Cyberattacks are a top priority in most IT organizations; the risk associated with ransomware attacks, data breaches, business email compromise, and supply chain attacks has garnered that significant attention be made to IT resources and budget to address these threats. The challenge in building a comprehensive security strategy designed to prevent attacks is the ever-changing threat landscape.
In recent years, we’ve heard more and more about the use of Artificial Intelligence (AI) and Machine Learning (ML) to help make security efforts more current, effective, and responsive. But every vendor is claiming to use AI/ML today. It can create a ton of buzz and hype, reflecting the “state of the art,” but the question remains does it bring any practical value?
To help clear up the confusion, in this paper, we’ll provide a high-level definition of both AI and ML from a security perspective, as well as how to practically apply the principles of each to domains in an effort to demonstrate how they are used to spot malicious traffic before it becomes a problem.
In this paper we will cover:
- Clear definitions of both artificial intelligence and machine learning
- The value of applying artificial intelligence and machine learning to malicious domains
- Proactive threat predictions and protection with machine learning
