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To build an AI model that classifies phishing emails, define exactly what it should detect, prepare responsibly labeled messages, establish a simple text-classification baseline, and evaluate it on messages kept separate from training. Measure false positives and false negatives—not just accuracy—and begin deployment with a monitored, low-impact action such as routing suspicious messages for review. A classifier is one part of email security, not a guarantee that phishing will be caught.
What should the model classify?
Choose the model’s output and intended action before selecting an algorithm. A first prototype might classify messages as phishing or legitimate. Another might distinguish several threat classes, or assign a risk score to help a person prioritize review. Those are different tasks and need labels that match their intended use.
Keep threat categories separate from clues used to detect them. Microsoft’s documentation distinguishes phishing, spam, and malware as threat types, while spoofing, impersonation, and URL reputation describe detection types or signals. A message could involve more than one threat or signal, so decide whether your system needs one label per email or multiple labels.
Also specify when and how the prediction will be used. A model that ranks messages for a reviewer can tolerate a different balance of errors from one that automatically blocks or quarantines email. That distinction should guide the later choice of threshold.
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What data do you need, and how should you handle it?
Use examples that represent the messages and operating conditions the classifier will encounter, with labels that reflect the task you defined. Data quality matters: duplicates, campaign-specific wording, class balance, and message recency can all shape what the model learns. If near-identical emails appear in both training and evaluation data, results may look stronger than performance on genuinely new messages.
The sources cited here do not establish a current, representative public dataset whose license and suitability for this use are confirmed. They also do not settle privacy or legal requirements for training on real employee email. Before using organizational messages, obtain appropriate authorization and privacy review for your jurisdiction and use case; do not treat this article as legal advice.
- Limit access to message contents and personal data to people and systems that need them.
- Document what data may be used, for what purpose, and how long it will be retained.
- Review whether message content, sender information, URLs, or derived features could expose sensitive information in datasets, logs, or review tools.
- Keep a record of label definitions and how uncertain or disputed examples are handled.
Which model should you start with?
Start with a reproducible baseline rather than assuming a larger or newer model will work better on your data. A useful sequence is to classify message text with TF-IDF features and a conventional classifier, then test whether carefully chosen structural or sender-related features improve results. Compare more complex neural or pretrained language models only after the baseline and evaluation design are sound.
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| Approach | What it can use | Practical consideration |
|---|---|---|
| TF-IDF text features with a conventional classifier | Words and other text-derived features | A straightforward baseline that can be compared with more complex methods. |
| Text plus structured indicators | Message text and selected numeric or categorical features, such as sender- or URL-related indicators | Use only features available at the moment the prediction is made, and handle them safely. |
| BiLSTM or another neural text model | Text represented for a neural model; structured signals can be evaluated separately or incorporated as appropriate | Compare on the same leakage-resistant data splits; greater complexity does not establish better performance on new mail. |
| Pretrained language model, such as DistilBERT | Message text processed by the model | Evaluate inference cost, latency, calibration, and retraining needs alongside predictive metrics. |
An experimental public project illustrates this comparison by combining TF-IDF text features with 28 structural and phishing-related indicators and evaluating Logistic Regression, BiLSTM variants, and DistilBERT. It also documents deduplication before splitting and a stratified train/validation/test setup. These are examples of methods to compare, not a universal recipe or a production benchmark.
Keep data preparation, feature extraction, model settings, and threshold selection reproducible. If any of these change between experiments or after deployment, record the change so later evaluations can be interpreted correctly.
How do you evaluate a phishing classifier fairly?
Separate related messages before measuring performance
Deduplicate messages before creating data splits so copies or near-copies from the same campaign do not appear on both sides of evaluation. Fit the model using training data, use validation data to select settings and a decision threshold, and reserve a held-out test set for final evaluation. Where possible, also evaluate on messages separated by time or campaign; that better probes whether the model generalizes beyond patterns it has already seen.
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Measure the errors that matter
Report a confusion matrix, precision, and recall, along with accuracy. Precision describes how often messages flagged as phishing are actually phishing in the labeled evaluation set; recall describes how many phishing messages in that set are caught. Accuracy alone can obscure performance when classes are imbalanced or when the two kinds of error have different consequences.
- False positive: a legitimate message is flagged, potentially interrupting work or sending a useful message for review.
- False negative: a phishing message is classified as legitimate, leaving a recipient exposed to the threat.
Choose the threshold for the action you intend to take. A warning or review queue can be a reasonable first step while the model is being assessed; automatic blocking carries different operational consequences. Select the threshold on validation data, then report its results on the untouched test set rather than tuning it on that test set.
Interpret published experiment numbers narrowly
A public GitHub experiment reports results on its own 5,845-record CEAS_08 test set. Its documentation, with publication year not stated on the reviewed page, reports the following:
| Model in the experiment | Accuracy | Precision | Recall | F1 | ROC-AUC |
|---|---|---|---|---|---|
| TF-IDF plus numeric features with Logistic Regression | 0.9926 | 0.9975 | 0.9892 | 0.9934 | 0.9988 |
| DistilBERT | 0.9974 | 0.9982 | 0.9972 | 0.9977 | 0.9999 |
These are results for that project’s dataset and setup, not expected performance on your organization’s email or proof of production readiness. Dataset artifacts, duplicate campaigns, changing attacker language, and distribution shift can all make offline results misleading.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What signals can complement message text?
Phishing detection can use signals beyond the body of an email, but no single signal should be treated as a complete classifier. NIST’s SP 800-177 Rev. 1 recommends SPF, DKIM, and DMARC for sender-domain authentication and discusses TLS and S/MIME for email transmission and content security. These mechanisms provide security context; they do not, by themselves, answer every question about whether a message is phishing.
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Microsoft’s threat-classification documentation describes signals including spoofing, impersonation, and URL reputation. Such signals can complement a model, provided they are available at decision time and evaluated with the same care as text features. A 2024 preprint, “Characterizing the Networks Sending Enterprise Phishing Emails,” found that reputable hosting networks can also carry phishing, which cautions against relying on static network reputation alone. The authors reported that a dynamically updated network-feature classifier identified 3–5% more enterprise email attacks over 4.5 months in a production deployment with an email-security company; that is one study’s result, not a general guarantee.
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How should you deploy and monitor the model?
Begin with a low-impact workflow, such as ranking messages or routing uncertain cases for human review. Use review outcomes to identify errors and emerging patterns, but do not assume reviewer feedback is automatically correct or suitable for retraining without checks.
- Track false positives and missed threats against reviewed or otherwise validated outcomes.
- Monitor calibration—the relationship between confidence scores and observed correctness—as well as changes in input data and attacker tactics.
- Keep a human review path for uncertain cases and define who can change model thresholds or actions.
- Re-evaluate after changes to data, features, preprocessing, or model versions, and use time-separated checks where possible.
- Protect training data, predictions, logs, and review records with appropriate access and retention controls.
Adversarial evasion and data poisoning are lifecycle risks: attackers may try to make malicious messages look legitimate or contaminate data used to train or update a system. NIST’s adversarial-machine-learning report provides a general framework for considering such risks; it is not a phishing-specific deployment recipe.
Is phishing awareness training the same as classifier evaluation?
No. NIST’s Phish Scale was created for professionals evaluating simulated phishing awareness exercises. It rates the human difficulty of detecting a simulated email in context, rather than measuring a machine classifier’s precision, recall, or error rate. NIST states that academic use is available at no cost and that commercial use requires a commercialization license. It may be relevant to a training program, but it is not a replacement metric for evaluating an email-classification model.
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