OpenAI’s Sora isn’t just redefining AI video generation — it’s also exposing a serious flaw in how platforms detect and label deepfakes. Sora is showing us how broken deepfake detection is, and it’s a wake-up call for everyone from policymakers to tech leaders.

While OpenAI promotes Sora’s use of a metadata system meant to help platforms tag AI-generated content, the truth is more concerning: those tags often fail to appear, vanish when shared across platforms, or are easy to strip away.

The illusion of safety: how C2PA was supposed to help

Sora uses the Coalition for Content Provenance and Authenticity (C2PA) system — a framework designed to embed metadata in AI-generated media. In theory, this should make identifying deepfakes easy. But in practice, it’s not working as intended.

Once videos are downloaded, edited, or re-uploaded, those C2PA tags can disappear. The result? AI videos that look eerily real are circulating online without any visible markers that they’re fake.

That’s the core of the issue: Sora is showing us how broken deepfake detection is when it relies on systems that crumble under everyday internet use.

Why platforms can’t keep up with AI-generated content

Social media platforms like X, TikTok, and Instagram still lack consistent tools to verify the authenticity of videos. Deepfake detectors rely on watermarking and metadata — both of which are easily lost during sharing or compression.

Even when C2PA information exists, most users never see it. Very few platforms display AI-origin metadata in an obvious way, meaning that content generated by tools like Sora blends seamlessly with genuine footage.

This is exactly how deepfake misinformation can spread faster than moderation tools can respond.

Sora’s realism raises bigger concerns

Sora’s lifelike results blur the line between imagination and reality. Its ability to generate realistic human faces, emotions, and movements makes it one of the most advanced video AIs yet — and also one of the hardest to regulate.

If Sora is showing us how broken deepfake detection is, it’s also revealing how unprepared the internet ecosystem is for the next generation of synthetic media.

Without transparent labeling and cross-platform standards, users are left to question what’s real — and that uncertainty benefits those who want to deceive.

What can be done next?

Experts say that better solutions will require cooperation across tech companies, governments, and AI developers.

• Standardized metadata enforcement that survives re-uploads and compression.

• AI-native watermarks that are invisible but persistent.

• Public awareness campaigns to help users identify AI-generated content.

OpenAI’s transparency efforts are a step forward, but they also expose a painful truth: detection alone isn’t enough.

Sora’s debut is more than a technical milestone — it’s a stress test for digital trust. The fact that Sora is showing us how broken deepfake detection is should spark a global conversation about authentication, accountability, and the future of truth online.

Until deepfake detection tools evolve to match AI’s pace, the line between authentic and artificial will continue to blur — one realistic frame at a time.