False positives in cybersecurity

How do false positives differ from false negatives?

False negatives are arguably more dangerous than false positives. A false negative occurs when a security tool fails to detect a genuine threat, allowing a malicious actor to operate undetected on the network. While false positives create noise and alert fatigue, false negatives represent a critical blind spot that can lead to a breach.

The core difference between a false positive and a false negative is the outcome. A false positive is an alert on a non-threat, leading to wasted time. A false negative is a missed threat, leading to a potential breach. Security teams must strike a balance between a high volume of false positives and the risk of a false negative.

The hidden costs of false positives

While a single false positive is a minor inconvenience, a constant barrage of them can cripple a security team. The costs are far greater than just a few wasted minutes.

1. Analyst burnout and alert fatigue: SOC analysts facing hundreds or thousands of daily alerts, most of which are false positives, can quickly become desensitized. This scenario leads to fatigue, and analysts might miss or ignore a real threat simply because they're overwhelmed–a major factor in SOC turnover and talent drain.
2. Wasted time and resources: Every false positive requires an analyst to triage, investigate, validate, and dismiss an alert or detection. This takes time away from investigating true threats, proactive threat hunting, detection engineering, and incident response–a direct financial cost to the business.
3. Increased risk of breach: The most significant cost is the risk of marking an alert as a false positive, when it was in fact a real attack. An analyst suffering from alert fatigue is more likely to accidentally dismiss a legitimate alert as just another false positive, leaving the organization exposed to a breach.
4. Reduced confidence in tools: When security tools consistently generate noise, the team loses trust in the technology. This erodes confidence in the entire security stack and can delay response times during a real incident.

Why do false positives happen?

False positives are a symptom of a deeper problem: poorly tuned tools, security tools that lack the necessary context and data fidelity to make accurate judgments.

• Overly broad or poorly tuned rules: Many security tools rely on signature-based detection rules. If these rules are too broad or not tuned for a specific environment, they will inevitably flag legitimate activity. An organization might use an IDS signature for a known attack, but if a routine internal process mimics that behavior, it will trigger an endless stream of false positives.
• Lack of contextual data: Low-fidelity data sources, such as traditional firewall logs or NetFlow, only provide a high-level view of network activity. They can tell you that a connection happened, but they can't always tell you the user, the application, or the full context of the transaction. Without this information, it's impossible to make an accurate judgment.
• Changes in systems or environment: Legitimate changes in your network—such as a new application deployment, a software update, or an IT script—can trigger a flood of new false positives. Security tools that don't adapt to these changes will continue to raise alarms until their rules are manually updated.
• Static signatures: Many security tools rely on a static library of signatures. An advanced threat actor can easily change their tactics to evade these static detections, but benign, dynamic network activity can still trigger them–a key reason why detection engineering is critical.
• Over reliance on a single detection method: The problem of false positives is often compounded when an organization relies heavily on a single detection method, such as signature-based or anomaly detection. No single tool or method is perfect, and each has its own blind spots. An over-reliance on one approach can lead to a security team being overwhelmed by alerts for benign activities that slip through the cracks of a narrow detection scope.

Steps you can take to reduce false positives

Combating false positives requires a proactive approach to detection. Here's a practical checklist to help your team reduce noise and increase confidence in your security tools.

1. Implement a multi-layered detection strategy: Avoid over-reliance on a single type of detection. By combining different methodologies—like signature-based rules, behavioral analysis, and threat intelligence—you can create a more comprehensive security posture. This approach allows each detection method to compensate for the inherent gaps of another, increasing the overall confidence in an alert. When an alert is triggered by more than one independent detection method, its legitimacy is significantly higher.
2. Prioritize high-fidelity data: Move beyond low-fidelity sources like NetFlow and firewall logs. Invest in tools that provide rich, contextual network evidence like Zeek® logs.
3. Tune your rules regularly: Continuously review and refine your detection rules and signatures. Regularly audit your most common alerts and suppress or adjust the rules that lead to false positives.
4. Use behavioral analysis: Supplement signature-based detections with machine learning and behavioral analysis. These methods can help identify anomalous activity that traditional rules might miss.
5. Establish a feedback loop: Create a streamlined process where analysts can easily provide feedback on false positives. This feedback is critical for informing detection engineering and rule tuning. To make this process more effective, ensure your team understands the value of red and purple team exercises for testing and validating your detection rules against real-world attack simulations.
6. Focus on detection engineering: Dedicate time and resources to creating custom detections tailored to your specific environment. This allows you to hunt for specific threats and reduce generic alerts.
7. Leverage contextual enrichment: Ensure your security tools are integrating with other sources (like threat intelligence, asset management, and user data) to provide a complete picture for every alert and correlation for higher confidence in alerts.

How Corelight Open NDR can help reduce false positives

Corelight Open NDR does not rely on a firehose of simple alerts; instead, Corelight provides rich, high-fidelity network evidence in the form of enriched transaction logs.

These logs provide more detail than high-level NetFlow, yet are more concise than raw packet capture (PCAP). Corelight’s approach transforms low-fidelity data into network evidence by correlating events and adding relevant context, such as user, application, and device information.

By providing this transparent data, Corelight empowers detection engineering—the practice of developing and deploying effective threat detection logic. Corelight is built on the open-source framework Zeek and provides transparent, high-fidelity data that isn’t a "black box." Moving beyond “black box alerting” to enriched network evidence allows your team to understand exactly why an alert was triggered–facilitating response.

For example, a traditional IDS might fire an alert because of a suspicious-looking connection. Corelight would not only create a notice log, but would also provide a rich Zeek log of the entire transaction. This log would include the timestamp, source and destination IPs, protocols, and, most importantly, the application and user context. An analyst can then quickly review this log to determine if the activity was a legitimate administrative task or a true threat, reducing investigation time from hours to minutes. Read more about the differences between NDR vs. IDS here.

Here are a few ways an NDR solution like Corelight helps:

1. Network evidence: Corelight provides rich, high-fidelity network evidence, not just alerts. This allows analysts to pivot from an alert to the full context of the network transaction to rapidly confirm or dismiss a false positive.
2. Multi-layered detections: By combining signatures, behavioral analysis, and threat intelligence, Corelight provides multi-layered detections that increase confidence in an alert's legitimacy. This approach leads to more deterministic detections where multiple indicators validate a single event, drastically reducing the chances of a false positive.
3. Explainability of detections: Corelight provides full explainability behind its detections, allowing analysts to understand the exact conditions that triggered an alert. This transparency allows security teams to confidently tune rules and suppress noise without the risk of missing a genuine threat.

Reduce false positives with quality network evidence

False positives are more than just an annoyance; they are a persistent and costly problem that can obscure real threats and exhaust security teams. The solution isn't to get rid of every alert, but to elevate the quality of your detections by leveraging high-fidelity network data. By adopting an NDR solution that provides transparent, contextual evidence, you can transform your security from a reactive, alert-driven process to a proactive, evidence-based one. This empowers your team to hunt for and respond to the threats that truly matter, with the confidence that comes from working with the ground truth.

The most effective way to combat false positives is to leverage high-fidelity data that provides the context needed for confident detection and response. Ready to reduce false positives and build an evidence-based security posture? Learn how Corelight’s Open NDR Platform can transform your security operations.