When it comes to cybersecurity postures, there’s no such thing as absolute protection. Security controls occasionally fail to detect attacks or harmful actions. On the other hand, these cyber defenses can also be so vigilant that they produce false positives or the erroneous identification of safe and legitimate activities as threats.
A Morning Consult report shows that nearly one-third of security operations center (SOC) work time is spent on investigating and validating incidents that ultimately turn out not to be real threats. In other words, security teams are allocating a significant portion of their limited time and resources to addressing false positives.
False Positives in Firewall Defense
Unfortunately, false positives are extremely challenging to curb. The diversity of traffic and complexity of network environments continue to expand, making it difficult for firewalls to accurately distinguish safe from malicious traffic. Threats are also becoming dynamic and rapidly evolving. Additionally, there are always human factors that can cause security weaknesses.
Web application firewall (WAF) solutions process signals from a multitude of web services and network activities that play crucial roles in everyday operations. Wrongly blocking these connections can lead to costly interruptions and bad customer experiences, which can lead to reputational damage and revenue losses.
Firewalls are designed to determine whether network traffic is allowed or not, serving as an access regulator. In the process of assessing traffic, they unavoidably commit errors. While there are no recent studies on the false positive rates of new firewall applications and devices, it is widely accepted that the problem of mistakenly identified threats persists. This is why so many firewall vendors nowadays tend to tout the improvements they introduce, especially with the help of AI, to curb false positive rates.
Growing Complexity of Modern Networks
Modern networks deal with a wide range of traffic, from web traffic to peer-to-peer file transfers, real-time chats, multimedia streaming, gaming, and remote desktop connections. The connections that enable different types of traffic have to go through different channels and points, including content distribution networks. Online content providers, for example, use mechanisms to shield their servers from DDoS attacks, which means that the identifiable origins of their content may not appear the same to their audiences.
All these create complexities that make it difficult to set rules for distinguishing safe from harmful connections. On the other hand, the network environments of organizations continue to evolve as they add new devices, applications, and protocols. Firewalls have to take these changes into account and their configurations need to be updated. Legitimate traffic may be inadvertently blocked because the configurations have not been updated appropriately.
It is also possible that legitimate traffic is unwittingly prevented because of mistakes in the updated configurations. As these changes continue to take place in organizations, it makes sense that there will be false positives in firewall protection. Organizations unceasingly adopt new technologies, and security teams are usually unable to keep up with proactively managing all the changes. It is difficult to modify configurations often, especially given the multitude of endpoints and workloads connecting to a network.
Rapidly Evolving Threat Landscapes
Similar to the inescapable changes in modern networks, the threats are also rapidly evolving. Perpetrators tweak or retool their attacks as they discover new vulnerabilities in changing networks. They also modify their attacks in response to the changes in the defensive tools deployed by organizations. With the help of generative AI, it is now easier for threat actors to change their attacks to overcome defenses and exploit newly discovered security weaknesses.
It is difficult to set rules for spotting and blocking anomalous traffic when the threats are highly dynamic. Having an updated threat signature database no longer suffices. Zero-day attacks are becoming more prevalent, and security teams are challenged to defend perimeters against them.
Moreover, heuristic and signature-based detection becomes ineffective when legitimate traffic starts to resemble known threats, or when threats are designed to simulate legitimate traffic. Firewalls can even be tricked into perceiving traffic as safe by mimicking legitimate network activity.
Human Factors
it would be unreasonable to expect flawless configurations from network security teams or those responsible for overseeing firewall functions. There are times when configurations are made too restrictive and end up blocking legitimate traffic. This usually happens when anticipating zero-day attacks. Security teams may engage in configuration overkill to block all suspected threats, leading to an unacceptably high rate of false positives.
Many organizations also implement custom rules and tuning in response to new forms of attacks and suspected variants of existing high-profile threats. These custom rules are sometimes not tested carefully, resulting in false positives that remain undiscovered and not rectified for some time.
It is worth noting that the complexity of firewall rulesets is directly related to the likelihood of firewall misconfigurations. The more complex the rules are, the more difficult it is to update them – which means greater chances of committing mistakes.
Mitigating the Problem of False Positives
Completely eliminating false positives in firewalls is a pipe dream, but it is possible to reduce the rate of mistaken threat detection and blocking. AI has been enhancing cybersecurity solutions, including firewalls, enabling the automation of various tasks in firewall configuration and updates. AI can also be deployed to detect patterns across vectors as a smarter way to block anomalous traffic.
Likewise, automation can help optimize firewalls configurations dynamically. This might involve using and updating rulesets, policy enforcement, configuration backup and restoration, traffic analysis, performance tuning, threat intelligence updating, and consolidation, as well as incident response. The patching or updating of firewall software can also be automated, along with policy compliance checks, report generation, audit log generation, and user provisioning and de-provisioning.
When it comes to anomalous traffic detection and blocking, AI provides behavioral analysis and context-aware security so firewalls do not rely solely on threat signatures to detect malicious network activity. AI-powered firewalls can establish benchmarks for safe or regular network activity and examine traffic against these benchmarks to spot and prevent threats. A surge in network activity may appear unusual, but other information streams may suggest otherwise. For example, the time and source of the traffic may suggest that a surge is due to the recent introduction and heavy marketing of an app in a new market.
Conclusion
Again, it is not possible to fully get rid of false positives in firewall protection and cybersecurity in general. No system can be absolutely accurate given the evolving nature of networks, the dynamic threat landscape, and the inability to completely control the human factor in firewall configuration and management. Nevertheless, there are ways to reduce the rate of false positives and mitigate their impact on security department time and resources.
