Regulation & Policy
Deepfake
Also known as: synthetic media, AI face swap
A deepfake is synthetic audio, image or video created with AI to make a real person appear to say or do something they did not. It is the most deceptive end of synthetic media, distinguished by its intent to pass as authentic footage. For brands it creates risks around impersonation, fraudulent endorsements and reputational attack.
What it is
Deepfakes use generative models, often face swapping or voice cloning techniques, to transplant a person's likeness or voice into fabricated material. The same underlying technology also powers legitimate uses such as consented voice dubbing, synthetic presenters and archival restoration, so the term usually implies deception rather than a specific method. Quality now ranges from obvious fakes to clips that need technical analysis to assess.
Why it matters
Executive and celebrity impersonation is used in investment scams and payment fraud, which puts brand names and faces at the centre of other people's crimes. A convincing fake endorsement can spread faster than a correction and can end up cited in search results and AI generated summaries. Policymakers are responding with transparency and labelling rules for synthetic media, so disclosure practice is becoming a compliance matter as well as a trust one.
How it works
Defensive practice combines monitoring for brand and executive likeness across social platforms and ad libraries, a rehearsed response plan with platform takedown routes, and authoritative owned pages that state which channels and spokespeople are genuine. On the production side, teams using synthetic voices or avatars secure written consent, keep provenance records and label output, increasingly using content credentials embedded in the file. Verification inside the business relies on process rather than eyesight, such as call back procedures for payment instructions.
When it applies
It applies to any organisation with recognisable leaders, presenters or brand voices, and to any team producing synthetic media where the audience could mistake it for a genuine recording.
Examples
- A cloned voice of a finance director is used on a phone call to authorise an urgent supplier payment.
- Fake video ads use a well known presenter's likeness to promote a fraudulent investment platform, prompting the brand to publish a scam warning page.
- A training team creates an AI presenter for internal videos, with the narrator's written consent and an on screen label stating the voice is synthetic.
How it is measured
- Number of detected impersonation assets by channel and month
- Median time from detection to platform takedown
- Reach or engagement of fraudulent content before removal
- Share of published synthetic assets carrying consent records, labels and content credentials
Insights on Deepfake
Related terms in Regulation & Policy
- AI complianceAI compliance is the work of making sure AI systems meet the laws, regulations, standards and internal policies that apply to them. It spans data protection, transparency, risk classification, documentation, human oversight and record keeping across the life of a system. In practice it combines legal interpretation, engineering controls and ongoing evidence gathering.
- AI governanceAI governance is the set of policies, roles, controls and review processes an organisation uses to manage how AI systems are built, bought and used. It covers risk assessment, documentation, human oversight, data handling and accountability. It applies both to AI a company develops and to third-party AI tools used by staff.
- AI privacyAI privacy is the set of practices, rights and obligations that govern how personal data is collected, used, stored and exposed when building or using AI systems. It covers training data, prompts and outputs, retention by AI vendors, and the transparency and control offered to the people whose data is involved. For marketing teams it shapes what customer data can safely be put into AI tools and what must be disclosed.
- AI safetyAI safety is the practice of designing, testing and operating AI systems so they cause less harm, behave predictably and resist misuse. It covers alignment with intended behaviour, evaluation and red teaming, content guardrails, and monitoring once a system is live. For marketers it shapes what models will say, how assistants handle brands, and what compliance teams expect before AI tools go into production.
- AI transparencyAI transparency is the practice of making clear how an AI system works, what sits behind it, and when content or an interaction involves AI. It covers regulatory disclosure duties as well as voluntary explanations such as model cards, labels on synthetic media and citations in AI generated answers. For publishers and marketers it sets expectations about when AI involvement should be declared and how clearly.
- Competition and Markets AuthorityThe Competition and Markets Authority (CMA) is the United Kingdom's competition and consumer protection regulator. It investigates mergers, anti-competitive conduct and market practices, and it holds specific powers over large digital firms under the Digital Markets, Competition and Consumers Act 2024. Its decisions shape how search engines, app stores and AI assistants operate in the UK market.
Primary research · August 2026
How ChatGPT Shortlists Software Brands
An audit across 10 categories and 60 buying questions. I recorded what ChatGPT reads, throws away and links to when a buyer asks it which software to buy, and what that decides.
- 60
- Questions asked
- 10
- Software markets
- 2,680
- Results read
- 367
- Links shown
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