AI Misinformation: A Business Guide to Spotting and Responding to AI-Generated False Content

A deepfake video of your CEO announces your company is being investigated for fraud. Employees are already forwarding it, shareholders are calling their brokers, and your stock is moving—all before your team has even seen it. This is not the plot of a Hollywood movie. It’s the kind of incident security and crisis communication teams are already navigating.

False information has always existed. What’s changed is the barrier to entry. Now that anyone can use ChatGPT or similar generative AI tools, it’s faster, cheaper, and easier to produce convincing synthetic content at scale—and harder for the people encountering it to question what they’re seeing.

This guide covers what AI misinformation is, how it’s changing the global risk landscape, how to detect it, and what to do when it hits.

What Is AI Misinformation?

AI misinformation is false or misleading information created, altered, or spread with the help of AI, such as large language models (LLMs) and image generation tools. Fake content often includes text, images, audio, or video, and is becoming increasingly difficult to distinguish from authentic content.

Misinformation, disinformation, and malinformation are related but distinct threats. Understanding the difference shapes how you respond.

• Misinformation: False or misleading information, usually shared without intent to harm

(Example: Someone shares a fake news story in an internal Slack channel because they believe it.)

• Disinformation: False information deliberately created and spread to deceive or cause harm

(Example: A fabricated press release attributed to your company circulates online, triggering a flurry of media inquiries before your comms team has even seen it.)

• Malinformation: Genuine information that is true but used maliciously

(Example: Leaked private data is made public to embarrass or threaten a high-profile individual.)

AI can be used for all three, but it’s especially powerful for amplifying disinformation and making it look credible.

Why AI Misinformation Is a Real-World Business Problem

Total financial losses from deepfake-enabled fraud exceeded $200 million in the first quarter of 2025 alone. The AI systems driving that damage are accessible, inexpensive, and they’re improving every day. As automation and the use of AI become more widespread, the risk expands—and the business consequences are already documented across industries.

Reputational damage and brand impersonation

AI-powered misinformation, such as a deepfake video or fabricated article, can make your organization look reckless or fraudulent before your comms team has had a chance to respond. At that point, the damage isn’t just to your brand—it’s to the credibility of every official statement you make afterward.

Correcting the record is time-consuming and expensive. According to Regula’s Deepfake Trends report, businesses across industries incurred an average loss of nearly $450,000 per deepfake incident.

Financial fraud and social engineering

In 2024, a finance employee at engineering firm Arup authorized a series of wire transfers totaling approximately US$25 million after joining what appeared to be a legitimate video call with senior leadership. Every person on that call was a deepfake. The fraud wasn’t discovered until the employee followed up with Arup’s actual headquarters afterward.

Incidents like this are why a business impact analysis should account for AI-driven fraud scenarios, not just operational outages.

How to Spot AI-Generated Content

We’ve come a long way since the “Will Smith eating spaghetti” era of obviously glitchy AI video—and AI-generated content will only get harder to spot. There’s no single test that catches everything, and the tells below will evolve (treat them as a starting point, not a permanent checklist). But a working familiarity with the red flags across content types serves an important function: slowing down reaction long enough to verify before you act.

Much of this content first surfaces and spreads on social media platforms—making social media threat monitoring an important early warning layer for security and comms teams.

Visual red flags in deepfake images and video

AI image and video generation has improved dramatically, but telltale signs remain—especially under scrutiny:

• Blurry or unnatural edges around faces, hair, or background objects
• Inconsistent lighting or shadows between the subject and the background
• Distorted anatomy, such as extra fingers, misaligned teeth, warped lips, or mismatched eyes
• Stiff or jerky movement, unnatural blinking, or lip-sync that doesn’t align with the audio

When evaluating video, slow it down. Artifacts that pass at normal speed often become obvious at half speed or when paused.

Synthetic text patterns worth knowing

Surely you’ve seen the LinkedIn posts calling out em dashes or the rule of three as markers of AI-generated text. The real signals go deeper. AI-generated text is harder to flag by feel alone, but patterns emerge once you know what to look for:

• Overly smooth, generic tone—technically fluent but lacking a real point of view
• Repetitive phrasing, odd conjunctions, or vague sentences that seem confident but don’t say much
• Fact-like claims with no sourcing, or details (dates, names, statistics) that are confidently wrong
• Subtle stylistic ticks that don’t match the real person’s usual writing, like a specific word or punctuation pattern used repeatedly

What matters more than any single quirk is the overall pattern. Does the writing feel like a specific person wrote it, or does it feel like the average of a thousand people did?

What to listen for in voice clones and audio spoofs

Voice cloning tools are now very accessible and increasingly realistic.

Potential red flags include:

• Unnatural prosody, which could mean a too-steady rhythm, missing breath sounds, or robotic pauses between words
• Emotional mismatch—for example, a calm, flat delivery on content that should carry urgency or stress
• Subtle distortions in tone, pitch, or background noise that feel slightly too clean or artificially cut

Trust your instincts here. If something sounds “off” and you can’t place why, it’s worth a second look before you forward or act on it.

Verify Before You Respond: How and Why

Spotting the red flags buys you time, and what you do with that time matters.

In an active incident, the pressure to respond fast is real. But a response built on unverified information can make a situation harder to manage, not easier. A false escalation, a premature denial, or an internal alert based on fabricated content all have real operational costs.

Think of verification less as a fact-checking exercise and more as a short, repeatable protocol your teams run before they escalate, publish, brief leadership, or respond publicly.

Stop and SIFT

The SIFT method is a practical four-step filter worth building into your incident workflows. It’s not designed to be exhaustive, but rather fast enough that people will actually use it under pressure.

• Stop. If a piece of content triggers urgency or alarm, that’s your signal to pause, not react. Ask: Do we know this source? Have we seen this claim from anyone we trust?
• Investigate the source. Look at who is posting or sending the content—techniques from open source intelligence analysis can help your security team go deeper when a source looks suspicious.
• Find better coverage. Search for the same claim from sources your organization already treats as reliable—like industry associations, regulators, and other official channels. If no credible source is confirming it, treat it as unverified until one does.
• Trace to the original. Follow the chain back to the source document, video, press release, or dataset. This step catches the most common manipulation tactic: real content stripped of context, clipped, or reframed to mean something it didn’t.

SIFT has practical benefits across the entire org. For security and business continuity teams, SIFT works well as an intake filter for screenshots, forwarded messages, and viral posts about outages, breaches, or safety incidents. For comms, it functions as a gate before any external statement goes out. For legal and risk, it creates a documented verification trail—clear evidence that your team exercised reasonable diligence before acting.

Use reverse search for visual content

For images and video, reverse search is your fastest verification tool. Upload a frame or image to a reverse video search tool, and it will show you where that content has appeared online and when. This often reveals whether a supposedly breaking image is actually recycled footage from years ago, or whether a “screenshot” has been edited.

Give comms and security teams access to at least one approved reverse video search tool, and write it into your crisis management plan as a standard step for any suspicious visual.

Build your trusted source network before you need it

When something breaks, you shouldn’t be deciding in the moment who to believe. A pre-agreed, cross-functional list of trusted sources—such as regulators, key industry bodies, credible media outlets, law enforcement contacts, and open source intelligence partners—gives your teams a foundation they can lean on under pressure.

“It’s particularly important for all of us to have our trusted sources of information…like security, corporate comms, business continuity teams,” noted Pratley. With this approach, “there’s an expectation set really early for where information is going to come from.”

Align on this list across security, PR, legal, and business continuity, and make sure it’s specific. Instead of “check reputable sources,” lean more towards, “For claims about X type of incident, check A, B, and C before escalation.”

How to Respond to AI When AI Misinformation Strikes

How well your organization handles AI-generated misinformation depends largely on decisions made before an incident occurs—who owns the response, what gets communicated and when, and how quickly your teams can move without improvising under pressure.