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Minimizing False Positives: Enhancing Security Efficiency

Organizations waste enormous amounts of time chasing down security alerts that turn out to be nothing. Research has shown almost 20% of alerts are not legitimate, wasting massive amounts of time in the investigation rather than working on proactive security measures to improve organizational security posture. This is partially because 59% of surveyed security professionals feel that false positives take longer to resolve than true positives, wasting even more time. 

What is Low False Positive Rate Security?

Accuracy is one of the top goals when doing any security work. Still, with massive amounts of data being ingested and processed, there is always the possibility that an alert is given incorrectly. A false positive is an alert that appears to be legitimate but, upon investigation, turns out to be incorrect. Unfortunately, these alerts waste valuable resources that could be better utilized for investigating genuine threats. 

Tools with high false positive rates create a level of alert fatigue in security teams where alerts no longer command the same level of response. After many false investigations, teams assume that new alerts are likely false and do not use the same speed and efficiency in reviewing new alerts. This creates a situation where legitimate alerts are not quickly identified and managed, allowing potential issues to remain unmitigated for extended periods. 

What Are False Negatives?

The alternative issue with alerts happens when threats are missed by a tool, going undetected. These false negatives are a significant issue as they leave vulnerabilities unnoticed for extended periods, which weakens the organizational security posture. This is especially an issue as other tooling may detect these issues, allowing attackers to find weak spots while your organization remains unaware. These failures to detect real threats complicate response strategies, making managing and mitigating risks difficult. They can ultimately lead to significant damage or data loss due to these undetected threats.

Why Are True Positives Valuable?

Optimally, the best tools have high accuracy, presenting true positive values that identify real threats. By ensuring immediate threat identification, true positives allow organizations to quickly respond to and mitigate potential dangers, significantly reducing the risk of damage. This prompt detection not only aids in enhancing security protocols and systems through valuable data but also curtails the financial impact of security breaches. 

By improving the precision of threat and vulnerability detection, organizations can enhance the overall efficiency of their security infrastructure, leading to reduced operational costs and strengthened defensive measures. 

Accuracy In AppSec

When looking at accuracy, it is essential to note that tooling is leveraged differently across different areas of infosec. For Application Security (AppSec), the tooling is intended to identify and mitigate vulnerabilities in software applications. Some of this is done proactively during development using static application security testing (SAST) to analyze code for weaknesses. Others are done after applications are live, testing the running application and APIs for exposures. 

When issues are detected for AppSec, teams must investigate the problem to verify it is legitimate. SAST tools may require reviewing code and working with teams to determine if the issue detected is legitimate. False positives waste the time of these teams and reduce their faith in the AppSec teams, which ensures that future findings will take longer to review on their part. This is unfortunate as accurate SAST tools are highly effective for detecting security and code quality issues, helping teams deliver more resilient code. 

Teams review running code for more dynamic application security testing (DAST) or API-based testing, which may even be in production. This testing helps identify common attacks such as XSS (cross-site scripting), SQL Injections, and other top web-based threats. Alerts from this testing also require investigative time and inter-team communications. As these alerts are often against production systems, findings require swift investigative efforts to remediate before attackers can detect them. Tools with high false-positive rates lead to “alert fatigue,” slowing down team reviews and leaving potentially vulnerable interfaces open to attack. 

Accuracy In Threat Detection

Accuracy also plays a crucial role in threat detection, where identifying true security threats and minimizing unnecessary alerts is critical to a rapid response. Unlike looking for application vulnerabilities, threat detection looks for utilization patterns that indicate a vulnerability is being exploited. However, many threat detection tools analyze massive volumes of data, generating many alerts of lower confidence, which must be investigated. 

To combat this, many organizations have turned to advanced technologies such as machine learning and artificial intelligence, which further enhance the precision of these systems, making them more adept at discerning genuine threats from benign anomalies. 

Getting Accurate Findings

Probely is a highly accurate tool for secure application development, with an extremely low rate of 0.06% for false positives. It specializes in identifying over 3000 types of vulnerabilities, including critical ones like XSS and SQL injections, Probely integrates seamlessly into development workflows. This integration facilitates automated and continuous security assessments, greatly enhancing development efficiency. With Probely, developers are equipped to secure applications comprehensively throughout their lifecycle, ensuring robust protection for your organization’s sensitive data.

Learn more about how Probely can help secure your applications by signing up for our fully-featured, 14-day free trial.


Can false positives be completely eliminated in security?

While it’s challenging to eliminate false positives entirely, continuous improvements in technology and processes can significantly reduce their occurrence.

How do security tools balance sensitivity and specificity?

Security tools aim to maximize sensitivity (detecting threats) while minimizing false positives to maintain specificity and operational efficiency.

What role does machine learning play in cybersecurity?

Machine learning helps improve the accuracy of detecting real threats and distinguishing them from benign activities, enhancing overall security effectiveness.