PhishGuard AI inspects suspicious emails using high-speed Scikit-Learn classification algorithms, paired with Gemini 2.5 Flash to deliver instant contextual threat explanations.
Paste any raw email message or header payload below to execute instant ML feature evaluation.
Submit email text on the left console to trigger instant model evaluation.
Calculating Scikit-Learn feature metrics & Gemini threat context...
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Combining rapid statistical text classification with generative AI explanations for defense in depth.
Extracts N-gram frequency matrices from raw text payloads to identify linguistic manipulation, urgency tropes, and suspicious domain patterns.
Executes sub-10ms risk classification trained on benchmark phishing datasets, generating a calibrated probability score from 0 to 100.
Translates classification features into plain-English threat breakdowns, highlighting specific social engineering triggers and remediation steps.
Explore the underlying synthetic training data, published Kaggle model pipeline, and production application source code.
Custom-generated dataset containing over 100,000 structured email samples engineered for binary text classification, urgency keyword analysis, and phishing detection algorithms.
The fine-tuned Scikit-Learn classification model card and interactive training notebook, featuring model evaluation metrics, confusion matrices, and TF-IDF feature weights.
The full-stack Flask application source code, API backend logic, Gemini 2.5 Flash orchestration, dynamic fallback engine, and serverless Vercel configuration files.
Everything you need to know about PhishGuard's machine learning model, Gemini integration, and privacy guarantees.
Here is how PhishGuard AI is safeguarding security teams, analysts, and organization endpoints.
"The hybrid architecture is brilliant. Having Scikit-Learn handle rapid scoring while Gemini generates plain-English threat summaries saved our SOC team hours during investigation triage."
"Zero-day spear phishing used to pass right through our legacy filters. PhishGuard's contextual reasoning engine catches subtle urgency tropes instantly."
"Sub-15ms classification latency is no joke. Integrating this pipeline into our live email gateway yielded zero noticeable slowdown."
"The explanations provided by Gemini 2.5 Flash give actionable insights directly to our employees without technical jargon."
"The hybrid architecture is brilliant. Having Scikit-Learn handle rapid scoring while Gemini generates plain-English threat summaries saved our SOC team hours."
"Zero-day spear phishing used to pass right through our legacy filters. PhishGuard's contextual reasoning engine catches subtle urgency tropes instantly."