What is synthetic media?
Privacy, security and identity
Synthetic media is the broad category of content that has been generated or meaningfully modified by AI or related algorithms. It can include text, images, audio and video, from harmless dubbing and image editing to convincing voice clones and fabricated clips. Deepfakes are one subset of synthetic media, not the whole field. The term matters because the same technology can support useful business work, while also creating new risks around trust, fraud, privacy and evidence.
What this means
A simple mental model is this: synthetic media is media that a machine has helped create in a substantial way. Sometimes the machine makes something from scratch, such as a marketing image, a translated voiceover, or a written summary. Sometimes it changes something that already exists, such as replacing a background, cloning a speaker's voice for localisation, or editing a person's appearance in a video.
That breadth is important. Public discussion often jumps straight to deepfakes, celebrity impersonation, or election disinformation. Those are real concerns, but they are only one part of the picture. In ordinary organisational work, synthetic media also covers product mock-ups, AI generated illustrations, automated training narration, multilingual customer support audio, and edited meeting clips. A lot of it is routine, useful, and not meant to deceive.
It also helps to keep "synthetic media" distinct from "fake". Synthetic media can be clearly labelled, authorised, and perfectly acceptable. A company might use an AI voice for accessibility, or generate draft imagery for an internal concept deck. The problem starts when people mistake synthetic content for authentic records, or when it is used to imitate, manipulate, impersonate, harass, or defraud.
Deepfakes sit inside this wider category. In most policy and regulatory discussions, a deepfake is a narrower kind of synthetic media that misrepresents someone or something, usually in audio or video. So if you are writing policy, buying tools, or setting controls, it is better to start with the bigger category. That helps you cover both the useful and harmful cases, rather than treating every synthetic asset as a scandal or every label as enough protection.
For leaders, this is not only a media issue. It is becoming a communications issue, a fraud issue, a privacy issue, a procurement issue, and in some settings an evidence issue. The practical question is not whether synthetic media exists. It does. The practical question is how your organisation will create it, label it, verify it, respond to misuse, and keep trust when the line between generated and authentic content is harder to see.
Why it matters
Synthetic media now appears in ordinary work much earlier than many leaders expect. Marketing teams use it for drafts and localisation. Learning teams use it for narrated training. Customer teams use synthetic speech and avatars. Internal communications teams may use AI edited clips or translated video. That means governance cannot be left to a small innovation group. It touches brand, legal, information security, procurement, HR, and frontline operations.
The business case is straightforward. Synthetic media can reduce production time, widen language coverage, make content more accessible, and lower the cost of repetitive creative work. It can help smaller organisations produce material that used to require specialist studios.
The risk case is just as straightforward. The same tools can be used for impersonation, payment fraud, fake evidence, reputational attacks, staff harassment, synthetic identity abuse, and misleading public communications. They can also create privacy problems when a person's face, voice, or likeness is used without proper authority. In regulated settings, the issue is not just whether content is synthetic. It is whether people can tell what it is, why it was produced, and whether they should trust it for the decision in front of them.
There is also a procurement angle. Detection tools, watermarking tools, provenance standards, and content credentials are improving, but none is a magic fix. A leader who assumes "the detector will catch it" is likely to be disappointed. In high trust contexts, the stronger approach is layered: decide when synthetic media is allowed, require disclosure where appropriate, record provenance where possible, and design business processes that do not rely on a single signal of authenticity.
How it works
Synthetic media is usually produced in one of two ways. The first is generation, where a model creates new content from prompts or other inputs. The second is transformation, where a model edits or reworks existing material. Text models can draft copy. Image models can generate or inpaint visuals. Audio models can clone or synthesise speech. Video tools can animate avatars, lip sync speech, or alter scenes.
Under the surface, most of these systems learn patterns from very large sets of examples. They do not understand the world in the way a human does. They learn statistical relationships between words, sounds, pixels, timing, and context. That is why they can create convincing material quickly, but also why they can produce errors, distortions, or details that look plausible without being true.
For organisations, the more important part is usually not the model architecture. It is the content pipeline. A piece of synthetic media tends to move through three stages: creation, publication, and consumption. Content is created by a person using a tool, or by a tool acting on instructions. It is then published through internal systems, websites, campaigns, chat channels, or external platforms. Finally, someone consumes it and decides whether to trust it, act on it, or share it. Risk can enter at every stage.
Because detection alone is unreliable, many technical efforts now focus on transparency rather than perfect classification. One group of methods records provenance, meaning facts about where content came from and how it was changed. This can include metadata, signed records, timestamps, device information, edit history, or machine readable markings that indicate AI generation or modification. Content Credentials, based on C2PA standards work, are part of this broader push. The idea is not to prove that content is true. It is to provide a verifiable history that helps people assess it.
Another group of methods tries to signal synthesis within the content itself. Watermarks are the most familiar example. Some are visible, such as a label on an image. Others are covert and meant to be detected by software. Watermarking can be useful, but it is not dependable in all settings. Watermarks can be stripped, corrupted by format changes, or simply absent when bad actors choose not to cooperate.
A third group of methods uses detection systems to estimate whether content has been artificially generated or manipulated. These systems may look for patterns in images, audio artefacts, or inconsistencies in how content was made. They can help analysts, platforms, and investigators triage suspicious material. But they do not settle the matter on their own. Detection quality varies by modality, by tool, by compression, by editing chain, and by how new the generation method is. In practice, detection often works best as one layer in a wider process, not as the sole judge of authenticity.
That is why provenance matters so much. Provenance makes a different promise from detection. Detection asks, "Does this look synthetic?" Provenance asks, "What can we verify about the history of this file?" Those are related but not identical questions. A real file can still mislead if it is taken out of context. A synthetic file can still be legitimate if it is properly authorised and labelled. Provenance helps with the history of content, not the truth of every claim attached to it.
This is also where governance becomes practical. A useful synthetic media policy usually answers five questions. What kinds of synthetic media may we create? When must we disclose that it is synthetic or AI modified? What records of provenance or approval do we keep? Where do we need extra consent or legal review, especially for likeness, voice and personal data? And how will we respond if someone uses synthetic content to impersonate us or our people?
In other words, synthetic media is less about one scary file and more about a chain of decisions. The technology generates or alters content. The organisation decides where it is allowed, how it is labelled, how it is verified, and what level of trust is appropriate in each context. Strong organisations treat that as an information integrity discipline, not just a creative tool choice.
Examples
A marketing team creates draft product images for internal review before a studio shoot. The images are not published externally, but they save time in briefing agencies and aligning stakeholders. The control need here is modest but still real: keep drafts clearly marked and make sure nobody republishes them as if they were photographs.
A learning team localises training videos by generating synthetic narration in several languages. This can improve reach and reduce production cost, especially when content changes often. The governance issue is not whether the audio is fake. It is whether learners know they are hearing synthetic narration, whether terminology is accurate, and whether any cloned voice has proper permission.
A customer operations team uses a voice assistant to triage routine service calls. This may be entirely appropriate, but it changes the trust equation. People should know they are interacting with an AI generated voice, and sensitive actions such as payments or account changes should still require stronger checks.
An executive communications team receives a voice note that seems to come from a senior leader asking for an urgent transfer or document release. This is where synthetic media moves from creative tool to fraud vector. The right defence is not better listening. It is a separate verification step using trusted channels and role based approval.
A newsroom, public affairs team, or regulated communications function uses provenance records and review workflows for published media. Here the aim is to preserve trust over time. If a clip is disputed later, the organisation can show where it came from, how it was edited, and who approved it.
Common misunderstandings
One common misunderstanding is that synthetic media means the same thing as deepfake. It does not. Deepfakes are a subset, usually focused on deceptive or manipulative audio visual content. Synthetic media is broader and includes routine, lawful, and disclosed uses.
Another is that synthetic means deceptive by definition. It does not. A translated AI voiceover, a generated illustration, or a clearly labelled avatar can be perfectly legitimate. The real issue is representation, consent, trust, and context.
A third misunderstanding is that labelling or watermarking settles everything. It does not. Labels can be omitted, watermarks can fail, and provenance records do not prove the truth of the message itself. They help, but they do not remove the need for process and judgement.
A fourth is that detection tools will keep pace automatically. This is unlikely. Detection is improving, but so are generation tools. In high trust settings, leaders should assume layered controls are necessary.
Risks and boundaries
Synthetic media does not remove the need for human accountability. If a business publishes AI generated material that misleads customers, breaches consent, or enables fraud, the fact that a model produced it does not change the organisational responsibility.
There are also clear boundary issues. Personal data, biometric data, likeness rights, confidential data, intellectual property, and sector specific communications rules can all be relevant. The exact legal position varies by jurisdiction and use case. That is why internal policy should draw lines around approved uses, sensitive uses, mandatory disclosure, and escalation routes.
It is also worth being clear about what provenance can and cannot do. Provenance can support trust by showing content history. It cannot guarantee that the underlying message is honest, fair, or contextually accurate. Likewise, absence of provenance does not automatically prove bad intent. Many legitimate files still lack strong provenance today.
This article is a practical explainer, not legal advice. If your organisation plans to use cloned voices, synthetic likenesses, or public facing generated media at scale, legal and privacy review should be built in.
What to do next
First, map where synthetic media already appears in your organisation. Do not just ask the marketing team. Include learning, customer operations, internal communications, product, design, and any team using AI assistants that generate text, images, audio, or video.
Second, decide which uses are allowed, restricted, or prohibited. Most organisations do not need a blanket ban. They need categories. For example, internal concept images may be acceptable, while executive voice cloning or public use of employee likeness may require special approval.
Third, set disclosure and provenance rules. Decide when content must be labelled for audiences, when internal records must show AI involvement, and when machine readable provenance or content credentials should be required from tools and suppliers.
Fourth, strengthen identity and payment controls. Assume that voice and video can be spoofed. Critical approvals should rely on independent verification, dual control, and role based checks, not on apparent familiarity.
Fifth, update procurement questions. Ask vendors how they handle provenance, watermarking, metadata, detection claims, consent controls, biometric data, moderation, and audit records. Ask what happens when a user removes or edits the generated content after export.
Sixth, prepare an incident path. If your brand or executives are impersonated, teams should know who owns response, which evidence to preserve, how to contact platforms, and how to communicate quickly with staff, customers, and partners.
Finally, train people in plain language. Staff do not need a lecture on model architectures. They need to know what synthetic media looks like in their work, what checks to use, and when to escalate.
FAQs
Is all AI generated content synthetic media?
Broadly, yes. If AI has generated or materially altered text, images, audio, or video, it usually falls within the category. The practical question is then how it is used, disclosed, and governed.
Is a deepfake the same as synthetic media?
No. Deepfakes are usually treated as a narrower subset, often focused on deceptive audio visual content that misrepresents a person, event, or object.
Can synthetic media always be detected?
No. Detection can help, but it is not consistently reliable across every modality, tool, compression chain, and editing workflow. Provenance, process controls, and human review still matter.
Does provenance prove that content is true?
No. Provenance can show a content item's origin and edit history. It helps assess authenticity and chain of custody, but it does not by itself prove that every claim in the content is accurate or fair.
Should companies ban synthetic media outright?
Usually not. A blanket ban often fails in practice. A clearer approach is to define acceptable uses, sensitive uses, approval thresholds, disclosure rules, and stronger controls for high trust scenarios.
Why does this matter for fraud prevention?
Because synthetic voices, faces, and messages can be used to imitate trusted people. If payment approvals or account changes rely on a familiar voice or video alone, the organisation is exposed.
Sources
Reducing Risks Posed by Synthetic Content: An Overview of Technical Approaches to Digital Content Transparency (National Institute of Standards and Technology). Primary. Definition of synthetic content, creation publication consumption pipeline, provenance and detection approaches, and the point that transparency measures help but are not a silver bullet.
Synthetic media and its identification and detection (Information Commissioner's Office). Primary. Privacy and personal data implications, including biometric and likeness related issues, and the growing difficulty of distinguishing synthetic from authentic media.
Public content provenance for organisations (National Cyber Security Centre). Primary. The point that provenance records content history rather than guaranteeing truth, and practical considerations for selecting provenance systems.
Deepfake detection technology (GOV.UK). Primary. Corroborates that deepfakes are a subset of synthetic media and supports the practical fraud, identity verification and content moderation use cases.
Regulation (EU) 2024/1689 of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (European Union). Primary. EU context for machine readable marking of certain AI generated synthetic content.
Deepfake detection technology source notes on synthetic media and deepfakes (Ofcom and DRCF via GOV.UK report citations). Secondary and corroborative. Supports the narrower treatment of deepfakes inside the wider synthetic media category.