Generative AI vs. Predictive AI: A Cybersecurity Perspective

In the context of cybersecurity, AI promises considerable benefits however there’s still a lot of confusion surrounding the topic, particularly around the terms generative AI and predictive AI. Given the high failure rate for AI projects (80%) let’s understand the differences between the two terms as they pertain to cybersecurity and how organizations can best find value in AI implementation.

1. Generative vs. Predictive AI: Different Goals, Different Applications

Predictive and generative are apples and oranges. The word “generative” isn’t a reference to something specific in terms of the technology; it’s just how you’re using it, which is to generate new content. GenAI can be used to create complex passwords or encryption keys and draft targeted phishing emails for training purposes. In contrast, the word “predictive” refers to the ability to predict future events or behaviors based on historical data. Predictive AI can be used to analyze historical attack vectors and current trends to infer future attack methods.

2. How Generative and Predictive AI Train

Both predictive AI and generative AI are built on machine learning (ML), a technology that learns by identifying patterns from data. Generative AI requires massive datasets for its training. It then kicks off a process to identify and understand the underlying patterns, structures and relationships in that data. Once model training is complete, new data is generated based on the understanding of those patterns and relationships. Predictive AI on the other hand needs historical data (past cyberincidents, vulnerabilities, user behavior). It also needs both positive and negative examples (of data) as part of its learning process. This process is referred to as supervised machine learning because the learning process is supervised by humans by way of labeling the answers.

3. The Power of Output

Generative AI as the name suggests is used to “generate” new content. However, the output of GenAI may not always match the input. In other words, the output doesn’t need to be always in an identical form or format as the input. A generative AI trained on certain types of malware samples could generate new strains of malware that have not been seen before. Predictive AI “predicts” the probability of an event occurring. Therefore, the technology can be commonly used for tasks relating to predictions or forecasts such as the likelihood of a certain type of attack occurring, the probability that an insider is dangerous, and the possibility that a system can be exploited or compromised.