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Protect an AI video app with safeguards at every stage: define prohibited uses, screen prompts and uploaded media, inspect generated video and audio before delivery, require consent for real-person likenesses, preserve provenance, and give people a clear way to report abuse. No single filter or provenance signal can determine every case of deception or misuse, so automated checks need policy rules, human review, and consistent enforcement behind them.
Define the risks and rules before building filters
Write policies that describe prohibited uses in terms users and reviewers can apply. Relevant cases include unauthorized use of a recognizable person’s likeness, non-consensual intimate imagery, impersonation or fraud, harmful deception, and sexual or exploitative content involving minors. Explain how the rules apply to prompts, uploaded media, generated outputs, and content shared through your app.
Make consent operational, not merely a sentence in the policy. Specify who may authorize a likeness, what uses the authorization covers, how the app records it, and how a person can revoke it. Set stricter limits for minors and intimate contexts. Some cases depend on context: a depiction may be misleading or harmful because of its presentation or intended use, even when a simple content category does not capture the risk.
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Check both the text prompt and any uploaded image, video, or audio before generation. A benign-sounding prompt can become unsafe when paired with a sensitive input, so evaluate the combination rather than relying on text alone. OpenAI’s Sora system-card materials describe multimodal prompt and media filtering as a product-specific example of this scope, not a universal standard or guarantee.
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Choose a response for each policy outcome. Depending on severity and confidence, the app can block a request, hold it for human review, or allow it under applicable restrictions. Keep the moderation decision and the relevant policy category so an appeal or later investigation can be understood.
Treat prompt injection as a security issue
When an app retrieves or processes third-party text or media, that material may contain instructions designed to redirect the model—for example, text embedded in a clip or retrieved from an external source. Treat such content as untrusted data, not as an instruction with authority over the user’s request or the app’s rules. Constrain how generation components can use it, and do not give them unnecessary access to sensitive data or consequential tools.
OpenAI’s official “Understanding prompt injections” explainer calls prompt injections “an evolving security challenge for AI.” That is a useful security framing: prompt abuse is not only a matter of detecting objectionable wording, but also of preventing untrusted content from changing what a system is allowed to do.
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Inspect generated video and audio before release
Input checks cannot catch every problem in the result. Before delivering or enabling sharing of a generation, inspect video frames and speech or audio transcripts against the same policy categories. OpenAI’s Sora materials describe checks over frames and audio transcripts; this is a documented product example, not evidence that classifiers catch every violation.
For severe or uncertain flags, block delivery or hold the output for human review. For lower-risk cases, apply the policy’s appropriate restriction or labeling. Preserve the review outcome so the app can explain a decision and identify recurring failure patterns. Avoid treating a model score as a final judgment when context, consent, or likely harm is unclear.
Build explicit safeguards for likeness and consent
If users can create or use recognizable real-person likenesses, make permission a distinct control in that workflow. Define who can create a likeness asset, who can use it, which kinds of depictions are permitted, and how authorization can be withdrawn. Provide a route for the depicted person or an authorized representative to request review or removal.
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Apply higher thresholds to minors and intimate or deceptive depictions. A system should not assume that uploading a photo proves permission to synthesize the person in it. OpenAI’s Sora system-card materials describe consent controls and restrictions on some real-person content at launch; these are time-bound choices for that product, not proof that any particular control is effective across apps.
Preserve provenance and make reporting usable
Where feasible, attach content credentials such as C2PA metadata, use visible AI labels, and avoid stripping provenance information during export. Keep internal detection tools as supporting signals. OpenAI’s Sora materials describe C2PA metadata, visible watermarks, detection tools, and reporting as elements of that product’s approach.
Provenance helps assess where a file came from or how it was handled; it does not establish that the video’s claims are true or that use of a person’s likeness was authorized. Metadata may also be lost during editing or reposting. Treat provenance as one piece of evidence, not as a truth label or a substitute for consent checks.
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Let people report a specific video and, where relevant, associated accounts or interactions. Make the reporting path easy to find and explain what information is useful, such as the content link and the nature of the concern. A reporting process should route cases to investigation rather than treating a report as an automatic finding.
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Combine automated signals and user reports to identify patterns, then prioritize high-impact or ambiguous cases for trained human review. When a violation is confirmed, remove or restrict the content and apply the account-level enforcement your policy specifies. Give affected users a meaningful route to request review or removal, and provide recourse for users whose content or accounts were actioned incorrectly.
Retain the records needed to understand decisions: moderation outcomes, consent records, provenance data where available, reports, and enforcement history. Review false positives, missed abuse, and emerging bypass patterns, then update policy and safeguards. OpenAI’s Sora system-card materials describe automated monitoring, human review, and enforcement as product-specific measures; they do not establish a universal best practice or comparative effectiveness result.
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Map each safeguard to a stage and response
Use this operational map to check whether your design covers the full path from creation to post-publication action. It is a planning framework, not a published benchmark comparing apps.
| Stage | Risk to address | Possible response | Record to retain |
|---|---|---|---|
| Prompt and uploaded media | Unauthorized likeness, minor-safety concerns, intimate imagery, fraud or deception, and prompt injection | Block, restrict, or hold for review according to policy | Moderation decision and policy category |
| Generated video and audio | Violations that were not apparent in the inputs, including content in frames or speech | Block delivery, hold for review, or allow with applicable labeling | Output-review result and action |
| Likeness workflow | Use of a recognizable person without valid authorization | Require permission, limit the use, or reject it | Consent scope, authorization, and revocation status |
| Export and publication | Loss of context or provenance as content is shared | Attach available credentials and visible AI labeling | Available provenance information |
| Reports and enforcement | Harmful content or repeated abuse discovered after publication | Investigate, remove or restrict violations, and apply consistent enforcement | Report, investigation outcome, and enforcement history |
What Sora’s documented safeguards do—and do not—show
OpenAI’s “Creating with Sora safely” page states that Sora was no longer available as of April 26, 2026. Its Sora and Sora 2 materials can therefore serve as examples of documented safety mechanisms and historical product choices, not recommendations to use a currently available product. They also do not establish an industry-wide effectiveness ranking: the cited materials provide no comparable cross-app statistic that would justify one.
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