Machine learning (ML) is a subset of artificial intelligence that mimics the way humans learn by utilizing data and algorithms. It’s no secret that machine learning is an excellent tool to help assess and prevent cybersecurity threats. With the proper data, it can help companies avoid falling victim to similar attacks by seeking out patterns of the system's behavior. Machine learning also utilizes predictive modeling, which is very effective for detecting attacks while they are happening and can even help companies prevent new attacks.
With all of its functionality, it may seem like machine learning is the key to enhancing your company's cybersecurity. However, there is one critical factor that determines if machine learning will be a useful tool. This critical factor is good quality data because you get out what you put in! When your data quality is bad or incomplete, machine learning cannot provide accurate insights about what is happening in your environment, which will hinder a company’s ability to respond quickly to cyberattacks and/or prevent them from happening.
You can determine the quality of your data set by analyzing it based on these six dimensions of data quality:
Machine learning is already changing the world of cybersecurity and has the potential to enhance it even more. Though it may cost your company more time and money now, obtaining good quality data will give you the necessary foundation to use ML effectively. Only then will your company gain the full range of benefits that machine learning has to offer.
To learn more about how accurate data builds the foundation for machine learning use cases in cybersecurity, including security orchestration and automation (SOAR), join our seminar with CS2AI and Splunk on August 18 at 1 PM EDT.