Understanding False Negatives in Cybersecurity

In cybersecurity, a false negative is when a security tool fails to identify a threat. A scan, test, or other detection method cannot spot malicious activity, vulnerabilities, or other threats affecting your IT infrastructure, mistakenly returning a negative result instead of a positive one.

False negatives are particularly dangerous as they allow attackers to operate undetected, compromising further systems or worsening data breaches before security teams can respond.

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Examples of False Negatives

Given the broad scope of cybersecurity, false negatives vary depending on the security tool and its focus. False negative examples include:

- Network Security: An intrusion detection system that fails to identify malicious activity as a threat, logs it as safe, and allows the attacker to infiltrate the network unnoticed to spread malware or gain unauthorized access to data.

데이터 보안: An attacker accesses or exfiltrates sensitive data, bypassing a data loss prevention tool that fails to flag their activity, leading to a data breach without any alert.

클라우드 보안: Tools overlooking misconfigurations across cloud services, leading to vulnerabilities and unauthorized access to sensitive data.

애플리케이션 보안: Malicious queries bypassing web application firewall (WAF) filters or scanning tools that do not identify software bugs or vulnerabilities during testing. Both of these can also lead to data being exposed.

These false negative examples are some of the possible scenarios that arise when security tools incorrectly classify malicious activity as safe or fail to spot vulnerabilities.

Potential False Negative Implications for Your Business

Generally speaking, false negative implications result from the attacker having free rein to access and exfiltrate sensitive business data before you can respond and remediate the threat. This can have severe consequences for businesses, with the global average cost of a data breach in 2024 increasing to $4.88 million (USD), according to research from IBM and the Ponemon Institute.

These financial losses can be due to a range of factors:

Loss of Intellectual Property: The theft of proprietary information or trade secrets key to maintaining your competitive advantage in the industry. This can lead to losing customers, income, and market position.

Loss of Customers: Data breaches can also lead to customers leaving due to reputational damage or operational disruptions. Customers hand over their sensitive information to businesses with the expectation it will be safe from third parties. Data breaches mean a loss of trust and credibility that drives away many existing and prospective customers. Customers may also leave if cyberattacks bring service interruptions as staff attempt to recover and fix potential issues.

Paying Ransomware Demands: An example of a cyberattack that disrupts services is ransomware, where sensitive business data is encrypted until a payment is received. Paying the ransom may result in the attacker decrypting your data to resume operations. But, this comes with significant costs while also encouraging hackers to continue this disruptive attack vector.

Fines and Legal Fees: Data breaches create legal liabilities for your business, potentially leading to significant financial penalties depending on the industry you operate in, the regulations you are subject to, and the extent of the breach. Undetected data breaches demonstrate non-compliance with data protection laws (e.g., CCPA, GDPR, etc.), potentially triggering hefty fines. Additionally, unauthorized data access could cause a breach of contract, opening yourself up to legal recourse from clients and compensation being paid.

The Most Common Causes of False Negatives

False negatives mean there are issues with your existing security posture. This could be due to a lack of resources, cybersecurity management problems, or an inherent issue with the security tools and solutions you have in place.

The most common causes of cybersecurity false negatives include:

- Inaccurate Detection Algorithms: Security tools that rely on poor or incomplete algorithms increase the likelihood that threats go undetected. This could be due to complex attack patterns or the inability to identify subtleties and nuances in user behavior indicative of malicious activity.
- Outdated Threat Signatures: A common way detection algorithms may miss real vulnerabilities is by relying on out-of-date threat databases. As new exploits are discovered, you must promptly update your software and security tools.

Advanced techniques or zero-day exploits: Hackers utilizing sophisticated techniques (e.g., obfuscation, polymorphic malware, etc.) or zero-day exploits are more likely to cause false negatives and bypass your security systems. Zero day exploits target previously unknown vulnerabilities and are not present in existing threat databases.

Insufficient Coverage: Less advanced security tools struggle to provide comprehensive coverage. This can cause gaps in testing when scanning your network or applications.
Misconfigured Security Tools: IT teams may implement overly lenient or inadequate rules that fail to capture threats, leading to false negatives. Security tools typically require significant time and expertise to set up without misconfigurations. Some staff may not have the knowledge to implement policies and all the necessary parameters to be scanned manually.

False Negatives vs. False Positives

Another error that can occur during security scans and testing is a false positive. While false negatives allow a threat to go undetected, false positives mistake legitimate activity as malicious or incorrectly identify a vulnerability that does not exist.

Effective security tools should aim to minimize both errors. But, false negatives pose a greater risk as they allow real threats to go unnoticed leading to more severe consequences from cyberattacks.

False positives mostly create operational challenges rather than security risks. Creating unnecessary alerts that require investigation leads to wasted time and energy that could be spent working on legitimate threats. However, false positives can result in “alert fatigue” within the organization, potentially leading to prolonged responses when real positives occur or staff ignoring future new alerts altogether.

Strategies for Reducing False Negatives

Organizations need to find methods of reducing false negatives as much as possible to safeguard IT infrastructure and prevent attacks from propagating undetected. Thankfully, there are a range of strategies to help you minimize false negatives. These include:

Implement a Positive Security Model

Many security postures rely on a negative security model. They grant access to all traffic that isn’t deemed hostile (e.g., doesn’t match a known threat signature). This approach inherently leads to more false negatives, offering attackers more opportunities to bypass your protection. In contrast, positive security models deny access to all traffic that isn’t deemed valid. This change of focus makes it harder for attackers to go unnoticed, significantly reducing false negatives.

Incorporate Behavioral Analysis

Sophisticated attacks and zero day exploits can cause false negatives by either fooling your security tools or targeting previously unknown vulnerabilities. The likelihood of these attacks causing false negatives can be significantly reduced using User and Entity Behavior Analytics (UEBA) tools. These tools establish baselines of normal activity among users and systems. When patterns differ from this baseline, they are immediately flagged as suspicious, reducing false negatives and helping to catch sophisticated and new threats.

Rely on Multi-Layered Protections

Many organizations rely on multi-layered protection, incorporating multiple overlapping tools to provide additional fail-safes to their security posture and reduce the chances of false negatives. This could include a range of tools and technologies that both identify threats and protect data if these threats successfully compromise your systems.

Update Your Systems Regularly

Ensure all your security tools are promptly updated to utilize the most up-to-date threat databases and track the latest vulnerabilities. Proactively updating your systems reduces your attack surface and gives your security tools the best chance of identifying all threats.

Minimizing False Negatives with CloudGuard WAF

Check Point’s next-generation web application firewall CloudGuard WAF, provides a prevention-first approach to protecting web applications and APIs. CloudGuard minimizes false negatives (even against zero day threats) through real-time contextual AI analysis instead of just comparing against known signatures.

Learn more about how CloudGuard was awarded best cloud security service across various categories by GigaOm, or use our WAF comparison tool to see why we think it is the best solution on the market.