3 Myths About Deepfakes That Are Putting Your Company at Risk

With generative AI rapidly improving, and only 24.5% of people able to identify high-quality synthetic media, it is becoming almost impossible to reliably detect deepfakes with the human eye alone. At the same time, there were an estimated $410 million in financial losses from deepfake-related fraud in Q2 of 2025. While 1 in 4 leaders are still unaware of deepfakes, many others assume their organization is too small, too niche, or too well-run to be targeted.

The reality is the potential cost of AI-driven fraud is far greater than the cost of taking early action to protect your organization.

Here are three common myths about deepfakes that leave companies vulnerable to AI-powered threats.

1. Myth #1 ‘’I’d be able to tell that the caller is impersonating my boss.’’

An executive at Ferrari most likely thought the same until they received a call from Ferrari’s CEO, Benedetto Vigna. The caller had Vigna’s perfect Southern Italian accent and almost convinced the executive. The only reason the fraud failed was because the executive asked a question only the real Vigna could have answered correctly.

Today, it’s possible to generate convincing audio from just a few seconds of someone’s voice. Accents and speech patterns can be mimicked with uncanny accuracy. Video conference calls can be spoofed in real time, complete with realistic facial movements and blinking.

Combine that with an urgent tone (“This can’t wait,” “We’ll miss the deal,” “Do this now”) and even experienced professionals can have their critical thinking short-circuited. It might be almost impossible to tell that it’s not your boss asking you to transfer $25 million on a video call, exactly what happened to a finance worker in Hong Kong last year.

2. Myth #2 ‘’We’re a small business. Hackers won’t target us.’’

This is a dangerous misconception that creates huge cybersecurity gaps. Small businesses are prime targets for cyberattacks.

46% of all cyberattacks impact businesses with fewer than 1,000 employees. Smaller organizations often lack enterprise-grade protection, have limited budgets and in-house expertise. That makes them easier, not harder, to attack.

Skipping investments in deepfake and fraud detection might look like smart budgeting in the short term but in 2025, small businesses can expect to pay anywhere from $120,000 to $1.24 million to respond to and resolve a single data breach.

Deepfake-enabled fraud doesn’t just hit global banks and Fortune 500 companies. It targets whoever has money, authority, or access, and weak defenses.

3. Myth #3 ‘’My team and I can differentiate deepfakes just by looking at them.”

Eight in ten people can’t tell real media from synthetic media. With the introduction of powerful tools like Nano Banana and Gemini, we’re exposed to nearly perfect synthetic images, videos, and audio every day, often without realizing it.

At the same time, deepfake attacks have surged 2,137% in the last three years. That scale alone makes it neither efficient nor safe to rely on manual, human-only review of large volumes of potentially malicious content.

Will Smith’s famous spaghetti deepfake video is a great example of how fast this technology is evolving. In 2023, it was clearly fake and almost cartoonish. Today, similar synthetic media can look incredibly realistic, especially when seen quickly on a small mobile screen or inside a busy inbox.

Relying on “we’ll just spot it if it looks weird” is no longer a defence strategy, it’s a liability.

4. What safety measures can be taken?

When you have the right safety measures in place, deepfakes are not something to panic about, they are something to prepare for.

Start by:

• Assessing your current security stack: Are your fraud detection, identity verification, and security operations tools capable of handling AI-generated media?
• Raising awareness among leaders and teams: Make sure executives, finance, HR, security, and operations understand what deepfakes are and how they’re used.
• Defining clear response playbooks: What should employees do if they suspect a deepfake call, email, or video? Who do they escalate to?
• Augmenting human review with AI-native detection tools: Humans are still critical but they need support from systems built specifically to analyse synthetic media.

Your existing security systems may not be as advanced as today’s generative AI. That’s why it’s essential to augment them, not just rely on legacy controls.

5. Where Cyberette Comes In

As mentioned above, the idea that humans can reliably detect deepfakes with the naked eye is now outdated. Today’s generative AI tools can produce images, videos, and audio that:

• Match accents and speech patterns,
• Reproduce subtle facial expressions and blinking, and
• Synchronize mouth movements and audio with impressive accuracy.

To keep up, organizations need forensic-grade fraud detection.

Cyberette enhances your security posture with an AI-native platform specifically designed to catch AI threats your team can’t see. Cyberette combines six complementary detection methods so no threat goes unchecked:

• Landmarking Analysis
• Cross-Modality Checks
• Behavioral Signal Analysis
• Artifact-Based Detection
• Provenance Analysis
• Pattern-Based Detection

The platform doesn’t just say “real” or “fake”, it provides explainable results that support the decision-making of:

• Threat intelligence experts
• Defence and security teams
• Law enforcement and investigative units

With Cyberette, your organization can move from reactive guesswork to proactive, evidence-based detection of AI-powered threats.

📩 Have questions? Reach out at info@cyberette.ai