Deepfake Technology Threats are no longer a problem for Hollywood editing suites or election-watchers alone. In 2024, a finance worker at the British engineering firm Arup joined what looked like a routine video call with his CFO and several colleagues, then authorised fifteen transfers totalling twenty-five million US dollars. Every person on that call was fake. The case, confirmed by Hong Kong police, marked the moment deepfake fraud moved from theoretical risk to boardroom reality.
This guide sets out what deepfake technology actually is, who is creating these attacks and why, the three main ways deepfake technology threats hit UK businesses and individuals, what current UK and EU law says about it, and the practical steps that reduce your exposure. We also cover why your antivirus software will not catch a live deepfake call, and answer the questions IT managers and finance directors ask most often.
Table of Contents
What Is Deepfake Technology?
Deepfake technology uses artificial intelligence to create video, audio, or images of a person doing or saying something they never did. The results are convincing enough to fool colleagues, journalists, and even close family members under time pressure.
Most deepfakes are built using a pair of competing neural networks called a Generative Adversarial Network, or GAN. One network, the generator, creates a fake image or clip. The other, the discriminator, tries to spot the fake. The two train against each other thousands of times until the generator produces output that the discriminator can no longer reliably flag. This is why deepfakes have become sharper and more convincing every year rather than plateauing at an early, easily spotted stage.
Voice cloning works on a similar principle, but needs far less source material. A few minutes of a person’s voice, lifted from a company podcast, a conference talk, or a LinkedIn video, is often enough to produce a convincing clone. If you want to understand the wider vocabulary around this kind of manipulation, our glossary of key cyber definitions explains related terms such as synthetic media and biometric spoofing in plain English.
Who Is Behind Deepfake Technology Threats?
Understanding the people and groups building these attacks helps explain why the problem has grown so quickly. It also clarifies why the response needed from businesses has changed too.
A decade ago, producing a convincing fake video required specialist skills, expensive hardware, and hours of manual editing. That barrier has largely disappeared. Deepfake-as-a-service platforms now let a buyer generate a cloned voice or a face-swapped video for a modest fee, often through the same kind of subscription model used for legitimate software. Open-source models, originally released for research purposes, have been repurposed by organised fraud groups who run these scams at volume rather than as one-off stunts.
This matters for two reasons. First, the attackers targeting a UK business are increasingly likely to be a professional fraud operation rather than an amateur chancer, which means the social engineering behind the attack is well rehearsed. Second, the falling cost means smaller businesses, not just multinational engineering firms, are now realistic targets. Our guide to the different types of hackers explains how this shift from individual actors to organised, service-based crime has played out across cybercrime more broadly, not just in deepfake fraud.
The Three Faces of Deepfake Technology Threats in 2026
Deepfake technology threats fall into three broad categories: financial fraud, reputational attacks, and disinformation. Each has its own attack pattern, its own victims, and its own defence.
Financial Fraud and the New Face of Business Email Compromise
The Arup case is the clearest illustration of how deepfakes have upgraded business email compromise. The old version of this scam relied on a convincing email alone. The new version adds a live video call, with a synthetic CFO or managing director instructing staff to move money urgently and in confidence.
The scale of the shift is significant. Sumsub’s Identity Fraud Report recorded a ninety four percent year-on-year rise in deepfake attempts in the UK, and separate industry survey data suggests that around half of finance professionals in the UK and US have already been targeted by an AI-powered impersonation attempt, with a substantial share saying the attack succeeded at least partially.
A 2025 Gartner survey of over three hundred organisations found that more than six in ten had experienced some form of deepfake attack in the previous twelve months, spread across voice and video formats in roughly equal measure. Criminals no longer need Hollywood-grade production values. Commercial deepfake generation tools and open-source models have brought the cost and skill barrier down to something within reach of an ordinary fraud gang.
Our statistics on email phishing scams show how the initial contact in these attacks usually still begins with a fairly conventional phishing email, before the deepfake video call is used to overcome any lingering doubt.
Reputation Hijacking and Non-Consensual Synthetic Imagery
Away from the finance department, deepfakes are used to damage reputations directly. Non-consensual sexual imagery remains the single largest category of deepfake content online, and it disproportionately targets women. The damage extends well beyond the individual depicted, since employers, clients, and family members may see fabricated material before anyone has a chance to explain it is fake.
UK law has moved to address this specific harm. Since January 2024, sharing intimate images without consent, including deepfakes, has been a criminal offence under the Sexual Offences Act 2003, as amended by the Online Safety Act. The Data (Use and Access) Act 2025 went further, criminalising the creation or the request to create a non-consensual intimate deepfake, with that offence brought into force in early 2026. Anyone experiencing this kind of targeted harassment should also read our guide to cyberstalking, which covers the reporting routes available to victims.
Disinformation and the Erosion of Institutional Trust
The third pillar is harder to measure but no less serious. Fabricated statements from politicians, executives, or public health officials can spread faster than any correction, and by the time a fake clip is debunked, the damage to public trust has often already been done. The UK’s National Cyber Security Centre has flagged deepfake-enabled disinformation as a rising concern in the run-up to major democratic events, alongside more familiar threats such as state-backed spear-phishing.
The Legal Picture: UK and EU Rules on Deepfakes
There is no single UK law called the Deepfake Act. Instead, deepfake technology threats are addressed through a patchwork of existing and recently updated legislation, and it helps to separate the rules by the type of harm involved.
For non-consensual intimate imagery, the Online Safety Act 2023 and the Data (Use and Access) Act 2025 create specific criminal offences, discussed above. For financial fraud, the relevant development is not a deepfake-specific law at all but the Economic Crime and Corporate Transparency Act 2023. Its new failure to prevent fraud offence came into force on 1 September 2025 and applies to large organisations, defined as those with more than 250 employees, more than £36 million in turnover, or more than £18 million in assets.
Under this offence, a company can be criminally liable if an employee or associated person commits fraud for its benefit, including deepfake-enabled fraud, unless the organisation can show it had reasonable fraud prevention procedures in place. Boards can no longer treat a deepfake incident purely as an IT problem, since the law now expects a documented, risk-based prevention framework at the corporate level.
At EU level, Article 50 of the AI Act introduces transparency obligations for synthetic content, requiring providers to mark AI-generated output in a machine-readable format and deployers to disclose deepfakes to the people viewing them. These obligations apply from 2 August 2026, and UK businesses serving customers in the EU fall within scope even if they have no physical presence there. Non-compliance can attract fines of up to fifteen million euros or three percent of worldwide annual turnover, whichever is higher, which puts deepfake labelling on the same enforcement footing as other AI Act obligations rather than treating it as a minor administrative point.
For Irish readers and any UK business handling the personal data of Irish or EU customers, it is also worth noting that deepfakes built from a real person’s face or voice involve processing biometric data, which brings GDPR and the oversight of the Irish Data Protection Commission into play alongside the AI Act itself. A deepfake incident involving a customer’s likeness is therefore not just a fraud matter but potentially a data protection one too.
Why Antivirus Software Cannot Catch a Deepfake
Standard antivirus and endpoint protection tools scan files and network traffic for known malicious code. A deepfake video call is not malware. It arrives through a legitimate video conferencing platform, carries no malicious payload, and triggers no signature-based alert. This is the core reason deepfake technology threats slip past defences that were built for an earlier generation of attacks.
The vulnerability being exploited is human trust, not a software gap. That is why the most effective countermeasures are procedural rather than technical, built around verification steps a person follows rather than a filter a machine applies. Reading our explainer on why cyber security matters for every business is a useful starting point if your organisation has historically treated this as a purely technical budget line.
Some vendors now offer real-time deepfake detection for video calls, typically looking for inconsistencies in blinking patterns, lighting, or blood-flow-driven micro-expressions in skin tone. These tools are improving, but they remain an arms race. As detection gets better, so does the generation technology built to evade it, which means detection software should sit alongside process controls rather than replace them.
Building a Practical Defence Against Deepfake Technology Threats
Reducing your exposure to deepfake technology threats means combining a small number of clear verification habits with an updated approach to business continuity planning. Neither element works well without the other.
Verification Protocols for Finance Teams
The single most effective control is out-of-band verification: confirming any unusual or high-value instruction through a second, independently initiated channel before acting on it. In practice, that means calling the requester back on a known number rather than the one supplied in the suspicious message, and treating any request for secrecy or urgency as a red flag rather than a reason to move faster.
A pre-shared verbal code word, agreed in advance and never sent over email or chat, is increasingly recommended for high-value financial approvals. If a caller or video participant cannot supply it, the transaction should stop regardless of how convincing the call appears. This sounds basic, but it defeats even a flawless deepfake, because the fraudster simply does not have the word.
Updating Business Continuity Plans for AI-Enabled Fraud
Most business continuity plans were written with ransomware, power outages, or supply chain failure in mind. Few explicitly address a scenario where a senior executive’s identity is convincingly faked on a live call. Boards should review their plans to include a defined escalation path for suspected deepfake incidents, clear authority for staff to pause a transaction without fear of reprisal, and a rehearsed process for notifying the bank, Action Fraud, and affected customers or partners quickly.
Awareness training also needs updating. Warning staff to “look for blinking” is outdated advice, since most current-generation tools have largely solved that particular tell. Training should instead focus on behavioural red flags, such as unusual urgency, requests to bypass normal approval steps, or a caller who refuses to perform a simple, unplanned action like turning their head or repeating a random phrase. Our statistics on cybersecurity awareness training effectiveness set out what measurably changes staff behaviour, rather than what merely ticks a compliance box.
How to Spot a Deepfake: Practical Warning Signs
No single tell reliably identifies a deepfake, which is why a layered approach works best. The table below separates technical artefacts, which are becoming harder to spot as the technology improves, from psychological red flags, which remain reliable because they exploit the human element of the scam rather than the video itself.
| Technical artefacts | Psychological red flags |
|---|---|
| Odd lighting or shadows that do not match the background | Unusual urgency or pressure to act immediately |
| Blurred or warped edges around the hairline and ears | Insistence on secrecy from managers or usual contacts |
| Unnatural blinking or a fixed, glassy stare | Refusal to perform a simple, unscripted action on request |
| Audio that lags slightly behind lip movement | A request that bypasses normal approval processes entirely |
| Flat or inconsistent skin tone during head movement | Contact arriving through an unfamiliar channel or number |
Building a Culture of Healthy Scepticism
Deepfake technology threats will keep improving in realism, and no business can rely on staff being able to spot a fake by eye indefinitely. The organisations coping best are not the ones with the most expensive detection software. They are the ones that have made verification a habit: a callback here, a safe word there, a culture where pausing a payment to double-check is rewarded rather than seen as an inconvenience.
Treat deepfake technology threats the way you would treat any other evolving fraud risk: with a documented procedure, regular staff training, and a board that reviews the plan at least once a year rather than filing it away after the initial rollout. For further independent guidance on reporting suspicious contact and protecting your organisation, the National Cyber Security Centre’s advice for smaller organisations is a useful reference to share with your wider team.
Frequently Asked Questions
Is it illegal to create a deepfake in the UK?
It depends on the type of deepfake. Creating or sharing a non-consensual intimate deepfake is a criminal offence under the Sexual Offences Act 2003 as amended by the Online Safety Act, and under the Data (Use and Access) Act 2025. There is no general UK law banning all deepfakes outright, and satire, clearly labelled fiction, and consensual content fall outside these specific offences.
Can antivirus software detect a deepfake?
No. Standard antivirus tools scan for malicious code and will not flag a deepfake video call, since the call itself carries no malware. Specialist deepfake detection tools exist separately and analyse the visual or audio stream itself for inconsistencies.
How much does it cost a criminal to make a deepfake?
Costs have fallen sharply. Commercial deepfake generation services and voice cloning tools are available for a modest monthly fee, and some capabilities are accessible through free or low-cost consumer apps, which is a major driver behind the rise in deepfake technology threats over the past two years.
Should my company use a pre-shared safe word?
Yes, for any process involving high-value transfers or sensitive instructions delivered by phone or video. A safe word costs nothing to implement and defeats even a highly convincing deepfake, because the fraudster cannot supply information that was never shared electronically.
Are deepfake scams covered by cyber insurance?
This varies by policy and often sits in a grey area between cyber cover and social engineering or fraud endorsements. Businesses should ask their broker specifically whether deepfake-enabled fraud is covered, rather than assuming a standard cyber policy responds.
How do deepfakes affect smaller businesses, not just large corporations?
Smaller firms are often more exposed, not less, since they typically lack a dedicated verification protocol and may rely on a single trusted approver for payments. The failure to prevent fraud offence under the Economic Crime and Corporate Transparency Act 2023 only applies to large organisations, but the underlying fraud risk applies regardless of company size.