This approach requires sourcing and maintaining accurate information, which means you can't fabricate numbers or exaggerate metrics. AI models increasingly cross-reference claims across sources, and inconsistencies damage credibility. The data you include must be truthful and, where relevant, attributed to primary sources. But when you consistently provide specific, accurate information, you build a reputation as a reliable source that AI models return to repeatedly.
Victoria and Albert Museum
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This is the critical step. If a key with Gemini access is embedded in client-side JavaScript, checked into a public repository, or otherwise exposed on the internet, you have a problem. Start with your oldest keys first. Those are the most likely to have been deployed publicly under the old guidance that API keys are safe to share, and then retroactively gained Gemini privileges when someone on your team enabled the API.
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