AI-generated deepfake technology has evolved from a novelty to a destructive tool in the hands of cybercriminals. Once limited to political disinformation and celebrity impersonations, deepfake deception has now become a serious and highly plausible threat to businesses worldwide, not just a rare media headline. With the power to undermine operations, cripple security strategies, and jeopardise high-stakes communications, deepfakes pose an urgent risk to your bottom line and reputation. It’s a stark reminder of AI’s growing influence shaping cybersecurity, and why businesses must look beyond contingency plans and focus on securing the very core of their digital infrastructure to stay ahead of this rapidly advancing threat.
What’s real, what’s not, and what it means for businesses
Deepfake technology has found its way into popular culture in all sorts of ways. While some AI-generated digital avatars have been used to spread joy in the form of four Super Troupers, others have been leveraged for malicious purposes –- impersonating political figures and facilitating large-scale financial fraud. One of the most alarming real-world examples involved a bank manager being tricked into transferring £20 million after receiving a video call from someone he believed to be a company executive. The fraudster used an AI-generated video call and supporting fraudulent emails and documents, highlighting the increasing sophistication and accessibility of deepfake technology.
The tools to create these manipulated media are now so widely available, businesses cannot afford to consider them hypothetical threats. The legal and compliance consequences of ignoring this growing risk are also severe. Under regulations like the EU’s Digital Operational Resilience Act (DORA), businesses must maintain operational resilience and safeguard sensitive data. The regulatory landscape is evolving, with the Danish government recently tightening copyright laws to protect individuals’ body, facial features, and voice from AI-generated deepfakes.
Failure to safeguard against deepfake-driven fraud could result in penalties, legal repercussions, and lasting reputational damage. It’s essential for organisations to integrate deepfakes into their broader cybersecurity and risk management strategies. Collaborating with legal teams to align incident response plans and processes with relevant regulations can help mitigate the risk of liability and ensure businesses are prepared if an attack occurs.
Zero Trust: a thing of the past?
The rise of deepfakes has shattered visual and auditory trust, as video and audio can now be easily manipulated. Traditional trust models are no longer enough, making it essential for businesses to update their security measures and verify content authenticity.
The Zero Trust security model, which operates on the principle of “never trust, always verify,” is a critical layer in defending against evolving threats. But it’s also an example of traditional frameworks focused on software alone. The shortfall? Relying on manual updates and human intervention – putting too much pressure on employees to spot and respond to threats. In an AI-driven landscape designed to deceive the human eye, this strategy is highly susceptible to human error, which continues to be the leading cause of breaches.
Companies should implement procedures to verify the authenticity of video, audio, or image-based requests to protect against suspicious communication. If actions like transferring funds or granting access to sensitive information are involved, secondary verification steps, such as follow-up calls or additional approvals, should be triggered.
To further mitigate deepfake fraud, leaders must implement policies for verification, detection, and escalation. Any request involving sensitive data, such as financial approvals or credential requests, should undergo additional verification. Policies must also ensure all employees receive regular training to spot red flags of deepfake attacks and use available tools to verify content authenticity. Fostering a culture of scepticism—especially when something seems urgent or unusual—will be key.
However, for a truly proactive approach, organisations must adopt a multi-layered security model that integrates content authenticity checks across every layer of their security infrastructure.
A new layer of trust
Incorporating security at the hardware level, working seamlessly alongside software defences, creates a strong foundation for verifying content authenticity and reducing the risk of deepfake manipulations slipping through the cracks.
AI plays a dual role in the deepfake dilemma. Advances in machine learning, particularly multi-modal AI systems, have led to the development of powerful tools capable of detecting the subtle anomalies in audio-visual content like unnatural blinking, or mismatched audio elements, so elusive they deceive the naked eye. These tools use techniques such as Convolutional Neural Networks (CNNs) to identify minute details in images, and Long Short-Term Memory (LSTM) networks, along with Gated Recurrent Units (GRUs), to track audio-visual syncing. By integrating these AI-driven capabilities with hardware-verified content checks, organisations can ensure that every piece of digital communication is genuine before it’s ever trusted.
Picking the perfect defence
To select the right solution, businesses must prioritise three core considerations. Firstly, it must be genuinely zero trust, application-agnostic. This will mean the solution must have the power to detect deepfakes in real-time and be compatible with your business applications (such as Zoom, Teams, Webex, Chrome, Meta and YouTube). Secondly, focus on ease of adoption, ensuring seamless deployment with flexible options that can scale to meet diverse enterprise needs. Lastly, the solution must provide protection at every endpoint, including options like lightweight software agents on personal computers and laptops, or packaged with secure SSDs to form a unified defence layer.
Whether its defending against deepfakes, data breaches or ransomware, to secure organisations from malicious activity, businesses must adopt a secure-by-design approach. One where AI-driven security features are integrated at the hardware and endpoint level – expanding defences to establish a multilevel posture that monitors, flags and protects systems twenty-four-seven, even without the broader protection of a corporate network. The rise of AI-driven threats demands equally sophisticated defence mechanisms, making AI the cornerstone of a truly proactive cybersecurity strategy for the future.
