A false positive in cybersecurity is an alert or indication that a security system has incorrectly identified a threat or vulnerability when none exists. It is a false alarm generated by security software and tools. False positives can arise for various reasons, such as overly aggressive security settings and outdated threat data.
In the context of attack surface management (ASM), these misclassifications can lead to unnecessary alerts, wasted resources, reduced ability to identify and respond to genuine threats, and, ultimately, contribute to a larger attack surface.
Table of Contents
- What Are the Impacts of a False Positive in Cybersecurity?
- What Causes a False Positive in Cybersecurity?
- How Can Security Teams Identify False Positives?
- How Do You Mitigate False Positives?
What Is a False Positive in Cybersecurity?: A Deep Dive
What Are the Impacts of a False Positive in Cybersecurity?
The implications of false positives extend beyond mere inconvenience. They can significantly deter an organization’s attack surface reduction efforts, leading to wasted time and resources. That is because security teams’ valuable time and resources are diverted from genuine security issues to investigating false positives.
Aside from the distraction, the time and resources consumed in investigating and remediating false positives can also add to direct and indirect security costs.
Additionally, real threats may be overlooked as security teams become desensitized to alerts (i.e., alert fatigue), increasing the attack surface and potentially allowing attackers to exploit unaddressed vulnerabilities and compromise systems undetected.
What Causes a False Positive in Cybersecurity?
Several factors can contribute to the occurrence of false positives. Here are some of the most common ones.
- Overly sensitive security settings: When security systems like firewalls, antivirus, and vulnerability scanners have very stringent settings, they may flag harmless activities and files as malicious.
- Incorrect asset inventory: In some instances, security solutions can mistakenly attribute assets to your organization and create false positive alerts when vulnerabilities are found in them.
- Outdated threat data: Security systems may detect vulnerabilities that no longer apply to an asset. These flaws may have already been mitigated and marked as acceptable, but related alerts still get generated.
- Software bugs: False positives may be caused by software bugs or glitches in security tools or operating systems (OSs).
- Human error: There are cases when false positives are caused by human error, such as incorrectly configuring security settings or failing to update threat signatures.
How Can Security Teams Identify False Positives?
The first approach to spotting false positives is understanding infrastructure components’ normal behaviors and activities. That can be done by analyzing historical data on traffic patterns, security events, and asset configurations. Security teams can then compare these baselines to industry benchmarks to determine anomalies. For example, if the cause of an alert aligns with industry standards, then it may be a false positive, and the security configuration may need to be adjusted.
Another way to detect false positives is to validate the alerts in one tool with those of other security solutions. Providing context surrounding an alert may help uncover false positives. For instance, a recent update or scheduled maintenance may explain a flagged asset.
How Do You Mitigate False Positives?
There are several effective strategies to reduce the occurrence of false positives. We’ll talk about them briefly below.
Fine-Tune Security Settings
Regularly reviewing and adjusting security configurations can help balance the sensitivity and accuracy of security tools. This process is essential to avoid overly restrictive settings that trigger excessive false positives.
Security teams can also implement safelisting and blocklisting, creating lists of known safe assets (safelists) and known malicious files (blocklists) to help distinguish between benign and suspicious activities.
Leverage High-Confidence Discovery Techniques
Security teams may also use attack surface management (ASM) solutions and other security systems that employ reliable asset discovery and vulnerability detection techniques to avoid incorrect asset attribution and irrelevant security alerts.
Utilize Tools Enabled by Machine Learning
Machine learning (ML) algorithms can be trained to identify patterns in data that are historically linked to false positives. These patterns may include certain data formats, network traffic characteristics, and user behavior anomalies that often lead to false alarms.
Through learning and refinement, models representing a typical false positive can be built. These models can be used to analyze new data points and determine the likelihood of them being false positives.
Implement an Efficient Prioritization Method
Despite implementing all these measures, eliminating all false positives can be difficult. However, security teams can focus on what matters most and avoid alert fatigue by efficiently prioritizing alerts according to their severity and impact.
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False positives in cybersecurity can be detrimental to an organization’s security posture, leading to wasted resources, increased risks of overlooking genuine threats, and uncontrolled attack surface growth. To avoid these adverse effects, companies must implement adequate approaches addressing the root causes of false positives.
Key Takeaways
- A false positive in cybersecurity refers to instances where a security system or tool flags a benign entity or activity as a potential threat.
- A false positive in cybersecurity can result in wasted time and resources, alert fatigue, increased costs, and a larger attack surface.
- False positives can be caused by overly sensitive security settings, software bugs, and human error.
- False positives can be mitigated by fine-tuning security settings, updating threat signatures and intelligence sources, relying on high-confidence asset discovery and attribution techniques, implementing safelisting and blocklisting, utilizing tools enabled by ML, and using an efficient prioritization method.
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