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Criminals have always relied on one simple truth - it’s often easier to trick a person than to hack a system. Social engineering has long exploited human trust, urgency, and curiosity. But with AI‑powered deepfakes, that old playbook has been upgraded into something far more scalable, convincing, and dangerous.
Today, attackers can generate realistic voice, video, and chat content that looks and sounds like someone you know and trust. Executives, IT admins, customer support teams, and even family members are being impersonated in real time. The result is a new wave of fraud and account takeover that traditional security tools struggle to detect.
In this post, we’ll tackle how AI‑driven deepfakes are transforming social engineering, what this means for executives and brands, and why continuous monitoring across social media, the surface web, deep and dark web is a baseline requirement.
Phishing Emails Through to Fully Synthetic People
Classic social engineering is usually centered around simple phishing emails, and while those still exist, attackers now have access to:
- AI chatbots that can hold long, tailored conversations in email, chat, or DMs
- Voice cloning models that can mimic an executive or colleague from a short audio sample
- Video deepfakes that can place a target’s face onto another person or drive a “live” fake video call
This means the attacker isn’t just sending a suspicious link; they might be calling your finance team with a voice that sounds exactly like the CFO, urgently requesting a wire transfer, appearing on a video call looking like your IT admin and asking a user to share their MFA code “just this once,” or messaging employees on LinkedIn or Slack as a senior leader, pushing them to open a document or join a “confidential” project. In each case, the interaction feels familiar, urgent, and legitimate enough to bypass a user’s instinct to question it.
For the person on the other end, all the traditional cues they rely on, tone of voice, facial expressions, and writing style, can now be convincingly spoofed. That’s a serious problem when your security controls ultimately depend on humans making the right call, because the very signals they use to decide who to trust are being actively weaponized against them.
Why This Threat Is Exploding Now
Deepfake and generative AI technology have existed for a few years, but several industry and lifestyle changes have made them mainstream for attackers. Powerful AI models are now available as easy‑to‑use online tools or APIs, so you no longer need great technical skills to create a convincing deepfake.
Cost and speed have also changed the game. Cloning a voice or generating fake video can take minutes and cost a few dollars, or nothing at all, making it completely viable for mass campaigns instead of one‑off, high‑effort scams. At the same time, social media, podcasts, webinars, and online meetings give attackers an endless supply of high‑quality audio and video to train on.
Put together, the barrier to entry is essentially gone. A small criminal group, or even a lone actor, can now run complex, multi‑channel social engineering operations that previously would have required significant time, money, and manual effort. To put this in perspective, Regula, a digital forensics firm, reported that in 2024, every second business globally reported incidents of deepfake fraud.
Real‑World Scenarios: How Deepfake Social Engineering Plays Out
To understand the risk, it’s useful to walk through a few real-world scenarios:
Case study 1: Deepfake CFO video call scam (Hong Kong, 2024)
What happened:
A finance worker at multinational engineering firm Arup in Hong Kong was invited to a video conference that appeared to include the company’s CFO and several familiar colleagues. In reality, every “participant” on the call was a deepfake, created using AI to mimic their faces and voices in real time.
Result:
Because the people on the video call looked and sounded like genuine senior executives, the employee followed their instructions and executed 15 separate transfers to multiple local bank accounts over a short period. The fraud was only discovered later when the real executives were contacted and confirmed that they had never requested the payments.
Financial Implications:
The company lost around HK$200 million, roughly 25–35 million USD depending on the report, across those transfers, and authorities have indicated that recovering the funds will be extremely difficult. This single incident is now widely cited as a wake‑up call for finance teams about the risk of deepfake‑enabled executive impersonation.
Case study 2: Deepfake principal smear video (Maryland, USA, 2023–2024)
What Happened:
In Baltimore County, Maryland, a high school principal, Dr. Marcus Eiswert, was targeted with a deepfake audio and video clip that appeared to show him making racist, antisemitic, and other derogatory remarks about students and staff. The clip was created by the school’s athletic director using AI tools and then circulated widely on social media as if it were a secretly recorded rant.
Result:
The video went viral, triggering public outrage, protests, and widespread calls for the principal to be fired. Eiswert was placed on administrative leave while the district and law enforcement investigated, and even after investigators confirmed it was a deepfake and charged the athletic director, the damage to Eiswert’s reputation and personal safety continued, with ongoing online abuse and threats.
Financial And Real‑world Impact:
Beyond legal costs and investigative resources, the district faced significant reputational harm and community mistrust, and Eiswert ultimately felt compelled to leave and take a position at another school for his safety and well-being. While this case wasn’t about a wire transfer, it’s a powerful U.S. example of how deepfakes can cause severe real‑world and career damage, and it underscores that the financial and legal fallout from reputational attacks can be just as serious as direct payment fraud
Why Continuous Monitoring Is Essential
Existing security controls like email gateways, firewalls, and endpoint protection were not designed to catch subtle, human-focused manipulation. While these tools still have value, they are no longer sufficient as standalone solutions, and businesses need to think in terms of continuous digital risk monitoring.
Required Continuous Threat Monitoring Practices
Social Media Monitoring
Tracking for fake executive accounts, brand‑impersonation pages, and malicious ads that target your customers or employees. This includes major platforms as well as smaller, niche communities where scams often incubate.
Surface, Deep, And Dark Web Monitoring
Identifying where your brand, domains, or executive identities are being discussed, sold, or abused in criminal forums, marketplaces, and chat groups. Early visibility often means you can act before a campaign fully scales.
Identity Resolution
Correlating seemingly separate profiles, handles, and email addresses back to the same actor or campaign. Attackers rely on fragmentation, but good identity resolution reconnects the dots and reveals patterns behind the noise.
Synthetic Content Detection
Flagging suspicious profiles, images, or videos that exhibit signs of AI generation, unnatural artifacts, inconsistent activity patterns, or connections to known malicious infrastructure.
The goal is to build an early‑warning system that alerts you when your people, brand, or customers are being weaponized, so you can respond quickly and decisively.