Why video is the canonical source of truth for AI and your brand’s best defense

The Wild West of web scraping is changing, due in large part to OpenAI’s deal with Disney. The deal allows OpenAI to train on high-fidelity, human-verified cinematic content – intended to combat AI slop fatigue. 

This deal opens up new opportunities to reinforce your brand’s visibility and recall. AI models are hungry for high-quality data, and this shift turns video into an essential asset for your brand.

Here’s a breakdown of why video is the new source of truth for AI and how you can use it to protect your brand’s identity.

How AI brand drift happens

When a large language model’s training set lacks data on a specific brand, the LLM doesn’t admit that it doesn’t know. Instead, it interpolates, filling the gaps in your brand’s story. It makes guesses about your brand identity based on patterns from similar brands or general industry information. 

This interpolation can lead to brand drift. Here’s what it looks like when an AI model narrates an inaccurate version of your business.

Say you represent a SaaS company. A user asks ChatGPT about one of your product’s features. But the model doesn’t have information about that specific feature.

So, the model constructs elaborate setup instructions, pricing tiers, and integration requirements for the phantom feature.

This has surfaced for companies like Streamer.bot, where users regularly arrive with confidently wrong instructions generated by ChatGPT – forcing teams to correct misinformation that the product never published. 

AI brand drift happens to local businesses, too. As one restaurant owner told Futurism, Google AI Overviews repeatedly shared false information about both specials and menu items.

To correct brand drift and prevent AI from distorting your brand message, your company must provide a canonical source of truth.

Video as a source of truth

By producing authoritative videos (e.g., a demo that explicitly clarifies pricing), you provide strong semantic information through the transcript and visual proof. The video becomes the canonical source of truth that makes things clear, overriding opinions from Reddit and other sources.

In contrast, a text file contains low entropy. A statement like “50% off” is identical whether it was written in 2015 or 2025. Text often lacks the timestamp of reality, making it easy for AI to manipulate or lose the context of the real world.

To fix this, you need a medium with more data packed into every second. A five-minute video at 60 frames per second contains 18,000 frames of visual evidence, a nuanced audio track, and a text transcript.

Video enables LLMs to capture non-verbal, high-fidelity cues, creating a validation layer that preserves the visual evidence often flattened or lost in written content.

Creative studios like Berlin-based Impolite specialize in high-production-value video that provides the chaotic, non-repetitive entropy that AI needs to verify. The studio’s work for global brands serves as the high-density data source that prevents brand drift.

For example, Karman’s “The Space That Makes Us Human” project is a masterclass in creating a canonical source of truth, using high-fidelity, expert-led video to anchor brand identity.

Dig deeper: How to optimize video for AI-powered search

Authenticity as a signal

As deepfakes proliferate, authenticity is shifting from a vague moral concept to a hard technical signal. Search engines and AI agents need a way to verify the provenance.

Is this video real? Is it from the brand it claims to be?

For AI models, real-world human footage is the ultimate high-trust data source. It provides physical evidence, such as a person speaking, a product in motion, or a specific location. In contrast, AI-generated video often lacks the chaotic, non-repetitive entropy of real-world light and physics. 

The Coalition for Content Provenance and Authenticity (C2PA) is developing a new provenance standard to verify authenticity. The organization, which includes members such as Google, Adobe, Microsoft, and OpenAI, provides the technical specifications that enable this data to be cryptographically verifiable.

At the same time, the Content Authenticity Initiative (CAI), spearheaded by Adobe, drives the adoption of open-source tools for digital transparency.

Together, the two organizations go beyond simple watermarking. They allow brands to sign videos the moment they begin recording, providing a signal that AI models can prioritize over unverified noise.

Ever notice that tiny “CR” mark in the corner of certain media on LinkedIn? This label stands for content credentials. It appears on images and videos to indicate their origin and whether the creator used AI to produce or edit them. 

When you click or hover over the “CR” icon on a LinkedIn post, a sidebar or pop-up appears that shows:

• The creator: The name of the person or organization that produced the media
• The tools used: Which software (e.g., Adobe Photoshop) the creator used to edit or generate the media
• AI disclosure: A specific note if the content was generated with AI
• The process: A history of edits made to the file to ensure the image hasn’t been deceptively altered

Some creators are already looking to circumvent the icon. Some have shared tips to hide the tag.

While some call it LinkedIn shaming, its presence signals authority. It’s also gaining traction. 

Google has begun integrating C2PA signals into search and ads to help enforce policies regarding misrepresentation and AI disclosure. The search giant has also updated its documentation to explain how C2PA metadata is handled in Google Images.

Dig deeper: The SEO shift you can’t ignore: Video is becoming source material

How verified media maintains its integrity

For content marketers, adopting C2PA is a defensive moat against misinformation and a proactive signal of quality.

If a bad actor deepfakes your CEO, the absence of your corporate cryptographic signature acts as a silent alarm. Platforms and AI agents will immediately detect that the content lacks a verified origin seal and de-prioritize it in favor of authenticated assets.

Here’s how it works in practice.

1. Capture: The hardware root of trust

Select Sony cameras use the brand’s camera authenticity solution to embed digital signatures in real time. The signature uses keys held in a secure hardware chipset. Sony uses 3D depth data alongside the C2PA manifest rather than a 2D screen or a projection to verify that a real 3D subject was filmed.

Similarly, select Qualcomm’s products support a cryptographic seal that proves the photo’s authenticity. In addition, apps like Truepic and ProofMode can sign footage on standard devices.

2. Edit: The editorial ledger

C2PA-aware software, such as Adobe Premiere Pro, integrates content credentials. This allows brands to embed a manifest listing the creator, edits, and software.

Think of it as a content ledger. Content credentials act as a digital paper trail, logging every hand that touches the file:

• When an editor exports a video, the software preserves the original camera signature and appends a manifest of every cut and color grade.
• If generative AI tools are used, relevant frames are tagged as AI-generated, preserving the integrity of the remaining human-verified footage.

3. Verify: Tamper-proof evidence in action

If the content is altered outside of a C2PA-compliant tool, the cryptographic link is severed.

When an AI model performs an evidence-weighting calculation to decide which information to show a user, it will see this broken signature.

Dig deeper: How to dominate video-driven SERPs

The expert content workflow

Information overload is constant nowadays. Traditional gatekeepers are struggling because AI generates content faster than humans can verify it. Authenticity becomes scarce online as Audiences increasingly seek out authenticity and strive to distinguish signal from noise.

From LLMs to search engines like Google, AI systems struggle with the same challenge. Verified subject matter experts (SMEs) are emerging as critical differentiators and as guarantors of credibility and pertinence.

An SME is a human anchor point of credibility for both humans and machines. When brands pair expertise with verifiable video documentation, they create something AI can’t replicate: authentic authority that audiences can see, hear, and trust.

Why expert video should be the source material 

A video transcript of an expert explaining a complex topic often captures colloquial, nuanced details that polished, static blog posts miss. Here’s how to use expert-led videos as the starting point of your content flywheel: 

• Text stream: Extract the transcript to create authoritative, long-form blogs, FAQs, and social captions. This provides the semantic foundation for text-based retrieval.
• Visual stream: Pull high-quality frames for infographics and thumbnails. This provides visual proof that anchors the text.
• Audio stream: Repurpose the audio for podcast distribution, capturing your expert’s tonal authority.
• Discovery stream: Cut vertical TikTok and YouTube clips. These act as entry points that lead AI agents back to your canonical source.

By repurposing a single high-density video asset across these formats, you create a self-reinforcing loop of authority.

This increases the probability that an AI model will encounter and index your brand’s expertise in the format that the model prefers. For example, Gemini might index the video, while Perplexity might index the transcript.

It doesn’t have to be fancy, as this clip from Search with Sean shows:

What to look out for

Before you hit record, identify where your brand is most vulnerable to AI drift. To maximize the surface area for AI retrieval, proceed this way: 

• Identify the gap: Where is AI hallucinating elements of your story? Find the topics where your brand voice is missing or being misrepresented by outdated Reddit posts or competitor noise.
• Anchor with verified experts: Use real people with verifiable credentials. AI agents now cross-reference experts against LinkedIn data and professional knowledge graphs to weigh the authority of the content.
• Preserve the nuance: Marketing and legal departments often strip it from blog posts, making them generic. Video preserves the colloquial, detailed explanations that signal true expertise. 

Here’s a concrete example recorded with Semrush’s Brand Control Quadrant framework:

Dig deeper: The future of SEO content is video – here’s why

Context still beats compliance

With infinite, low-cost AI slop cropping up, it’s going to get harder and harder to fight deepfakes. But it’s harder for an AI to hallucinate a real physical event than a sentence.

The most valuable asset a brand owns is its verifiable expertise. By anchoring your brand in expert-led, multimodal video, you ensure that your identity remains consistent, protected, and prioritized.

A clear hierarchy of data is emerging: high-fidelity, cryptographically signed video is the premium currency. For every other brand, the mandate is simple: Record reality. If you don’t provide a signed, high-density video record of your business, the AI will hallucinate one for you.