Short answer. Synthetic content is information — text, image, audio or video — generated or significantly modified by an algorithm, the definition NIST uses. Whether an enterprise should use it is not a question about the technology but about what the output represents: a generated training-module background and a realistic depiction of a named executive use the same technology but carry completely different risk. The rule: risk rises with how easily a reasonable viewer could mistake the output for a record of something real.
Most enterprise policies on this are written either as a blanket permission or a blanket prohibition, and both are wrong for most of what teams actually want to produce. This guide separates the cases, gives the tiering that scales, and states the questions that settle a marginal one.
Key takeaways
- Synthetic content covers both AI-generated material and heavily algorithm-modified real footage; editing real footage does not exempt it from the category.
- Risk depends on what the output claims to represent, not on which tool made it.
- Six triggers — real-person depiction, material factual claims, documentary framing, decision impact, rights issues, and unreviewed scale — move an asset out of routine use.
- A three-tier review model (routine, reviewed, escalated) scales better than applying one process to everything.
- Provenance records — model, inputs, reviewer, approval date — must exist internally regardless of whether the audience ever sees them.
What counts as synthetic content?
The category is wider than "made by a generative model." Synthetic content is any output — text, image, audio or video — that an algorithm generated or substantially modified, the sense NIST uses in its guidance on managing AI content risk. It covers an AI-written paragraph, a generated product scene, a synthetic voice, a virtual presenter, a simulated dataset, and a heavily transformed piece of real footage. The last one catches people out: substantial algorithmic modification of a real recording lands in the same category as wholesale generation, which is why "we only edited it" is not a category exit.
What it does not determine is risk. The same generator produces a placeholder illustration and a fabricated depiction of a real person. Governance that keys on the tool has to treat both identically, which means it either blocks useful work or permits harmful work. Governance that keys on the representation does not have that problem, a distinction covered further in AI content governance and provenance.
Three questions define the representation: what does this content claim to be — an illustration, a depiction of a real thing, or a record of an event; who could be misled, and how badly — a colleague reviewing a draft, or a customer making a financial decision; and where does it go — an internal deck, a controlled channel, or open distribution where context is stripped.
Which uses are straightforward?
These uses are low-risk because nothing in the output purports to be a record of anything: ideation and mood boards, storyboard frames, internal prototypes and design exploration, synthetic environments and backgrounds, draft copy a human will rewrite, training simulations of clearly hypothetical scenarios, placeholder assets ahead of final production, and plainly fictional or diagrammatic illustration.
Production use is also entirely workable once rights and review are settled — campaign assets, voiceover, localised variants of an approved master, catalogue imagery, and educational media, the kind of managed workflow described in AIGC as a service. What makes those safe is not that the content is low-stakes but that the pipeline around them is defined: the approval path exists, the source rights are clear, and a human is accountable for the factual content.
When does synthetic content become high risk?
Risk rises sharply on six triggers, and any one of them moves the asset out of the routine path.
| Trigger | Why it changes the calculation |
|---|---|
| Depicts an identifiable real person | Consent and likeness rights attach, independent of how the output was made |
| Carries a material factual claim | The output is now evidence, and a fluent error is a defensible-looking error |
| Could be taken as documentary | Photorealism plus a news, incident or record framing |
| Influences a financial, legal or health decision | Harm from error is direct rather than reputational |
| Uses copyrighted or confidential input | The rights problem is upstream of the output |
| Ships at volume without per-asset review | Scale converts a small error rate into a large number of errors |
The two most often underestimated are the factual-claim trigger and the scale trigger. A confident sentence containing a wrong figure is more damaging than an obviously wrong one, because it survives review by anyone who is not checking. And a 2% defect rate is a curiosity at fifty assets and a serious problem at five thousand, which is why quality control at scale has to be designed in rather than added later.
How should review tiers be structured?
Three tiers, applied by trigger rather than by team, is enough to make review scale without becoming a bottleneck.
| Tier | Examples | Controls |
|---|---|---|
| Routine | Internal drafts, prototypes, backgrounds, non-public exploration | Approved tools; no confidential input; creator accountable; no external release |
| Reviewed | Public marketing assets, localised variants, educational media | Named human reviewer against a rubric; facts checked against a source pack; rights confirmed; provenance recorded |
| Escalated | Any high-risk trigger above | Subject-matter, legal or compliance sign-off; documented consent where a real person is depicted; full rather than sampled review; disclosure decided explicitly |
Two design rules make the tiering hold. Tier by trigger, not by team — a routine asset produced by an escalated team is still routine, and vice versa. And automate the cheap checks at the boundary: banned terms, missing disclaimers, unapproved logos and unsupported product claims can be blocked before a human sees the asset, which is what keeps the reviewed tier from becoming a bottleneck for the kind of human-in-the-loop review that the escalated tier needs. Track the exception rate per workflow — a workflow that repeatedly triggers escalation was scoped wrong, and fixing it is cheaper than reviewing its output forever.
What questions settle a marginal case?
Five questions settle a marginal case faster than a policy document. Does generating this create meaningful value, weighed against the review cost it adds — the net is frequently negative once that cost is counted. Could a reasonable viewer be misled about what they are seeing, not could an expert detect it. Are the source rights clear for every input, reference and likeness a model was conditioned on. Can a human actually verify the important claims, since an asset is unverified regardless of how many people approved it if nobody in the chain is qualified to check the substance. And is there a defensible answer if it is challenged six months later — what made it, from what, reviewed by whom, on what date.
If the benefit is marginal and the misleading-viewer or defensible-answer question is uncomfortable, conventional production is the better answer. That is a legitimate outcome, and a policy that never produces it is not being applied.
What must be recorded, regardless of tier?
Every asset needs an internal record of how it was made, independent of whether that record is ever shown to the audience. Provenance is the traceable record of how a piece of content was produced — which model or process, which source materials, who reviewed it, and where it was published — and it is an internal requirement before it is a public one, as covered in content provenance standards like C2PA and SynthID.
This record is what survives when the file does not. Metadata is stripped by ordinary operations — transcoding, resizing, most platform uploads — so an internal ledger independent of the file is the only thing that still substantiates a claim about an asset after it has been through a distribution pipeline. It is also what an audit actually asks for.
How does Lifewood approach synthetic content?
Lifewood produces synthetic media inside a controlled workflow rather than as a raw tool output. An asset-level record covers the model, inputs, reviewer and labels; human review is a required pipeline step rather than a final glance; and the tiering model above keeps high-volume routine work from being held to the pace of the escalated tier.
Where the work is multilingual, review is in-market rather than central — across 100+ languages and 40+ delivery centres across 30+ countries, with a dual-layer human-in-the-loop process held to a 95%+ accuracy SLA. Part of that in-market capacity runs through Lifewood's Bangladesh workforce, which logged 414,120 training hours in 2025. The reason review stays local is narrow and practical: whether a depiction reads as illustrative or as a record is partly a cultural judgement, and it is not one that can be made from headquarters. The scope of this work is described in AIGC services and in how it feeds AIGC video production.