---
title: "Artificial Intelligence (AI)"
description: "General background on AI techniques like spam classification and reputation scoring in email systems. MailMum itself uses rule-based blacklist/whitelist matching, not AI/ML scoring."
url: "https://mailmum.io/glossary/artificial-intelligence-ai/"
---

# Artificial Intelligence (AI)

Artificial intelligence (AI) describes systems that perform tasks normally
requiring human judgement — classifying, predicting, generating — by
learning patterns from data rather than by following rules a person wrote out in
advance.

Email was one of the first mass applications of machine learning, and it remains
one of the clearest: spam filtering has used statistical classification since
the early 2000s, long before the term AI came back into fashion.

## How is AI used to filter spam?

Spam filtering is a classification problem, and it has been solved with
progressively more elaborate models:

* **Bayesian filters.** The original approach, still in use in SpamAssassin and
  rspamd. The filter learns the probability that a message is spam from the
  tokens it contains, trained on the messages a user marked one way or the
  other. It is cheap, explainable, and adapts per-user.
* **Supervised classifiers on engineered features.** Rather than words alone,
  the model sees header structure, URL properties, sending patterns and
  authentication results. This is what most commercial filters run today.
* **Neural models.** Used mainly by large mailbox providers, for phishing
  detection in particular, where the signal is in the phrasing and the visual
  presentation rather than in individual keywords.

The economics are unusual. False positives cost far more than false negatives
— a missed spam is an annoyance, a blocked invoice is a business problem
— so production filters are tuned much more conservatively than raw
accuracy figures suggest.

## How is AI used for IP and domain reputation?

Reputation systems consume behavioural data about a sender — volume over
time, ratio of valid to invalid recipients, spam trap hits, complaint rates,
authentication results, the behaviour of neighbouring addresses in the same
range and the same [ASN](https://mailmum.io/glossary/autonomous-system-number-asn/) — and
produce a score that receiving systems use to accept, defer or reject.

Two properties of these systems matter to anyone running a mail server:

1. **They are predictive, not just historical.** A brand-new IP address with no
   history is not neutral; it is scored by the properties it shares with other
   addresses, which is why warming up a new sending IP is necessary at all.
2. **They are opaque.** A model-derived score cannot be traced back to a single
   rule, which is why "why was my mail rejected?" so often has no better answer
   than a generic 5xx and a postmaster page.

## Where else does AI appear in email systems?

* **Phishing and BEC detection**, where the model looks for anomalies in a
  relationship — a first-time sender asking for a payment change —
  rather than for bad content.
* **Natural language processing** for routing, summarising and drafting replies
  in helpdesk and shared-mailbox tooling.
* **Anomaly detection on authentication logs**, flagging a mailbox whose sending
  behaviour has suddenly changed, which is usually the first evidence of a
  compromised account and of the [abuse](https://mailmum.io/glossary/abuse/) that follows.

## What are the limits?

AI-based filtering is probabilistic, and that has consequences the marketing
rarely mentions. Decisions are hard to explain, which makes disputes hard to
resolve. Models drift as sending behaviour changes and need retraining.
Adversaries adapt deliberately, since spam is one of the few classification
problems with an opponent actively optimising against the classifier. And a
model trained mostly on English commercial mail will behave differently on a
German-language transactional stream.

None of this argues against using them. It argues for keeping deterministic
controls — authentication checks, explicit
[blocklists](https://mailmum.io/glossary/ip-blacklisting/), rate limits — underneath the
probabilistic layer, so that the outcomes you care most about do not depend on a
score.

## Note on MailMum

The techniques above describe the broader email industry, not MailMum
specifically. MailMum blocks or passes a connection based on explicit
blacklist/whitelist rules an admin defines, not a machine-learning reputation
score — every decision is traceable to a specific rule, logged in the
audit trail, rather than an opaque model output.