What is generative AI (GenAI)?

Generative AI, also known as GenAI, is a branch of artificial intelligence that focuses on generating new data based on existing data. This sophisticated technology enables a variety of use cases — such as data retrieval and analysis, content generation, and summarization — across a growing number of  applications.

Generative AI also has applications within the context of cybersecurity, from assisting threat hunters with data retrieval for ongoing investigations to providing real-time insights that inform vulnerability management workflows. In this post, we’ll look at the integral role of generative AI in contributing to a robust cybersecurity posture.

How generative AI works: a brief overview

Generative AI comes out of a subset of AI known as machine learning (ML). ML involves the use of algorithms that improve automatically by learning patterns from vast amounts of data. Among the various domains of ML is deep learning, which uses layered algorithms (called neural networks) that mimic the way neurons function in the human brain. This enables systems to learn and make decisions autonomously.

Within deep learning, we have a type of design for a neural network called a transformer. The transformer model uses layers of artificial neurons that analyze input data in parallel, making the process incredibly efficient. One of the most well-known of these models is the Generative Pre-Trained Transformer model, commonly known as GPT. Pre-trained on large amounts of data, these models can generate eerily human-like text.

Put simply, generative AI involves the following steps:

• The model begins training on a massively large dataset
• The model learns and understands the underlying patterns and structures in the data
• The generative process enables the creation of new data that mimics these learned patterns and structures

With this understanding in place, let’s shift our attention to consider the role that generative AI has in the realm of cybersecurity.