False positives occur when a security tool or solution incorrectly flags a benign file, activity, or event as malicious. However, brushing aside a false positive alert as a mere nuisance can lead to disastrous consequences. Let's look at why false positives are a cybersecurity concern worth prioritizing.
What are false positives?
A false positive occurs when a security system identifies a threat that doesn't exist or mislabels harmless software as malicious. It can happen for various reasons but is primarily triggered by an over-sensitive system or a glitch in the tool or solution. The incidence of false positives can also arise with antivirus or malware solutions. For instance, in 2011, a Microsoft program erroneously determined that the Chrome browser was malware and deleted the entire browser.
False positives drain resources as the time consumed by professionals to identify the root cause is often wasted, for the threat is often nonexistent. They can also create a false sense of security, leading people to believe that their systems are more secure than they are.
Why do false positives matter in cybersecurity?
False positives can have a significant impact on cybersecurity. They can mislead security teams and waste time and resources that could be better spent on identifying and responding to actual threats. A study found that 80% of its respondents claimed to spend a chunk of time resolving false positive alerts from their security systems. The report indicated a worrying trend that 47% of security professionals claimed that they ignored 50% or more of such warnings.
False positives are especially problematic in intrusion detection and prevention systems (IDPS). IDPS are designed to detect and block malicious activity but often generate false positives. Such misdiagnoses often lead to IDPS being turned off or ignored altogether, leaving an organization vulnerable to attacks.
False positives should be treated as potential threats and investigated accordingly. Security teams should also have processes to identify and address false positives quickly.
How to reduce the number of false positives?
False positives consume time and resources without delivering any concrete instance of threats. They cause businesses to miss real threats. However, security professionals can reduce the number of false positives by following some best practices.
One way to reduce false positives is to use multiple security tools. This way, if one tool flags something as a threat when it isn't, the other tools can confirm or deny them.
Another way to reduce false positives is to customize your security settings. You can ensure that your security tools only flag actual threats by tweaking the settings.
Another approach involves using artificial intelligence (AI) and machine learning (ML) to differentiate between genuine problems and false positives. For example, machine learning can generate a domain map, identify the most significant sensor groups, and aggregate anomalies to create a more accurate definition of "normal" that reduces false positives.
For a machine learning (ML) verification system to be effective, professionals must train it on high-quality, reliable, and trustworthy data; otherwise, it will make mistakes and produce false positives at an alarming rate. Thus, the first step is to establish a reliable method of collecting data.
Despite watertight measures, there is no guarantee of security systems not flagging false positives, albeit the number will be drastically low.
False positives drain resources and time, resulting in missed opportunities to identify and mitigate real threats. Further, they can also damage systems or data. Companies can mitigate false positives by employing multiple security solutions to corroborate the findings of one tool and address them if they pose a real threat. Companies should adopt a proactive, data-driven approach to risk management. This way, they can better understand risks and ensure their limited resources are expended judiciously.
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