55% of Companies Say They Can’t Verify If AI-Generated Data is Trustworthy—That’s a Huge Risk

We’re Entering the AI Misinformation Era—and Most Companies Aren’t Ready

AI is helping businesses scale faster than ever. But it’s also scaling confusion, misdirection, and synthetic content at a pace that’s left traditional governance models in the dust.

According to a 2024 Forrester survey, 55% of organizations say they are unable to verify whether AI-generated data in their systems is accurate, reliable, or complete. In other words: more than half of companies don’t know if the data they’re making decisions on is real, valid, or manipulated.

In a world increasingly dependent on automation, that’s not a glitch—it’s a systemic risk.

What Does It Mean When You Can’t Trust the Data?

AI tools are being deployed across industries for content creation, customer support, analytics, decision-making, and even coding. But the challenge is that generative models are not fact engines—they are pattern engines. They reflect what they’ve seen, not what is true. They hallucinate. They invent. And when unchecked, they insert plausible-looking falsehoods into emails, reports, dashboards, codebases, and customer interactions.

Now imagine your sales forecasts, compliance summaries, or customer records include that kind of content—and no one knows it.

Worse, imagine employees using AI tools to assist with contracts, pricing, or legal summaries. Who owns the liability if an AI writes the wrong clause, or misquotes a regulation?

The Rise of Synthetic Content and Fake Identities

This goes beyond hallucinations. We’re also seeing:

• Synthetic identities used in fraud, hiring scams, and account takeovers

• Deepfake videos impersonating executives to initiate wire transfers or approve access

• Auto-generated phishing content crafted in perfect corporate tone

As tools like ChatGPT, Gemini, and open-source LLMs become ubiquitous, the flood of “close enough to real” information becomes harder to filter. Misinformation is no longer just a public issue—it’s a business risk.

Why Is This Happening?

1. AI integration is outpacing governance.
   Most companies adopted AI tools before they had guardrails in place. Shadow AI use is rampant, and centralized oversight is rare.

2. Most teams don’t know what data is real anymore.
   Without digital provenance, watermarking, or validation protocols, it's hard to distinguish AI-generated content from human-created records—especially at scale.

3. Security and compliance leaders weren’t trained for this.
   Traditional GRC programs were designed around structured data and known inputs. AI throws probabilistic, opaque systems into the mix—and that changes everything.

The Governance Crisis No One Is Talking About

What’s at stake?

• Legal exposure: If AI generates false information that leads to financial or legal consequences, who’s accountable?

• Brand reputation: One hallucinated public-facing document or post can undermine years of trust.

• Compliance violations: Especially in regulated industries, AI misuse can breach standards before leadership even realizes the tool is in use.

From GDPR to SEC cyber disclosure rules, the question isn’t just what AI is doing—it’s whether you can prove it’s doing it safely.

What to Do Now: Build AI Data Trust Layers

Here’s what forward-thinking organizations are doing:

1. Conducting an AI audit – Where is AI used across the org? Who is using it, and for what?
2. Implementing watermarking and digital provenance – Especially for internal comms, financials, and customer-facing content
3. Training teams on AI risk awareness – Not just developers, but every employee who might copy/paste or generate content
4. Creating centralized AI usage policies – With pathways for review, oversight, and exception handling
5. Pairing AI governance with human risk management – Because it’s not just the tool—it’s who’s using it, and why

Final Thought: If You Can’t Verify It, You Can’t Trust It

You don’t need to stop using AI. You need to start understanding what it’s really doing—and how risky that is.

If your organization is relying on automation, you need confidence in your data, not just your tools. And that means combining technical controls with human insight—fast.

We help organizations build the governance, awareness, and cultural maturity to operate safely in the AI era. Let us know if you’d like help sorting signal from synthetic.