Our latest cybersecurity study shows that enterprises are reaping the benefits of machine learning (ML) in five key cybersecurity areas:
- False positives: The prevalence of false positives can mean real detections get overlooked due to lack of time and resources. Ninety-one percent of respondents are satisfied, or very satisfied, with the effectiveness of machine learning when it comes to reducing false positives and redirecting resources.
- Classifying security incidents: Classification is the assignment of an appropriate category against each incident for its further routing to the appropriate resolving agency. Classifying security incidents is critical to ensuring correct and timely resolution. Eighty-nine percent of respondents reported a high level of satisfaction in the application of machine learning methods for automated classification and routing of security incidents, allowing security and IT functions to speed up response.
- Cyber risk assessment: Eighty-one percent of cybersecurity executives are satisfied, or very satisfied, with the use of machine learning in cyber risk assessment processes. Machine learning models offer data-driven insights as more data is correlated and analyzed. They enable security teams to reveal patterns in the noise of large data sets, which improves decision-making during risk assessment processes.
- Threat hunting capabilities: Seventy-eight percent of respondents highlighted the impact of machine learning on threat hunting capabilities. By supplementing their existing lagging indicators of compromise (IOC) with leading indicators such as indicators of risk (IOR) and applying machine learning techniques, security teams are able to perform real-time event correlations at scale and at the source.
- Identification of new cyber risks: Seventy-one percent of cybersecurity executives have reported satisfaction with machine learning in the identification of new cyber risks that arise from changing cyber threats or new technologies. Machine learning is enabling security teams to detect previously unknown patterns to uncover new risks fast.
The Bottom Line: ML applied to cybersecurity is no longer hype.
Cybersecurity challenges are continuously increasing in number and scope given the exponential proliferation of data. Gathering, transforming, and making sense of such data is fundamental to effectively running cybersecurity operations. Humans alone cannot handle the sheer amount of threats enterprises face today and we need intelligent solutions such as ML to augment our capabilities. Cybersecurity executives who adopt ML methods and techniques to support cybersecurity operations report high levels of satisfaction.
Don’t get left behind. Start building your ML capabilities today. Malicious actors are actively using it to their advantage, and you should do the same.
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