AI Phishing Detection: Research Trends and Practical Limits
A high-level look at ML, deep learning, and transformer trends in phishing detection, with practical limits and honest positioning.
Research explores many signals
Phishing detection research often studies URL features, page content, sender context, screenshots, text, and behavior. These approaches can help classify and prioritize suspicious links.
Machine learning needs careful data
Models depend on training data, labels, evaluation methods, and changing attacker behavior. A model that works in one dataset may not generalize perfectly to new campaigns.
Transformers can help with language and context
Modern models can summarize messages, compare text patterns, and assist review. They still need guardrails, explanations, and human oversight.
Practical limits remain
Attackers adapt. New pages appear quickly. Legitimate links can look strange. False positives and false negatives are unavoidable risk-management problems.
What CheckLink claims today
CheckLink currently emphasizes explainable URL/domain signals and manual review. ML-assisted review is a planned signal, not a current guarantee.
Checklist
FAQ
Does AI guarantee phishing detection?
No. AI can support detection and triage, but it cannot guarantee every phishing link is caught.
Does CheckLink claim production ML detection?
No. Current CheckLink positioning is based on available signals and manual review paths.
Related guides
Related glossary terms
Use CheckLink before the next click
CheckLink provides risk signals and review paths. It does not guarantee that a website is risk-free.