False positive

What is a false positive?

A false positive occurs when a security system incorrectly identifies legitimate, non-malicious activity as a threat. As a result, it generates an alert or blocks the action. False positives are common in tools like antivirus software, intrusion detection systems (IDS), and email filters, where overly sensitive or misconfigured settings can flag normal behavior and files as suspicious.

Summary

When security software mistakenly detects benign activities as threats, it is called a false positive. This causes unnecessary alerts and responses to attacks that can overwhelm security teams, diverting attention from real threats and wasting valuable resources.

False positives cause alert fatigue and reduce a team’s ability to respond effectively to genuine threats. For example, your antivirus software might flag safe files as malware or an IDS could generate alerts for routine network traffic, mistaking it for malicious behavior.

On the other hand, a false negative occurs when an actual threat goes undetected, posing risks as it proceeds unnoticed. Overly aggressive security settings can result in more false positives, whereas lenient settings increase the risk of false negatives.

Deep dive

False positive in cybersecurity

In cybersecurity, a false positive occurs when security mechanisms incorrectly flag legitimate actions as threats. For example, antivirus software might flag a safe app update as malware, or an intrusion prevention system (IPS) could block normal data transfers, mistaking them for an attack.

These errors stem from over-tuned or overly aggressive security settings that fail to distinguish between harmful and benign activity. False positives can lead to operational inefficiencies because teams waste time investigating non-harmful issues instead of real threats.

False negative in cybersecurity

A false negative occurs when a security tool fails to detect a real threat, allowing malicious activity to proceed without any alerts or intervention. For example, malware might slip past antivirus software due to evasion techniques like obfuscation, or a sophisticated phishing email could bypass spam filters.

False negatives are particularly dangerous because they give the impression that systems are secure when they are not, increasing data breach risks and other security incidents. When detection fails, malicious actors have free rein and can cause a cyberattack.

False positive vs. false negative in cybersecurity

Reducing both false positives and false negatives is important to improve efficiency, prevent resource wastage on non-threats, detect and mitigate real threats timely, and maintain a strong security posture.

For example, a security information and event management (SIEM) system might generate a false positive by flagging normal user behavior as suspicious, but a false negative could miss an intrusion attempt altogether.

Aspect	False positive	False negative
Definition	Occurs when security software—like a firewall—detects a benign or harmless action as a threat. This causes operational disruptions and resource wastage.	Occurs when security software—like antivirus programs—fails to detect a real threat, like malware.
Impact	Consumes valuable analyst time and resources.	Exposes organization to undetected breaches.
Consequence	Too many alerts cause alert fatigue, where excessive alerts cause desensitization and the risk of missing genuine threats.	Poses severe risk as undetected threats cause data loss, financial and reputational damage, and regulatory violations.
Response	Requires continuous tuning and refinement of detection rules.	Demands immediate analysis and remediation once identified.
Challenge	Striking a balance between sensitivity and specificity to minimize false positives without compromising detection of critical threats.	Ensuring advanced threats are not missed by continuously enhancing detection capabilities and updating threat intelligence.

False positive vs. benign positive in cybersecurity

A benign action is any legitimate, harmless activity that poses no threat to a system and data. These are safe operations that occur when you use software, network, or data, like updating software, authorizing user access, network traffic, or backing up data automatically.

A false positive occurs when a benign action is incorrectly flagged as malicious. On the other hand, a benign positive correctly identifies a threat that is low-risk or non-exploitable.

For example, a vulnerability scanner might identify software components not in use that pose no immediate danger as benign positives. These detections do not need urgent remediation.

But false positives often require immediate action to adjust settings or investigate, even though no real threat exists. Managing the balance between these types of alerts helps you prioritize efforts without ignoring critical safety issues.

Examples

False positives

- Email spam filters: A common false positive occurs when legitimate emails are incorrectly marked as spam, causing important communications to be missed.
- Antivirus software: Antivirus programs sometimes flag safe files, like software updates or system files, as malware, disrupting normal operations due to a false positive.
- Web application firewalls (WAFs): WAFs may mistakenly block legitimate traffic like user form submissions or API requests, mistaking them for attacks.
- Inactive user accounts: Security systems may incorrectly flag inactive accounts as risks, even though they pose no actual threat, a benign false positive.

False negatives

- Zero-day exploits: These target unknown vulnerabilities, bypassing security tools like antivirus software, firewalls, and intrusion detection systems (IDS) because the threat signatures are not yet recognized.
- Phishing emails: Some phishing attempts may evade email security filters, appearing legitimate and bypassing detection due to subtle social engineering techniques.
- Compromised credentials: Systems might overlook unauthorized access attempts if the attacker uses valid credentials, failing to detect that an account is compromised.

History

False positives have been around since the early days of intrusion detection systems in the 1980s. As security technologies evolved, so did their rules and algorithms, increasing the complexity and likelihood of incorrect detections.

When antivirus software, firewalls, and SIEMs expanded in the 2000s, security teams were inundated with alerts because the systems were overly sensitive and flagged benign actions. This led to an alert overload—a state where actual threats could be overlooked due to the sheer volume of false positives.

Over time, sophisticated machine learning and pattern recognition techniques aimed to reduce false positives. But despite advancement, fine-tuning systems to minimize false positives without compromising detection remains a challenge.

Future

The future of managing false positives will heavily rely on artificial intelligence (AI) and machine learning advancements, which can improve threat detection accuracy by learning from past incidents and continuously adapting to new attack patterns. For example, AI models can analyze patterns in network traffic, user behavior, and app logs to differentiate routine actions from potential threats.

Automated threat response systems are also expected to refine detection capabilities, ensuring that security teams can focus on real threats rather than sifting through incorrect alerts. These systems can assess alerts, dismiss false positives, and take action on real threats, reducing the burden on human analysts.

With regulatory frameworks like General Data Protection Regulation (GDPR) demand higher data protection standards, organizations would adopt these technologies to manage false and benign positives.