Understanding False Positives
While it is crucial to err on the side of caution, the prevalence of false positives can have significant ramifications for cybersecurity teams and overall organizational efficiency. A false positive occurs when a security system incorrectly identifies benign activity as malicious. A cybersecurity system like an Endpoint Detection and Response (EDR) platform or a Secure Email Gateway (SEG) flags an activity as a potential threat based on predefined rules, patterns, and algorithms. Due to the ever-changing and complex nature of cyber threats these rules and patterns are not foolproof. Many rely upon an updated catalog of known threats, leaving security teams dependent on information outside of their control. The National Vulnerability Database, for example, is so inundated with new threats that 75% of vulnerabilities submitted in 2024 have yet to be processed.
Consequences of False Positives
Resource Drain – Investigating false positives requires time and effort. Security teams often need to manually inspect and validate each alert, a time-consuming process. This diverts resources away from investigating genuine threats and proactive security measures.
Alert Fatigue – When security personnel are bombarded with false positives, they may become desensitized to alerts. This alert fatigue can cause legitimate vulnerabilities to be missed due to the sheer volume of flagged files to process.
Operational Disruption - Frequent false positives can lead to unnecessary disruptions in business operations. For example, when a legitimate file is flagged as suspicious business productivity slows as the security team works through the more recent alerts before realizing there is no real cause for suspicion.
Reduced Trust in Security Systems – Over time, a high rate of false positives can erode trust in cybersecurity systems. Security personnel might start to ignore alerts, assuming they are false, undermining the effectiveness of their organization’s security infrastructure.
Causes of False Positives
Several factors contribute to the prevalence of false positives:
Overly Sensitive Detection Rules - Security systems with highly sensitive detection rules are more likely to flag benign activities as threats. While this sensitivity can help in detecting new or evolving threats, it also contributes to a greater alert workload.
Lack of Context - Many security systems operate without the full context of user behavior and organizational norms. Without this context, distinguishing between normal and abnormal file behavior becomes challenging.
Evolving Threat Landscape - The constantly changing nature of cyber threats means that detection rules need to be continuously updated. Maintaining this pace can be difficult, leading to outdated rules that misclassify activities.
Mitigating False Positives
Addressing the issue of false positives requires a multi-faceted approach:
1. Improving Detection Algorithms: Advanced machine learning and artificial intelligence can enhance the accuracy of threat detection systems. By learning from historical data and contextual information, these systems can better differentiate between legitimate and malicious activities.
2. Tiered Alerting Systems: Implementing a tiered alerting system can help prioritize alerts based on their severity and likelihood of being true positives. This approach allows security teams to focus their efforts on the most critical alerts first.
3. Regular Updates and Tuning: Continuously updating and tuning detection rules based on the latest threat intelligence can help minimize false positives. Security teams should routinely review and refine these rules to adapt to the evolving threat landscape.
The CodeHunter Solution
ISC2 notes that only 52% of cybersecurity professionals believe that their organization has the tools and people needed to respond to cyber incidents over the next 2 to 3 years. That’s not good news for security teams already struggling to keep up with the daily warnings generated. So, what can be done to make the influx of alerts more manageable?
It’s no secret that having an active cybersecurity defense system is necessary to protect organizations from rampant cyber threats. Platforms like SentinelOne scan company environments at scale, running pattern-matching algorithms with rules informed by publicly known threats, threat actors, and their tendencies. Unfortunately, this abundance of caution comes with an abundance of alerts, far more than the typical security team can handle. That’s where CodeHunter comes in. CodeHunter's threat hunting engine automatically analyzes flagged files at scale and at speed, producing actionable intelligence in a fraction of the time it takes to manually reverse engineer malware. CodeHunter’s SentinelOne integration relieves security teams of the burden of investigating every warning to the fullest, supplying in-depth analysis to support timely response and remediation processes. Because CodeHunter doesn’t rely on pattern matching to identify malware, it properly assesses alerts raised by other systems to determine if the behavior is actually suspicious or just a false positive caught by an overly sensitive algorithm.
Learn how CodeHunter can maximize your SentinelOne investment by minimizing false positives here.
