What makes Paubox's generative AI different from others

Paubox’s generative AI offers a secure email solution for organizations seeking a cybersecurity option tailored to one of their most vulnerable outputs. One Issues in Information Systems study notes that “the healthcare sector is increasingly targeted by sophisticated phishing emails generated by Large Language Models (LLMs) and advanced online tools.” 

It uses LLMs, vector databases, and generative AI to detect nuanced phishing threats, including AI-generated attacks like fake invoices and business email compromise (BEC). It integrates seamlessly with Microsoft 365, Outlook, and Google Workspace, offering full-spectrum defense alongside features like ExecProtect+ for lookalike domain prevention, virus scanning, Paubox Tags for sender authentication, and Transcription for audio attachments.

With Paubox, patient data remains isolated, models continuously improve through user feedback aligned to healthcare workflows, and integrate cleanly with Microsoft 365 and Google Workspace without manual rule tuning.

The mainstream generative AI market

As it stands, the mainstream generative AI market is dominated by diffusion models for image and video creation, large language models such as ChatGPT and Gemini for text generation, and multimodal systems that combine text, images, and audio. Market estimates from ‘Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review’ place the sector’s value at nearly $17 billion, with growth projected at roughly 37.6% annually through 2030. 

There's a change from early proof-of-concept tools to widely deployed systems, particularly where generative AI is now used for synthetic medical imaging, automated clinical documentation, and decision-support pipelines built on retrieval-augmented generation and multi-agent workflows. Proprietary models continue to dominate performance benchmarks.

What Paubox’s generative AI offers 

Context-aware analysis

The system looks beyond keywords and static rules to evaluate how an email actually behaves. It examines tone, sender behavior, message intent, and past communication patterns to establish what normal looks like inside a specific organization. When something falls outside that baseline, the system flags it, even if the email appears legitimate at first glance.

Continuous learning

Accuracy improves over time as the system learns from how users interact with flagged messages. When security teams mark emails as safe or malicious, those decisions feed back into the model, reducing false positives and sharpening detection without requiring constant manual tuning. Defenses strengthen naturally as attackers change tactics.

Transparency features

Every alert comes with clear reasoning. Security teams see confidence scores, supporting evidence, and plain-language explanations that show why a message was flagged. Access to mail logs and historical patterns makes it easier to validate decisions and respond quickly, avoiding the black box problem common in traditional security tools.

The architectural differences between the two 

Most generative AI systems rely on massive transformer models trained on internet-scale data, optimized to generate text, images, or multimodal outputs across countless use cases, with reinforcement learning used to keep responses generally aligned. 

Paubox takes a very different approach. Its models are tuned specifically for email security, breaking messages into semantic embeddings and storing them in vector databases that reflect how an organization normally communicates. Tone, sender behavior, intent, and historical patterns form a living baseline. According to a Journal of Medical Internet Research study, “the exact method by which such tools prioritize information sources remains unclear.”

That focus sets Paubox apart from general architectures that emphasize retrieval-augmented generation for factual recall or diffusion models for image synthesis. Instead of chasing broad content creation, Paubox trains on rare and high-risk attack scenarios, including AI-generated business email compromise and fraudulent invoices, to close gaps in model knowledge. Addressing concerns raised in the study that generative systems often “paraphrase previous articles instead of reviewing the primary source,” compounding risk. 

General-purpose AI tools often struggle with opacity and hallucinations caused by overgeneralization. A limitation the authors warn is “particularly problematic in the context of biomedical research, where evidence quality may influence clinical practice and decision-making.” Paubox addresses that risk by pairing every detection with clear explanations, confidence scores, and supporting evidence drawn from mail logs and historical context. 

Rather than requiring enormous compute resources to do everything, Paubox uses a lean, domain-specific stack that integrates cleanly with Microsoft 365 and Google Workspace and works alongside existing protections such as ExecProtect+ and Paubox Tags.

Where Paubox AI outperforms its competitors 

Paubox outperforms its competitors by focusing on a single, high-risk problem, HIPAA compliant email security in healthcare, and engineering its technology around that reality rather than trying to be a general-purpose AI platform. It doesn’t just depend heavily on rules, signatures, and reputation lists. 

Paubox analyzes how emails behave within a specific organization, learning normal communication patterns and identifying subtle anomalies that indicate phishing or impersonation. This approach allows Paubox to detect sophisticated, AI-generated attacks such as business email compromise and brand spoofing that routinely bypass legacy filters, while keeping false positives low enough for clinical environments where missed or delayed emails can disrupt care.

FAQs

How does generative AI detect phishing emails?

It evaluates contextual signals such as sender behavior, language patterns, and historical communication norms to identify suspicious messages, including previously unseen attacks.

How does generative AI reduce false positives?

By learning what normal communication looks like inside an organization, generative AI avoids flagging legitimate emails that would trigger traditional rule-based filters.

What types of attacks can generative AI identify?

It can detect advanced threats such as business email compromise, domain spoofing, brand impersonation, and AI-generated phishing messages.