How do you measure brand safety in AI search results?

AI brand-safety measurement addresses a risk specific to the AI era: the engines synthesize answers from imperfect sources and can state things about a brand that are false, inappropriate, or genuinely harmful, delivered with the fluent confidence that makes errors persuasive. Measuring it means assessing each major engine for three kinds of risk, tracking every model separately, and watching how the picture changes over time.

The three risks we assess

Misinformation

Wrong facts presented as true, fabricated executives, nonexistent lawsuits, unshipped product features, or financial details that match no filing.

Inappropriate associations

The brand tied to content, topics, or entities it should not be linked with.

Dangerous claims

Statements that could cause real harm if a reader acts on them.

Why we track each engine separately

ChatGPT, Gemini, Copilot, Perplexity, Claude, Grok, Google AI Overviews, and Google AI Mode behave differently, so a problem that shows up in one model may not appear in another. Each is measured on its own rather than blended into a single score.

Why tracking over time matters

The engines change. A brand that is safely represented today may not be after a model update or a shift in the sources a model draws on, so brand safety is monitored continuously rather than checked once.

The value: early detection and traceability

The payoff is catching a harmful AI claim while it is still contained, before it spreads or gets repeated, and tracing it back to the underlying source so remediation can target that source. We measure this across the eight engines with AIQ™ and tie remediation to the sources the models are drawing on.