Integrating Threat Intelligence into Your Request Pipeline

How to use external threat intelligence signals — IP reputation, ASN data, and domain risk — in real-time request evaluation.

Threat intelligence as one signal among many

IP reputation databases, ASN classifications, and domain risk scores are useful signals, but they work best when combined with per-request behavioral context rather than used in isolation.

An IP address flagged in a threat feed from six months ago may now belong to a legitimate user. A clean IP with no reputation history may be the first time a residential proxy is observed in your system. Recency, confidence, and context all matter.

Latency constraints for real-time lookups

Real-time threat intelligence lookups add to request latency. For use in synchronous pre-render enforcement, the lookup must complete within your latency budget — typically under 5 milliseconds for network lookups against cached datasets.

Local MaxMind-style databases queried in-process add sub-millisecond latency and work offline. Remote API lookups are more current but add network round-trips. Many production systems use local databases for fast-path lookups and background async checks for deeper intelligence.

Contextualizing intelligence signals

The same threat signal should carry different weight depending on context. A datacenter IP visiting your marketing site is probably a monitoring tool. The same IP submitting login credentials is a much higher concern.

Weighting signals by endpoint risk class rather than applying global thresholds reduces false positives significantly while maintaining strong enforcement where it matters.