Deepfake artificial intelligence has transformed from a research curiosity into a commoditized weapon that requires almost no technical skill to deploy. A single photograph and a few seconds of audio are now sufficient for cybercriminals to generate convincing video impersonations capable of authorizing multimillion-dollar wire transfers. This shift represents a fundamental change in how organizations must defend against fraud, moving beyond traditional verification instincts that employees have relied on for decades.

How Did Deepfakes Become So Easy to Use?

The barrier to entry for deepfake creation has collapsed dramatically over the past few years. Four technical breakthroughs have made this possible. Early deepfake models required thousands of labeled images to produce convincing results, creating a practical barrier for most attackers. Modern tools have eliminated that requirement entirely. According to Sumsub's Identity Fraud Report 2025-2026, sophisticated fraud combining synthetic identities, deepfakes, and layered social engineering rose 180 percent globally in 2025 as stronger verification controls rendered simple tactics ineffective.

The most significant shift is the emergence of deepfake-as-a-service platforms. Criminal marketplaces now sell deepfake generation as a subscription service with point-and-click interfaces that require no machine learning knowledge. A cyberattacker can upload a target's photo, paste in an audio sample pulled from a public source like an earnings call or LinkedIn video, and receive a finished synthetic video within minutes. This commoditization is the critical turning point. Deepfake artificial intelligence is no longer the domain of nation-state actors or well-resourced criminal organizations; any motivated cyberattacker with a modest budget can deploy it.

What Makes Video Deepfakes the Most Dangerous Format?

Deepfake technology spans four distinct media categories: video, audio, images, and text. Each exploits a different trust mechanism and creates a different category of organizational risk. Video deepfakes are the costliest format because they combine visual and behavioral familiarity in a single channel, making them extraordinarily convincing.

The clearest proof of this risk came in early 2024, when a finance employee at engineering firm Arup joined a video conference where every participant was a deepfake, including a synthetic replica of the company's chief financial officer. The employee transferred 25 million dollars before anyone detected the fraud. Full-body puppeteering, where artificial intelligence animates an entire physical persona rather than only a face, extends this risk beyond video calls into recorded executive communications.

Voice cloning artificial intelligence generates synthetic audio from as little as a few seconds of sampled speech. The technology can replicate tone, accent, speaking style, and emotional expression with alarming fidelity. This creates powerful opportunities for legitimate applications like helping individuals with speech disabilities communicate more effectively, but it also opens serious risks for voice phishing, identity impersonation, and social engineering attacks.

How to Defend Your Organization Against Deepfake Fraud

• Implement Behavioral Detection Controls: Train employees to recognize inconsistencies in communication patterns, unusual requests that deviate from normal procedures, and behavioral signals that may indicate a deepfake. Employees should verify high-value requests through secondary channels before authorizing transfers.
• Deploy Procedural Safeguards: Establish multi-step verification processes for financial transactions that require in-person confirmation or calls to verified phone numbers. No single communication channel should be sufficient to authorize large transfers, regardless of how convincing the deepfake appears.
• Conduct Regular Deepfake Simulation Training: Organizations should expose employees to realistic deepfake scenarios in controlled environments before real incidents occur. This inoculation approach helps employees recognize the specific formats and techniques that criminals actually deploy in the field.
• Use Technical Detection Tools: Implement facial inconsistency analysis, audio pattern analysis, metadata verification, and behavioral detection systems. Researchers are developing artificial intelligence-based forensic analysis tools to identify synthetic content, though these tools are in a constant arms race with improving deepfake technology.
• Explore Emerging Authentication Methods: Some organizations are exploring digital watermarking systems that embed authenticity markers into digital content. Blockchain technology may help track digital content authenticity through tamper-resistant records and source verification.

Understanding the distinction between deepfakes and adjacent terms is critical for effective defense. Deepfakes are specifically synthetic media that impersonates a real, identifiable person without consent. Synthetic media is the broader category and includes artificial intelligence-generated images, text, avatars, and audio that may not target any real individual. Shallowfakes require no artificial intelligence at all; a video slowed to misrepresent context is a shallowfake rather than a deepfake. Conflating these terms creates dangerous blind spots in security programs.

Why Government and Industry Must Share Threat Intelligence

The scale of artificial intelligence-powered cyber threats has grown so rapidly that fragmented defense is insufficient. Nearly 52 percent of organizations in Vietnam encountered artificial intelligence-supported cyber threats in the past year, with over half reporting a twofold increase in such incidents and 36 percent seeing a threefold surge. An average of 36,000 vulnerability scans occur every second, with attempted intrusions reaching 97 billion in just the latter half of 2024 according to IDC reports.

Governments possess unique intelligence capabilities and insights into national security priorities. Industries are at the forefront of technological innovation and understand emerging attack vectors. Combining these strengths through robust threat intelligence sharing creates a powerful synergy that allows for a more comprehensive understanding of the threat landscape and enables proactive defense strategies.

"Cybercriminals increasingly use artificial intelligence to scan for vulnerabilities, which allows them to launch more refined and larger-scale attacks," noted Nguyen Gia Duc, Country Director for Fortinet Vietnam.

Nguyen Gia Duc, Country Director for Fortinet Vietnam

Effective intelligence sharing requires structured approaches. Governments and industries can implement secure information sharing platforms that allow for real-time exchange of threat indicators and attack methodologies. Joint working groups and task forces between government experts and industry leaders foster trust and shared understanding. Standardized reporting formats for cyber incidents streamline analysis and dissemination, reducing ambiguity and improving response times. Public-private partnerships can institutionalize cooperation and provide frameworks for joint research and development in artificial intelligence cybersecurity.

The Erosion of Digital Trust in the AI Era

One of the biggest dangers of deepfakes is the erosion of trust in digital content itself. For decades, people trusted photos, videos, and audio recordings as reliable evidence of reality. Images captured moments, videos documented events, and audio recordings preserved conversations. Digital media became one of the strongest forms of proof in modern society. But artificial intelligence is changing everything.

If people can no longer trust videos, audio, or images, society may enter a crisis of digital verification. Digital information may become increasingly difficult to verify. Deepfakes also create a dangerous phenomenon called the "liar's dividend," where people accused of wrongdoing may claim authentic evidence is fake. This can undermine accountability and public trust in institutions.

Journalists increasingly face challenges verifying digital content and authenticating media from reliable sources. Trustworthy journalism is becoming more important than ever as synthetic content becomes more realistic. Constant exposure to manipulated content may increase distrust, confusion, anxiety, and information fatigue. False information may influence elections and public opinion, threatening democratic systems that depend heavily on trusted information.

The challenge ahead is not simply technological. It requires a unified approach combining artificial intelligence-powered detection tools, blockchain-based authentication systems, regulatory frameworks that balance innovation with security, and a cultural shift toward verification practices that do not rely solely on visual or audio evidence. Organizations that invest in deepfake awareness training and procedural safeguards today will be far better positioned to protect themselves as this technology continues to evolve.