How do you leverage AI or machine learning to enhance your phishing resilience?

2 Use machine learning to train employees

Another way to use machine learning to enhance your phishing resilience is to use a training platform that can simulate phishing scenarios and test your employees' responses. Machine learning can generate realistic and customized phishing emails based on your industry, company, and employee profiles. It can also track and measure your employees' performance, feedback, and improvement over time. For example, machine learning can send different types of phishing emails to different employees based on their role, level, or previous behavior.

3 Use AI to monitor network activity

A third way to use AI to enhance your phishing resilience is to use a network monitoring system that can detect and respond to phishing attacks in real time. AI can use behavioral analysis, threat intelligence, and incident response to identify and block suspicious network activity, such as unauthorized access, data exfiltration, or credential theft. For example, AI can alert you if a user logs in from an unusual location, downloads a large amount of data, or changes their password frequently.

4 Use machine learning to analyze data

A fourth way to use machine learning to enhance your phishing resilience is to use a data analysis tool that can extract insights and patterns from your data and help you improve your security posture. Machine learning can use data mining, clustering, and classification to identify and segment your data sources, users, and assets and assess their risk levels, vulnerabilities, and compliance. For example, machine learning can help you discover which data is most valuable, which users are most targeted, and which assets are most exposed.

6 Use machine learning to innovate solutions

A sixth way to use machine learning to enhance your phishing resilience is to use an innovation tool that can create solutions that are novel, adaptive, or scalable. Machine learning can use deep learning, neural networks, and generative models to learn from data and generate solutions that are not predefined or limited by human knowledge. For example, machine learning can help you create new ways of detecting, preventing, or recovering from phishing attacks that are more effective, efficient, or resilient.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?