Detecting suspicious email with generative AI

Email is essential to business communication, but it is also a major channel for cyberattacks. As phishing and other suspicious messages increase, organizations need effective ways to identify and respond to these threats. Generative AI is attracting attention as one approach. This article explains how it can be applied to email detection.

How generative AI supports email detection

Generative AI learns from large datasets to produce new information. Applied to email analysis, it can help identify sophisticated attacks that are difficult for conventional methods to detect.
1. Collecting and preparing data
Large datasets of legitimate and suspicious emails are collected for model training. These include message content, sender information and attachments.
2. Training the generative model
A generative AI model, such as GPT-4, is trained using the collected data. It learns the characteristics of legitimate and suspicious emails and identifies relevant patterns.
3. Generating and evaluating messages
The trained model generates new examples and establishes criteria for distinguishing legitimate from suspicious messages. This process can improve detection of previously unseen suspicious emails.
4. Real-time detection
In operation, newly received messages are analyzed in real time for suspicious elements. The adaptability of generative AI can help teams respond quickly to new attack methods.

Benefits of generative AI

1. Accurate detection
Learning from large volumes of data helps generative AI distinguish suspicious emails accurately, including subtle differences that conventional methods may miss.
2. Adaptability and flexibility
Attack methods change continually. Generative AI can learn new patterns and adapt its detection approach.
3. Immediate response
Messages can be analyzed as soon as they arrive, allowing suspicious emails to be detected and blocked promptly.

Implementation examples

We have implemented generative AI systems for suspicious email detection in a range of projects. Two examples are outlined below.
1. Deployment for a retailer
After the generative AI detection system was introduced, the phishing email detection rate improved by more than 90%.
2. Deployment for a manufacturer
The introduction of generative AI substantially reduced the risk of information leakage from suspicious emails and helped prevent security incidents.

Introducing an event-driven security model

Generative AI email detection can be an effective part of an organization's security measures. Drawing on our experience in this field, we provide solutions tailored to individual requirements. Please contact us if you need help addressing suspicious email.

Further considerations

Serverless security also calls for changes to traditional permissions and authentication practices. Approaches under consideration include automatically assigning each function a principal with appropriate permissions, supporting more flexible and effective access control.