How Artificial Intelligence Can Enable Cybersecurity Education

Artificial intelligence (AI) is a hot topic across many industries, from household devices like vacuums and refrigerators to other essential items like smartphones and cars. This explosion of AI-powered products and services highlights a wide range of use cases — especially in the field of cybersecurity.

Get a clearer understanding of how AI can simplify complex cybersecurity concepts — and discover practical insights and tailored explanations to enhance your knowledge and spark further curiosity.

Making cybersecurity more accessible

Large language models (LLMs) are the AI models most people are familiar with. LLMs like ChatGPT, Google Gemini, and Meta AI can take user questions or prompts about cybersecurity and generate predictive, context-aware responses using millions of back-end calculations and vast amounts of training data.

One of the strengths of LLMs is their ability to explain complex topics in a simple, digestible format. They can transform intricate concepts into approachable explanations, making them especially useful for individuals new to a field or looking to expand their knowledge.

Finding clear, beginner-friendly explanations of key cybersecurity concepts isn’t always easy. Traditionally, people have used resources like the National Institute of Standards and Technology, the Joint Cyber Defense Collaborative, or online forums like Reddit and Stack Overflow to learn about cybersecurity and other technical topics.

While these resources provide valuable information, they can often be too technical, difficult to understand, or overwhelming. A forum post might contain helpful insights, but it may also include excessive information, making it hard to digest and retain.

This is where LLMs come in. They can provide responses ranging from short to detailed or somewhere in between — delivering explanations suited to the learner’s needs.

Learning from AI on your own terms

LLMs can change explanations to fit your background knowledge, making cybersecurity concepts more relatable. Consider the following prompt, which asks ChatGPT to explain the malicious technique of SQL injection to an accountant:

Here, the AI uses an example relating to a tool an accountant may be familiar with (like Microsoft Excel), making the explanation more accessible. While LLM-generated responses may not always be perfect or capture every nuance of a complex topic, they can serve as a solid starting point and encourage further research.

For instance, after learning about SQL injection, an accountant might wonder whether it’s a risk to their organization and whether investing in cybersecurity testing would be beneficial.

They could then ask ChatGPT about the importance of cybersecurity assessments for financial professionals, who may not care about performing security assessments or developing security practices at their organization. The LLM would likely generate a response highlighting the business risks of cyber threats, helping bridge the gap between financial and IT concerns:

This clear explanation and real-world reasoning can help departments with differing objectives understand how their goals are interconnected — not siloed — and how budget constraints against cybersecurity may negatively impact an organization’s public reputation, security, and compliance.

AI is a learning tool, not a replacement

The examples illustrate how AI-powered tools could help newcomers, students, and professionals alike gain a deeper understanding of cybersecurity. The field can be complex, but having access to clear, well-structured explanations tailored to your knowledge level can encourage deeper learning.

However, while LLMs are powerful learning aids, they aren’t foolproof. It’s always important to cross-reference AI-generated information with trusted sources to support accuracy. By combining AI-driven learning with professional insights, organizations and individuals can make more informed decisions about cybersecurity.