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 let's understand the differences between the two terms as they pertain to cybersecurity and how organizations can best find value in AI implementation.
Predictive AI can be used to analyze historical attack vectors and current trends to infer future attack methods.
Both predictive AI and generative AI are built on machine learning, a technology that learns by identifying patterns from data.
Generative AI requires massive datasets for its training.
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.
A generative AI trained on certain types of malware samples could generate new strains of malware that have not been seen before.
There are multiple use cases of GenAI in cybersecurity.
Content generated by GenAI models can be used to train predictive AI algorithms and augment existing cybersecurity datasets.
Predictive AI can be used for use cases such as detecting anomalies, automating repetitive security tasks, delivering autonomous responses to threats in real-time, simulating adversary movements, and predicting the maintenance of security systems.
Predictive AI on the other hand is a lot more interpretable and trustworthy because it is based on statistical techniques that are easier to interpret, understand and analyze.
Below are practical recommendations to get the most value from a generative or predictive AI project.
Ensure Highest Quality Data: The output of the generative or predictive model will only be as good as the integrity of the data on which it's been trained.
Be Realistic: Generative AI may seem human-like, but it still lacks human-level capabilities.
Generative for Small-scale / Predictive for Large-scale: To deliver better user interactions or experience, to train people, configure security systems, or report security performance using natural language, GenAI may be the best choice.
For security items that require predictive and investigative intelligence such as who did what, which systems to isolate or block, what is high-priority, and what is vulnerable or suspicious - answers to these large-scale security challenges will almost always come from predictive AI. 6.
Hire Expert Consultants: A skills shortage in both AI and cybersecurity can derail any plans or hamper efforts.
To summarize, there are many reasons why AI is so compelling as a tool for cybersecurity.
To realize AI's full potential, businesses should understand the nuanced differences in AI technology, define AI objectives and scope more clearly, strive for quick wins, and seek external help when needed.
Organizations must define the specific cybersecurity outcomes they wish to achieve from AI. Recent Articles By Author.
This Cyber News was published on securityboulevard.com. Publication date: Sat, 29 Jun 2024 09:13:06 +0000