When Fiction Becomes Reality: The Rise of Deepfakes and Associated Risks

A version of this article was originally published on Live Insurance News.

As technology progresses, so do manipulation tactics. Enter deepfakes—a rising threat that uses digitally altered images, videos, and audio recordings to convincingly fabricate reality, making it difficult to distinguish fact from fiction. The rapid advancement of this technology has serious implications for privacy, security, and trust, especially for businesses navigating a high-stakes digital landscape.

What are Deepfakes and How Do They Work?

At the core of deepfakes is a technique called Generative Adversarial Networks (GANs), where two neural networks—one generating content and the other evaluating it—work together to create increasingly convincing fakes. These AI-generated media are trained on vast datasets to replicate facial movements, voice patterns, and behaviors with alarming accuracy.

Deepfakes can take the form of:

Video Deepfakes
Manipulate a person's face or voice in video to make it appear they are doing or saying something they never did. One notable example is when Arup lost $25 million in a deepfake Zoom scam.

Audio Deepfakes
AI voice cloning replicates a person’s speech patterns to create convincing but fake phone calls or messages. In 2024, a Maryland principal was falsely implicated in hate speech via a deepfake recording.

Image Deepfakes
Digitally altered photographs—often used for non-consensual pornography—have surged across social platforms, particularly affecting minors and school-age children.

The Growing Threat: How Deepfakes Are Used Maliciously

The impact of deepfake scams and manipulations is far-reaching. Common threats include:

• Misinformation and disinformation in media and politics
• Financial fraud and identity theft using fake voice or video to authorize transactions
• Cyberbullying and harassment with AI-generated explicit content
• Trust erosion in media and institutional communication

These threats directly impact public perception, organizational integrity, and even national security.

Risks Posed by Deepfakes to Businesses

For businesses, deepfake risks include reputational damage, internal fraud, and data security compromise. From impersonating executives in video meetings to falsifying audio for wire transfer approval, deepfake fraud is already generating costly incidents in corporate environments.

The deepfake impact on business also extends to cybersecurity posture, biometric verification reliability, and the effectiveness of traditional fraud detection systems.

Insurance Implications: Is Deepfake Fraud Covered?

Many businesses are asking whether losses caused by deepfake scams are covered under current policies. The answer depends on the type of coverage in place. Cyber liability insurance may respond to certain forms of deepfake fraud, particularly those involving social engineering or financial theft.

However, insurers are only beginning to adapt policies to reflect the rising threat of deepfake risks insurance. Policyholders must work with specialized brokers to clarify what is and isn’t covered and explore endorsements that address emerging tech threats.

Strategies for Detecting and Preventing Deepfake Attacks

Combating deepfakes requires an integrated approach:

Technological Solutions
AI-driven detection tools are in development by companies like Microsoft, Facebook, and institutions like DARPA. These tools scan for anomalies in lighting, facial movement, or audio sync.

Legal Measures
While legislation lags behind the technology, governments are introducing laws to criminalize malicious deepfake usage. The EU’s Digital Services Act (DSA) is an example of policy targeting platform accountability.

Public Education
Raising awareness and enhancing media literacy are key defenses. Organizations like First Draft are equipping people with skills to spot and question suspicious content.

The Future of Deepfake Technology and Security

As detection methods improve, so too do the tools for creating deepfakes. This constant technological tug-of-war will require increased investment in AI-based defense systems and updated insurance frameworks that account for these evolving threats.

Businesses must remain vigilant and forward-thinking, recognizing deepfake detection as a permanent component of digital risk management.

Protecting Your Organization

Safeguarding against deepfake risks requires a combination of employee training, multi-factor authentication protocols, strong internal controls, and strategic insurance planning.

Working with knowledgeable partners who understand insurance implications of deepfakes—and how to tailor cyber policies accordingly—can provide the resilience needed to face future threats with confidence.

‍

Contact Flow Specialty today to discuss how deepfake risks could impact your business—and how cyber insurance solutions can help you prepare, respond, and recover with confidence.

‍

Frequently Asked Questions (FAQ)

What exactly is a deepfake?

A deepfake is a hyper-realistic, AI-generated video, image, or audio file that makes it appear someone is saying or doing something they did not. It’s created using machine learning techniques, especially Generative Adversarial Networks (GANs).

How can deepfakes harm businesses?

Deepfakes can be used to impersonate executives, falsify transactions, leak fake statements to the media, or disrupt biometric security systems. These attacks can lead to financial loss, reputational damage, and internal confusion.

Are losses from deepfake scams covered by typical insurance?

Not always. While some cyber liability insurance policies may cover deepfake-related fraud, coverage varies widely. Businesses should work with a broker to assess whether their current policy addresses the full scope of deepfake risks insurance.

How can businesses protect themselves from deepfake threats?

Strategies include deploying AI-based detection tools, training staff to recognize deepfakes, implementing stricter financial verification protocols, and maintaining updated cyber policies that include social engineering and media manipulation risks.

Is it possible to detect deepfakes reliably?

Detection tools are improving, but deepfakes are also getting more sophisticated. Many can still be identified through inconsistencies in lighting, facial movement, audio sync, and contextual clues. However, vigilance and layered defense systems remain essential.

‍