, but it's about figuring out how you can put additional policy controls on the data itself."
Trust Through Data Governance
Appropriate data governance, encompassing policy controls at the intersection of tagged data and entitled identities, allows companies to extract the maximum value from data while preserving its integrity.
In an increasingly AI-driven world, metadata and integrity checks will only grow in importance. Those laying the groundwork now with governance strategies that balance security, privacy and usability will have a competitive edge. Because one thing is increasingly clear - to AI or not to AI is no longer the question. But creating responsible and transparent AI is the imperative… and the world is watching.
