Which security approach helps in reducing false negatives?

A false negative occurs when a security tool fails to identify a genuine threat. Traditional signature-based systems often miss novel or polymorphic attacks. Machine learning (ML) and User and Entity Behavior Analytics (UEBA) establish a baseline of normal activity and can detect subtle deviations indicative of a threat, even without a known signature. This proactive, anomaly-based approach is crucial for identifying previously unseen attacks, thereby reducing the rate of false negatives and improving overall detection efficacy. This is a core principle of modern SecOps platforms like Palo Alto Networks Cortex XSIAM.

B. Ignoring alerts increases the risk of missing true positives and does not address the underlying cause of false negatives (missed detections).
C. Blocking all emails is an extreme, business-disrupting action, not a nuanced detection strategy to reduce false negatives.
D. Manually reviewing every log is not scalable or efficient for reducing false negatives and is highly prone to human error and oversight.

1. Palo Alto Networks. (2023). What is User and Entity Behavior Analytics (UEBA)? Palo Alto Networks Cybersecurity Glossary. "UEBA can detect malicious and abusive activity that would otherwise go unnoticed. By flagging anomalous behaviors, it can help security teams discover threats before they lead to a security incident."

2. Palo Alto Networks. (2023). Cortex XDR Prevention & Response. "Cortex XDR uses behavioral analytics to spot anomalies that indicate attacker activity. It builds a profile of normal endpoint, network, cloud, and user activity so it can quickly find the outliers that signal an attack."

3. Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A. (2018). A detailed look into the CICIDS2017 dataset. In 2018 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE. https://doi.org/10.1109/ICC.2018.8422657 (This paper discusses the use of machine learning and behavioral data for creating datasets to train systems that reduce both false positives and false negatives).