A set of rules that tells AI systems which brand messages can appear and which must be filtered out.
Technical documentation for managing content output from artificial intelligence platforms.
01What it is and how it works
The policy lives in a machine‑readable file (often JSON or YAML) that the AI provider reads before generating a response. Each rule matches on keywords, URLs, brand names, or content categories and assigns a decision: allow, flag, or block. When a user query triggers a rule, the model either includes the brand material, adds a disclaimer, or removes it entirely. The system checks the policy at query time, so the same brand can be shown in one context and hidden in another, depending on the rule specifics.
It is a rule list that decides what brand content AI can show or hide.
02What to do about it
1. Review the current policy file for your brand. 2. Add missing brand terms or URLs that you want to protect. 3. Mark any prohibited content (e.g., false claims, adult material) with a block action. 4. Test the changes with the provider’s sandbox or API dry‑run endpoint. 5. Schedule a weekly check of the moderation logs to catch unexpected blocks.
03How it is measured or noticed
AI platforms expose moderation logs that list every rule hit, the query that caused it, and the final decision. Look for columns named policy_match, action_taken, and confidence_score. A rise in block counts for your brand indicates the policy is catching more content, while a drop in allow counts may signal over‑restriction. Dashboard widgets often show trends over the last 7‑30 days.
04Common mistakes
- Using overly broad keywords that block unrelated brand mentions.
- Leaving placeholder values (e.g.,
example.com) in production files. - Relying on a single rule for multiple content types instead of separate, explicit rules.
05Limits
The policy only applies to content that the AI model can retrieve from its indexed sources. It does not affect organic web search results that are not processed through the model. Also, the policy does not replace legal compliance; it cannot override mandatory disclosures required by law.
06Worked example
"When a user asked, 'What are the latest features of BrandX's new phone?', the model checked our policy, found the brand name in the allow list, and returned the official product page link without any disclaimer."
Frequently asked questions
How does a Content Moderation Policy differ from a brand safety guideline?
It depends on the scope. A Content Moderation Policy is a machine‑readable set of rules that tells the AI which brand messages to allow or block, while a brand safety guideline is a higher‑level document that describes the overall brand tone and risk appetite. The policy is enforced automatically by the AI platform, whereas guidelines are usually applied manually by marketers.
Should we implement a Content Moderation Policy for every brand we monitor?
Usually you should have a policy for any brand that appears in AI‑generated results. Without a policy the AI may surface outdated or inappropriate brand messages, which can harm trust. Adding a policy ensures consistent filtering based on the brand’s current messaging rules.
How is a Content Moderation Policy actually applied by the AI platform?
The policy is read from a JSON or YAML file that the AI provider loads before generating a response. Each rule is evaluated against the content the model retrieves, and a hit can either allow, modify, or block the output. The result is recorded in the moderation log for later review.
Does a Content Moderation Policy still work if the AI pulls information from sources that aren’t indexed?
No, the policy can only act on content the model can retrieve from its indexed sources. If the AI accesses external data that isn’t part of its searchable index, the policy has no visibility and cannot filter that material. In such cases you need additional safeguards outside the AI system.
What happens if the policy incorrectly filters a brand message?
If a rule blocks a legitimate brand message, the moderation log will show the rule hit and the original query, letting you see the mistake. You can then adjust or remove the offending rule and re‑upload the policy file. Until the change propagates, the AI will continue to filter that message.
How long does it take for changes to a Content Moderation Policy to appear in moderation logs?
Typically the updated policy is applied within a few minutes after the file is uploaded, but some platforms may cache the old version for up to an hour. During that window you might still see the previous rule behavior in the logs. Monitoring the log after a change helps confirm the new policy is active.
Asked out loud
spoken, not typedThe same term in the words somebody uses speaking to an assistant rather than typing into a box — written from the situation, which is why each one carries the situation it came from.
Usually you can add an exception for that tagline in the Content Moderation Policy file and re‑upload it. Once the updated policy is loaded, the AI will re‑evaluate and should allow the message. Until the change propagates, you’ll continue to see the block in the moderation logs.
Yes, you can view the moderation log that the AI platform provides. It lists the rule that was hit, the original query, and the decision, so you can pinpoint why the competitor was filtered. The log is accessible from the dashboard or via the API.
It depends; you should review the current Content Moderation Policy file for any rules that might match the warning text. Use the policy validator tool to simulate queries and see if they would be blocked. This helps catch missing rules before they cause issues.