Generative AI can spot the subtle patterns that often signal malicious behavior. Instead of relying only on fixed rules, these systems look at the full picture, email headers, message content, and attachments, to spot threats before a user ever opens a file. Deep-learning models automatically pick up on hidden clues in a file’s structure and behavior that traditional tools often miss.
As a Scientific Reports study explains, “the emergence of transformer-based embeddings, multi-head attention, and sequential modelling enables detection systems to capture nuanced contextual cues that simple rule-based filters often overlook.”
What makes this approach so valuable is its ability to catch modern threats, including fileless and attachment-based malware that slips past signature-based scanners. According to the same research, the model achieved over 97% precision and 95% recall in distinguishing phishing emails.
Since many attacks still begin with a seemingly harmless attachment, AI models trained on large collections of both safe and malicious files can recognize danger even when there’s no known fingerprint to match.
Email attachments face a wide and constantly changing range of threats, many of which start with phishing attacks designed to fool people into opening what looks like a perfectly legitimate file. Attackers often clone internal emails and attach files that persuade staff to enable macros or run embedded scripts. They quietly set off malware that steals credentials or opens the door to deeper system access.
As the study ‘Prevention and mitigation measures against phishing emails: a sequential schema model’ notes, phishing is basically “the use of unsolicited email…purportedly from a legitimate company requesting personal, financial, and/or login credentials,” a definition that captures just how easily trust can be exploited in everyday inboxes.
Email is still the most common way malware gets into an organization, with attachments disguised as Word documents, PDFs, ZIP files, or embedded objects. Once opened, these files can install remote-access tools, keyloggers, downloaders, or ransomware in a matter of seconds.
Generative AI refers to a class of systems that learn patterns from massive amounts of data and then use those patterns to create new material, whether that’s text, code, or images, that looks authentic even though it’s entirely synthetic.
As one recent Frontiers in Artificial Intelligence study puts it, “Generative Artificial Intelligence marks a critical inflection point in the evolution of machine learning systems, enabling the autonomous synthesis of content across text, image, audio, and biomedical domains.” When applied to email security, generative AI can strengthen detection systems by producing realistic but artificial phishing and legitimate messages that expand training datasets.
This helps deep-learning models become better at telling the difference between what’s safe and what isn’t. Large language models also add another layer of protection by examining the tone, wording, and context of messages, spotting the subtle cues behind spear-phishing, business email compromise, and other socially engineered scams that often slip past traditional filters.
According to one journal article from the PeerJ Computer Science, “Deep learning techniques can automatically extract effective features from emails, eliminating the need for labor-intensive email feature extraction. Thus, they are able to capture a more thorough and comprehensive representation of information within the email text.”
Building on this foundation, platforms like Paubox’s generative AI for inbound email security apply these same advances in real-world environments, analyzing not just fixed indicators of compromise but the full context of every message.
Instead of relying only on fixed rules or known malware signatures, modern AI systems can examine an entire message, its headers, text, and attached files, to spot patterns that suggest something isn’t right. Phishing detection shows that these models learn to recognize warning signs such as hidden scripts, unusual file structures, or suspicious encoding methods that attackers often use to slip past traditional filters.
Training AI on large collections of both safe and malicious files helps it notice subtle red flags in attachment metadata, embedded macros, and content behavior, clues that can point to ransomware, credential-stealing forms, or malware installers. Generative techniques take this a step further by creating realistic but harmless versions of attack files, giving security systems more examples to learn from and making them better prepared for new or heavily disguised threats.
One of the biggest advantages of using generative AI to secure email attachments is that it helps systems stay ahead of new and highly evasive threats. The tools learn from large and varied sets of data and use that knowledge to anticipate how attacks might change.
As the study ‘Generative AI and LLMs for Critical Infrastructure Protection: Evaluation Benchmarks, Agentic AI, Challenges, and Opportunities’ notes, “there is great potential for Critical Infrastructure Protection (CIP) when cutting-edge technologies like Large Language Models (LLMs) and Generative AI are integrated,” highlighting how these advanced models can go beyond simple, rule-based detection to understand deeper patterns in data that signal malicious intent.
When AI is trained on both safe and malicious files, it becomes much better at spotting the small but telling signs of danger, things like hidden code, heavily disguised scripts, or subtle changes in a document’s structure that point to tampering.
Generative AI goes a step further by creating realistic examples of harmful files, such as altered PDFs or infected documents, to strengthen training data. This means security systems are not limited to yesterday’s attacks but are better prepared for zero-day threats, constantly changing malware, and fileless techniques that rely on attachments as their entry point.
The paper describes how researchers are now using benchmarks and evaluation strategies to “assess the cybersecurity capabilities of LLMs,” showing that these models are being tested against a wide range of security This leads to smarter, more flexible defenses at email gateways and endpoints, where suspicious files can be flagged, tested, or blocked before they cause harm.
There’s also a practical benefit for security teams. By automating parts of threat modeling and testing, generative AI reduces the manual workload for analysts and allows them to focus on higher-level decisions.
See also: HIPAA Compliant Email: The Definitive Guide (2025 Update)
No, traditional AI mainly focuses on classification and prediction, while generative AI specializes in creating new material.
It doesn’t replace creativity but acts as a tool that supports and improves human ideas and productivity.
Generative AI can be safe when it is deployed with proper security controls, ethical guidelines, and privacy protections.