A false positive arises when a security control mistakes normal, harmless activity for malicious behavior. The tool raises an alert, analysts investigate, yet no real threat exists.
Examples
- Antivirus marks a legitimate application as malware.
- A firewall blocks routine SaaS traffic.
- A network monitor flags scheduled backups as “data exfiltration.”
False positives occur in every layer of defense—from intrusion-detection systems and email gateways to endpoint protection platforms.
False Positive Alerts
Security notifications that trigger unnecessarily due to misidentification of safe activities as threats. These alerts consume security team resources and time for investigation, despite representing no genuine risk to the organization’s cybersecurity posture.
False Positive Rate and Formula
A performance metric that measures the frequency of incorrect threat identifications within a security system:
FPR = FP ÷ (FP + TN)
- FP (False Positives): Number of safe activities incorrectly flagged as threats
- TN (True Negatives): Number of safe activities correctly identified as safe
- FP + TN: Total number of all legitimate, non-threatening activities
A lower FPR means the system is better at letting legitimate traffic pass unchallenged.
False Positive Impact
- Alert fatigue among security analysts
- Wasted investigation time and resources
- Potential desensitization to real threats
- Business operation disruptions when legitimate activities are blocked
- Reduced confidence in security system reliability
Common Causes of False Positives
- Overly broad detection rules
- Outdated threat signatures
- Insufficient baseline understanding of normal network behavior
- Misconfigured security parameters
- Poorly tuned security systems for specific environments
