After your annual physical, your doctor sends you to complete some routine bloodwork. Instead of scheduling the appointment, you head to the lab, wait in line, and give the blood. When the results come back, several are above the normal thresholds. Worried, you call your doctor. The first question she asks you is, “Did you fast before getting the blood drawn?” As you smack your forehead and say, “no”, you realize that these testing outliers are false positives because you needed to fast before going to the lab.
In cybersecurity, false positives are a plague upon the security team. When a security tool’s detection engine identifies potential threat activity, it generates an alert. However, sometimes it fails to distinguish between legitimate behavior that resembles malicious activity and true security threats. False positives occur when the security solution incorrectly triggers a security alert for benign activity, like suspicious files, unusual user behavior, or unexpected network activity.
While occasional false alarms happen, high volumes of false positives contribute to alert fatigue and lead to security teams missing true malicious activity. To overcome these challenges, security teams need a repeatable process to validate false positives and report security issues based on available evidence, including understanding why false positives occur, how to investigate alerts, and what information improves detection accuracy.
What Is a False Positive in Security?
A false positive occurs when a security tool identifies legitimate activity as a potential threat. The detection engine generates an alert because an event meets predefined criteria for suspicious or malicious activity, but when security teams investigate the alert, they determine no security threat exists.
When a security control triggers a false positive, it identifies an issue but classifies the activity incorrectly. Validating a false positive requires security teams to investigate the underlying event and gather evidence that determines whether the activity was benign or represented a real security concern.
Examples of False Positives in Cybersecurity
Some examples of false positives include:
- Antivirus software using signature-based scanning flags a legitimate application because its code resembles a known malware signature.
- Behavior analysis identifies authorized administrative activity as suspicious because it deviates from normal behavior.
- A security scanner flags legitimate traffic containing a SQL command or malformed data because those characteristics can also appear during online attacks.
- A legitimate website is incorrectly classified as a known phishing sites.
- Security software flags legitimate changes to a registry entry or system files as potentially malicious.
Generally, security teams spend time investigating and validating these false positives before they can close out the alerts.
What Is the Difference Between a False Positive, a False Negative, and a True Positive?
Classifying security alerts focuses on whether malicious activity actually occurred and whether the security tool identified it correctly. When evaluating a detection engine’s accuracy, alerts fall into one of the following four categories:
- True positive: Malicious activity occurred and the tool correctly generated an alert.
- False positive: Legitimate activity occurred and the tool incorrectly generated an alert.
- True negative: Legitimate activity occurred and the tool correctly generated no alert.
- False negative: Malicious activity occurred and the tool incorrectly generated no alert.
False positives create additional work for analysts because they need to investigate the identified activity to determine it poses no threat. False negatives create a different problem since malicious activity may go undetected, potentially failing to trigger an alert that allows an attacker to operate within the environment.
Why Reducing False Positives Can Increase False Negatives
Reducing false positives is more than making a security solution less sensitive. When teams change detection thresholds to suppress noisy alerts, they increase the risk that the system will fail to identify legitimate threats that have similar characteristics.
For example, behavior analysis may flag an administrator using privileged credentials to perform an unusual task or an attacker with stolen privileged credentials. Suppressing every alert associated with the behavior would reduce false alarms but might also prevent attacker detection.
To improve detection accuracy without missing true threats, security teams can add context, refine detection logic, and use investigation outcomes to refine alerts.
What Causes False Positives in Security?
The causes of false positives can vary by security technology, but most often, they arise from detection rules, environmental context, and changing definitions of normal activity.
Overly Broad Detection Rules and Thresholds
Security tools use rules, signatures, thresholds, and other criteria to determine when activity warrants an alert. Rules designed too broadly or thresholds set too aggressively can classify benign activity as suspicious simply because it shares characteristics with a known threat.
Missing Context About Normal Activity
A detection engine may recognize that an action is unusual without understanding why it occurred. For example, a large volume of outbound data might indicate data exfiltration, but the same activity could result from an authorized backup, system migration, or other legitimate business process. Establishing normal patterns of activity gives behavior analysis the context needed to distinguish unusual behavior from potentially malicious behavior.
Signature-Based Detection Limitations
Signature-based scanning identifies threats by looking for known patterns or characteristics associated with malicious files or activity. However, legitimate software can sometimes contain code or other characteristics similar enough to a known signature to trigger a detection. Simultaneously, threats such as polymorphic viruses can modify their code at the same time or conversely to avoid matching known signatures, demonstrating why signature-based detection alone can produce both false positives and false negatives.
Security Tool Misconfigurations
Default configurations may not reflect an organization’s applications, infrastructure, users, or risk profile. Improperly configured detection rules, unnecessary overlapping alerts, or thresholds that have not been tuned for the environment can all increase the number of false positives.
IT Environment Changes
Normal activity changes over time. A software update, new application, cloud migration, automated process, or change in user responsibilities can introduce behavior that differs from an established baseline. Without updating detection logic and behavioral baselines to reflect these changes, legitimate activity may suddenly begin generating security alerts.
Why Are False Positives a Security Problem?
High volumes of false positives create broad operational problems, especially when analysts have to investigate them before determining the activity is benign. These processes consume limited IT resources and time that could be better spent investigating genuine threats.
As false alarms accumulate, they can also contribute to alert fatigue, when analysts have so many low-value alerts that they struggle to identify the activity that requires immediate attention. Over time, these investigations slow down incident response processes when a real attack occurs. Further, analysts lose confidence in the security solutions and may start treating the alerts as routine noise.
By reducing false positives, security teams focus their attention on higher-value investigations while maintaining the detection coverage needed to identify and respond to genuine threats.
How to Validate False Positives and Report Security Issues
When security teams validate a false positive, they need to investigate the activity that triggered the detection, correlate it with relevant security data, document the evidence, and use the findings to improve future detections.
1. Investigate the Data Behind the Alert
The first step is to identify the activity that triggered the alert by reviewing the original event and detection conditions. Then, the team needs to investigate the underlying data to establish what happened, including the users and systems involved and the activity that took place.
When validating a potential false positive, security teams could:
- Review the rule or event definition that triggered the alert.
- Replay the original search to investigate the underlying log data.
- Review timestamps, source data, users, assets, and other fields associated with the event.
- Preserve relevant logs and events as investigation evidence.
2. Correlate the Alert with Related Activity
An isolated event can look malicious when the security teams lack the context of surrounding activity. By searching across relevant security data, analysts can determine what happened before and after the event to see if other sources support or contradict the initial detection.
Security teams could:
- Search for related activity involving the same user, asset, source, or other relevant field.
- Review activity immediately before and after the triggering event.
- Correlate events across relevant security data sources.
- Compare the activity with historical data to determine whether similar behavior occurred previously.
3. Document and Report the False Positive
Once the evidence supports a false-positive determination, security analysts document how their investigation led to that conclusion. A consistent record helps other analysts understand what happened, supports collaboration, and provides evidence that can inform future detection decisions.
Security teams could:
- Record the evidence used to determine that the activity was legitimate.
- Document investigation findings and relevant contextual information.
- Assign an owner and update the investigation or alert status to reflect the determination.
- Retain the supporting evidence so teams can reference it during future investigations.
4. Use Validated False Positive to Improve Detection
After closing the alert, the security team should use any repeated false positives to identify detection logic that requires refinement. This process should focus on reducing unnecessary alerts without broadly suppressing activity that might indicate a genuine threat.
Security teams should:
- Identify detection rules or conditions repeatedly associated with false positives.
- Review thresholds, filters, and event criteria that may be generating unnecessary alerts.
- Refine detection logic to account for known legitimate activity.
- Continue monitoring the updated detection to confirm that changes reduce noise without creating detection gaps.
Graylog: Helping Lean Teams Reduce False Positives
Graylog enables security teams to investigate and validate potential false positives without manually piecing together evidence across disconnected data sources. By centralizing security data, supporting search and cross-source correlation, and providing investigation workflows that preserve evidence and document findings, teams can add context to suspicious activity and make more informed determinations about potential threats. Analysts can then use those findings to refine event definitions, filters, and detection criteria, helping reduce recurring false alarms while maintaining visibility into activity that warrants investigation.
