What is a spam filter?

Spam filters analyze incoming email across multiple dimensions and assign scores. Messages exceeding threshold scores get filtered to spam or rejected entirely.

Authentication checks come first. Does SPF pass? Does DKIM validate? Does DMARC align? Failed authentication raises flags before content is even analyzed.

Reputation signals follow. Has this sending IP or domain sent spam before? What's their historical engagement? Do they appear on blocklists?

Content analysis examines the message itself. Spam-associated words, suspicious links, text-to-image ratios, formatting patterns. Machine learning models trained on billions of messages identify patterns humans might miss.

Engagement history matters. How do recipients typically interact with this sender? Do they open and click, or ignore and delete? Past behavior predicts future behavior.

Real-time signals adjust. If many recipients suddenly mark a sender as spam, filtering increases immediately for everyone.

No single factor determines the outcome. Filters combine signals into an overall assessment. Strong authentication plus good reputation plus clean content plus positive engagement equals inbox. Weakness in any area increases risk. Multiple weaknesses compound.