How Synthetic Credibility Is Outsmarting Traditional Defenses

AI-generated fraud has quickly outpaced the traditional defenses that companies rely on to protect their brands. From eerily realistic deepfake videos to cloned product listings that hijack traffic and revenue, the threat is no longer hypothetical. It is sophisticated, fast-moving, and increasingly undetectable by the systems designed for a different era of fraud.

The danger lies in what researchers call “synthetic credibility,” AI-generated content that imitates the language, look, and behavior of legitimate voices. Its content is designed not to deceive the inattentive, but to persuade the observant. The kind of fraud that fools even the most trusted engines, platforms, and search algorithms.

AI-Generated Music and $10M in Stolen Royalties

Consider the case of Michael Smith, a North Carolina man who used AI tools to generate hundreds of fake songs. Paired with automated bots to simulate listener engagement, these fake tracks earned him over $10 million in streaming royalties before anyone noticed. The platforms, misled by metrics like rapid growth and play counts, had no reason to suspect wrongdoing—until it was too late.

Deepfake Influencer Scams That Cost Millions with Stolen Credibility

In another incident, a convincingly lifelike AI-generated video featuring financial expert Martin Lewis began circulating on social media. In it, the fake Lewis endorsed a cryptocurrency fraud. Similar celebrity scams have defrauded victims across the UK, Europe, and North America, resulting in collective losses of tens of millions. The video was eventually taken down, but not before it damaged Lewis’s reputation and highlighted the limitations of existing systems in stopping synthetic impersonation at scale.

Review Bombs Powered by AI: Trust Under Siege

Even more insidious are AI-written reviews. According to the Associated Press, fake reviews are spreading across retail platforms, indistinguishable from the real thing. Polished language and emotional authenticity are no longer signals of credibility—they are features of automated manipulation. The same applies to product demos, influencer shout-outs, and unboxing. Many are synthetic, some malicious, and most are difficult to trace.

Copyright Theft: Training AI on Your IP

The implications of AI misuse extend far beyond fake listings or fraudulent clicks.

In a watershed moment for intellectual property rights, Disney and NBCUniversal have filed lawsuits against the image-generation company, MidJourney, accusing it of training its models on copyrighted material—including characters and creative assets— obtaining the necessary permissions. Legal action underscores a broader concern: the very systems generating synthetic content may be built on your brand’s identity, absorbed without notice or consent.

A Broader Industry Wake-Up Call

If your brand lives online—through product pages, social media, videos, or customer reviews—you’re already part of the AI-driven digital battlefield, whether you like it or not.

This isn’t just a tech or retail problem—it’s a universal threat to anyone who relies on digital engagement. For instance, streaming platforms are plagued by manipulated metrics that inflate visibility; retailers are under siege from cloned storefronts; and pharmaceutical companies are forced to combat counterfeit drugs sold online and fabricated digital prescriptions.

Rethinking the Threat: A New Mental Model

Effective brand protection requires expanding the definition of risk to include all AI-visible data points, recognizing subtle AI-generated patterns, and prioritizing real-time detection and response.

1. AI-Visible Surfaces

Brands must consider that risk extends beyond customer-facing content to include all AI-visible data points such as alt-text, metadata, structured data, and even the tone of product descriptions, recognizing subtle AI-generated patterns, and prioritizing real-time detection and response.

2. Synthetic Credibility

Additionally, brand protection teams must tune their detection systems to identify the right signals. They need to identify AI fingerprints, such as repeated phrasing across unrelated reviews or a uniform tone, which only emerge through aggregate behavioral analysis.

3. Silent Signals

Furthermore, response time matters more than ever. AI-driven fraud escalates rapidly, making automated lead prioritization and cross-platform correlation essential to stop abuse before it spreads.

At Hubstream, we’ve built our platform around that very idea: that modern fraud can’t be spotted with outdated methods. We help teams uncover hidden relationships between sellers, campaigns, and content and prioritize critical infringement activities automatically, giving brand protection experts the clarity they need to act before the damage is done.

Want to see how it works in action? Let’s talk.