Patented Graymail Detection and Remediation
Discovering Graymail Through Real-Time Analysis of Incoming Email
Abnormal has patented a smarter way to detect and manage graymail. Unlike traditional filters that rely on static rules, Abnormal’s solution uses intelligent behavior profiling to identify graymail—those low-priority, non-malicious emails like newsletters and promotions that clutter inboxes. By analyzing user-specific preferences and patterns, the system determines when a message should be treated as graymail and takes automated action to reduce noise.
The diagram above illustrates how Abnormal's system identifies and handles graymail using AI and automation. It analyzes each message, determines if it’s graymail, and if so, uses a remediation service to move it to a designated folder. If the folder doesn’t exist, it creates one and updates the system’s cache for future use—keeping everything fast and efficient.
How Our Unique Patented AI Detects Graymail
Abnormal’s patented AI technology uses advanced machine learning models to intelligently detect graymail. Trained on vast datasets of real-world graymail, the system evaluates each message against a dynamic, AI-generated behavioral profile tailored to each user.
Rather than relying on rigid rules, the AI continuously learns from user preferences and interaction patterns to distinguish between wanted and unwanted content. This allows the system to make accurate, real-time decisions—automatically categorizing graymail and taking appropriate actions, such as moving messages to a dedicated folder. The result is a more efficient, adaptive email experience that evolves with your organization.
Why This Matters
Graymail may not be harmful, but it clutters inboxes, buries important messages, and drains productivity. Traditional filters can’t keep up, often relying on static rules that don’t reflect individual user preferences. Abnormal’s AI-powered system learns each employee’s individual communication patterns to accurately identify graymail and filter it out—delivering cleaner inboxes, greater focus, and improved productivity across the organization.
Meet One of the Inventors
Abhijit Bagri
Abhijit Bagri is a Founding Engineer and Chief Architect at Abnormal AI, where he leads the design and development of the company’s core detection systems. With deep expertise in large-scale software engineering and cybersecurity, Abhijit brings over 15 years of experience from industry-leading companies like Twitter, TellApart, Nokia, and Yahoo!.
Driven by a belief that security must adapt to user behavior, Abhijit pioneered a patented system that uses machine learning to detect subtle anomalies in email communications—identifying potential threats by scoring deviations from established behavioral patterns. This breakthrough technology is now protected under U.S. Patent No. 11,683,284.
Abhijit’s work has been instrumental in shaping Abnormal’s AI-native architecture, enabling more accurate threat detection with less noise. His technical leadership continues to advance the frontier of behavioral email security—empowering organizations to stop modern attacks before they reach the inbox.
The Patented Real-Time Graymail Detection Process
- 1. Identify Graymail Using ML Models:: The system checks a specific message and uses machine learning models—trained on examples of graymail—to determine if the message is graymail and categorizes it (e.g., newsletters, promotions).
- 2. Take Remedial Action:: If the message is graymail, the system takes an action, like moving it to a different folder or marking it as low priority.
- 3. Monitor User Response and Update Rules:: Later, if the employee interacts with the message (like opening or replying), the system notices this behavior and adjusts its rules to improve future decisions—making it smarter over time.
