How machine learning and AI make email security more effective

AI is not magic, but it can process signals at a scale humans never will. Here's how ML models improve phishing detection—and how Mailqor channels those insights to the people making decisions.

Behavioral baselines

• ML analyzes historical sender behavior, email cadence, and language to flag anomalies.
• Sudden spikes, unusual tone, or login patterns trigger higher suspicion scores.

Content and intent understanding

• Transformers evaluate urgency phrases, payment requests, and brand impersonations.
• Models spot mismatched logos, forged footers, or AI-written text.

Infrastructure intelligence

• AI correlates WHOIS updates, DNS changes, and hosting fingerprints to detect typosquats faster than manual reviews.
• Passive DNS feeds train classifiers on malicious infrastructure clusters.

Mailqor's AI-powered assistance

• In-inbox summaries explain why a badge is suspicious using natural language.
• Users can ask follow-up questions ("Is this domain new?") and receive structured answers.
• AI recommendations suggest next steps: call the vendor, forward to security, or archive confidently.

Human in the loop

• Mailqor never auto-approves actions; people confirm payments using the AI context.
• Feedback loops improve accuracy: marking false positives trains future detections.

Conclusion: AI amplifies human judgment

Machine learning sifts noisy signals, while Mailqor packages the results for exact teams who must act. Together, they shrink detection time and make phishing far less profitable.

FAQ

Does AI replace training?
No. AI augments decisions but employees still need awareness.

Is my data used to train public models?
Mailqor uses isolated models and never feeds customer data into public LLMs.

Can we disable AI features?
Yes, admins can limit access or require additional approvals.