Domain Reputation Database

Community-driven domain intelligence.

Automated engines analyze technical data, but users recognize deceptive patterns. URLert Discuss provides the human context needed to identify scams and other malicious activity.

Domain Channels

Discussions are anchored to specific domains. Every website has a dedicated space where users can contribute observations and review existing incident reports.

Incident Reporting

Share specific details about deceptive tactics or suspicious behavior. Community feedback helps others identify brand impersonation and typo-squatting early.

AI-Managed Quality

All submissions undergo AI analysis for relevance and quality. This maintains the integrity of the communication by filtering out off-topic content, bias, and automated spam.

Why this exists

The URLert engine uses automated analysis to detect threats in real-time. But technical scans only capture part of the picture—the most deceptive threats are designed to bypass machines and target people instead.

Being the victim of a scam is a frustrating, isolating experience. URLert Discuss was created to turn those individual encounters into a collective defense. This is an educational space where we learn from each other’s experiences to build the kind of resilience that algorithms can’t achieve alone.

By combining automated detection with community-sourced insight, we are building a more comprehensive and human map of the web’s reputation.

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AI Validation

Submissions are reviewed for relevance and bias to maintain a focused and reliable community intelligence platform.

Contextual Threads

Separating general conversation from reported incidents helps keep the information organized and actionable.

Identity & Privacy

We prioritize user privacy while establishing a community-driven reputation system for the web.