Artificial intelligence has grown rapidly in its influence of many products and services we take for granted today. Think about things such as self-driving vehicles, medical imaging, speech recognition, or even optimization of your playlist. AI is involved in all of those things, but it’s absent from one important area to cybersecurity and IT teams: enterprise vulnerability management.
Technology in the vulnerability management community has, to this point, evolved little since the field’s initial days. Given the abundance of rich and historic data, multi-dimensional risk elements, and a heretofore brute-force approach to remediation, the vulnerability management field appears ripe for AI exploitation.
Defining AI
Before undertaking a discussion of AI’s potential in vulnerability management, let’s first take a moment to define “artificial intelligence.” The phrase is used ubiquitously, but not always accurately.
AI is an umbrella term that encompasses several areas of advanced computer science, everything from speech recognition to natural language processing, to robotics, to symbolic and deep learning. AI technologists are constantly striving to automate seemingly “intelligent” behavior, or put differently, programming computers to do historically human tasks.
One AI component used extensively in many applications is machine learning: algorithms that leverage historical data to make predictions or decisions. The more ample the historical data, the higher the probability the prediction will be useful or accurate. As more historical data is gathered, the machine learning engine’s predictions improve, or in the vernacular of pop culture, the application “gets smarter.” For example, a machine learning-based application identifying the probability of lung cancer from an X-ray can make a prediction from a historical data set of 10 X-rays, but that prediction’s accuracy will be negligible. As the historical data set expands from 10 to 10,000, the prediction becomes more ..
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