Deepfakes Rising: Why Security Awareness Training is Critical

Deepfakes aren’t going away; they're evolving rapidly, going from niche internet curiosity to significant cybersecurity threat faced by everyone globally. Incidents involving artificial intelligence (AI)-generated synthetic media have increased exponentially, posing new challenges for organizations and their employees.

Created using powerful AI techniques, these sophisticated fakes convincingly manipulate images, videos, and audio recordings to impersonate real individuals, often executives or trusted colleagues.

The Escalating Threat: Attacks & Growth Trajectory

The use of deepfake technology in malicious activities saw a dramatic rise leading into the end of 2024. No longer just tools for political disinformation or online parody, deepfakes have become increasingly common weapons for AI phishing attacks to carry out financial fraud, corporate espionage, and other social engineering schemes.

Reports indicate staggering increases in deepfake attacks, with one analysis suggesting detected deepfakes increased 1,000% in 2023 across industries. The explosion was felt heavily in sectors like cryptocurrency and fintech, but the threat rapidly expanded to impact nearly all types of businesses.

Real-world examples illustrate the danger vividly. Arup, a global engineering firm, lost $25 million after a finance employee was duped into transferring money to an attacker disguised as a deepfake of the organization’s chief financial officer during a video conference.

The trend is undeniable, and the financial impact is clear, with research suggesting the average cost of deepfake fraud incidents has started exceeding $450,000. It’s a rapid escalation that underscores the need for robust detection and mitigation strategies exceeding today’s cybersecurity measures.

Understanding the Technology Behind the Threat

Deepfakes are the product of state-of-the-art AI and deep learning techniques, primarily neural network architectures like a generative adversarial network (GAN).

Here’s how a deepfake is made:

1. Data Collection: Creating a convincing deepfake requires a substantial dataset — think images, video footage, and audio clips — of the target individual. The more high-quality data available, which is often scraped from open-source intelligence (OSINT) such as social media and company websites, the more realistic the fake.
2. Training the AI Model: Data is fed into the neural network, and in a GAN setup, for example, a ‘generator’ network tries to create fake content, while a ‘discriminator’ network tries to tell the fake from the real training data. In competing against each other, the generator becomes progressively better at achieving hyper-realism.
3. Generating the Deepfake: Once trained, the model synthesizes the new content. This might involve swapping one person’s face onto another person’s body in a video, altering lip movements to match new audio, changing facial expressions, or creating entirely new audio in the target’s voice saying specific phrases.
4. Post-Processing: The raw AI output is refined, which involves adjusting elements like lighting, color grading, background blending, and smoothing transitions to enhance realism.

A lot goes into the development of a deepfake, but it’s becoming simpler to do for attackers without any technical skills.

Why Deepfakes Became Easier (& Cheaper) to Create

Today, it’s not exactly an insurmountable challenge to create a deepfake. Attackers now develop and deploy phishing attacks with manipulated images, videos, and audio recordings at an alarming pace.

Several converging factors have made deepfake creation significantly more accessible:

• Rapid AI Advancements: The underlying AI and machine learning algorithms have improved dramatically, requiring less data and expertise to produce convincing results compared to just a year or two ago.
• User-Friendly Tools: Software tools, some of which are open-source, like DeepFaceLab, became more powerful yet more straightforward to use, removing much of the deep technical complexity.
• Increased Computing Power: Generating deepfakes, especially video, is intensive. However, access to powerful graphics processing units (GPUs), including via cloud services, has become more affordable, reducing rendering times. What once took days? Now, just moments.
• Mobile Accessibility: Deepfake apps like Zao brought face-swapping capabilities to smartphones, normalizing the concept and demonstrating the ease with which appearances could be manipulated.

Even new or inexperienced threat actors can get started quickly now that deepfakes are easier and cheaper than ever to create.

So with the democratization of powerful AI tools, it was inevitable that there would be an increase in the potential for misuse across various domains:

• Disinformation & Propaganda: Spreading false narratives, manipulating public opinion, or discrediting individuals or organizations.
• Financial Fraud & Impersonation: Tricking employees or customers into authorizing payments, revealing account details, or bypassing security checks via executive impersonation.
• Blackmail, Harassment, and Reputational Damage: Creating fake compromising images or videos to extort victims or damage personal or corporate reputations.

With the proliferation of access to AI, it’s no surprise that headlines keep popping up about deepfake attacks.

Protecting Your Organization: The Role of Security Awareness Training

Because deepfakes directly target human perception and trust, technical defenses alone are insufficient. Building resilience requires a strong focus on the human element, spearheaded by next-generation security awareness training.

Here’s how specialized training helps protect against deepfakes.

Personalized training focused on deepfakes

Generic training won’t cut it anymore. Adaptive Security’s platform empowers organizations with tailored programs that educate employees about the risks associated with deepfakes and equip them with the skills to recognize manipulated media.

Personalized training includes:

• Understanding the Threat: Educating employees on how deepfakes are made and how they’re used in phishing attacks targeting businesses, such as vishing, fake video calls, or business email compromise (BEC).
• Detection Skills: Training employees to look and listen for subtle giveaways common in deepfakes, though acknowledging perfection is impossible. This might include unnatural facial movements, inconsistent lighting, poor lip-sync, or lack of emotion.
• Verification Protocols: Emphasizing the importance of never relying on video or voice alone for sensitive actions. Security awareness training reinforces mandatory procedures for multi-channel verification using trusted communication methods.
• Role-Based Training: Delivering customized content based on roles ensures relevance.
• Phishing Simulations: Utilizing cutting-edge phishing simulations to mimic executive team members in tests, allowing employees to practice identifying and responding to sophisticated attacks in a controlled environment, learning from mistakes without real-world consequences.

One-size-fits-all approaches for security awareness training, offered by legacy solutions, fall short to the point that they do more harm than good. Organizations need a next-generation platform like Adaptive Security to truly understand what they’re up against and strengthen security posture effectively.

Continuous and automated training delivery

Deepfakes evolve constantly, so security awareness training should be continuous, with programs running in the background and minimal administrative intervention necessary. It ensures ongoing reinforcement and keeps employees updated on the latest deepfake tactics and defense best practices, freeing up IT and security teams for other priorities.

Mobile-friendly and accessible learning

To maximize engagement and completion rates, security awareness training should be available on mobile devices. Employees access vital training modules conveniently on any device anytime, meaning they’re much more likely to complete modules.

Strengthening Security Posture in the Deepfake Era

The rise of sophisticated deepfakes represents an inflection point in cybersecurity threats. As demonstrated by real-world incidents in recent years, AI’s ability to mimic trusted individuals poses profound risks, enabling fraud and manipulation on an unprecedented scale.

Mitigating this threat demands a holistic approach, and while technology for detecting deepfakes will improve, it can’t be the sole reliance. Organizations need strong internal processes mandating multi-channel verification for sensitive actions, coupled with a workforce educated and reminded about the dangers.

Investing in continuous, targeted security awareness training that includes deepfake scenarios is critical to strengthening security posture and navigating this challenging new era of AI-driven deception.