Security operations centers (SOCs) play a critical role in cybersecurity, proactively detecting and responding to attacks in real time while reinforcing the business’ security posture. Though they make the job look easy, SOCs are overloaded with data, especially as artificial intelligence (AI) becomes widely adapted – almost 25% of C-suite executives directly use generative AI tools for work. To keep these security hubs operating efficiently, they also demand an extensive commitment of man-hours and a substantial annual budget. So, how do SOC analysts overcome these challenges? By tackling false positives at their source.

Lay of the (SOC) Land

Before we dive in, let’s first take an inside look at the SOC landscape. Managing the deluge of data within SOCs is an acute challenge. The sheer amount of security deployments and configurations makes it difficult to harden security defenses without risking business operations – on average, a SOC team fields over 11,000 alerts daily, with almost a third being false positives. Nonetheless, data comes from complex threat landscapes involving cloud environments, generative AI tools and third parties with data access, and the list continues. So much time is allotted to managing this due to lack of automation. Between the scarcity of time, expertise and resources, security teams face the challenge of navigating disparate configuration languages. Herein lies the secret: A substantial portion of this data is irrelevant – also known as false alerts – and need not burden the analysis process.

Find, Tackle and Overcome False Positives

Identifying these false positives is easier than most would think – nearly half of alerts are false positives. There’s no need to look elsewhere besides directly inside the security environment where these false alerts lie. The solution to managing this data lies directly within the security controls generating the alerts.

Determining where these false alerts are is easier than tackling them. It can be daunting but it all starts with automating the process of reducing false positives. Implementing a process through configuration and log analysis that doesn’t interrupt the business can alleviate the time and money typically spent managing these alerts. Efficiently mitigating false positives will only result in positive outcomes. Reducing false positives means that security systems accurately distinguish between genuine threats and benign activities, leading to an efficient use of resources, quicker response times to actual threats and a significantly elevated security posture.

False alerts are no easy task, but by addressing the issue at its roots, you can effectively resolve these critical challenges.

Here’s what we recommend adding to the top of your priority list to analyze for greater efficiency:
1. Enhance False Positive Event Detection: One approach is to establish baselines of normal behavior for the system or network being monitored, then compare each alert against these baselines to determine if it is anomalous. Another method is to use machine learning algorithms to analyze patterns in the data and identify any outliers indicative of false positives.
2. Eliminate False Positive Events: False positive events can be reduced or eliminated by improving the accuracy of security systems, such as adjusting security rules and configurations, implementing machine learning algorithms, or using threat intelligence feeds.
3. Minimize the Impact of False Positive Events: The best approach to minimize the impact of false positive events is implementing a multi-layered security strategy that includes real-time monitoring, automated analysis and contextualized remediation to identify and resolve false positive events.

Put a SOC in It

Security control optimization is a prime example of how we can reimagine these processes without compromising on the quality of outcomes and, most importantly, significantly reducing expenses.

Security Controls Optimization can provide organizations with:
● Intelligent Automation in SOCs: Intelligent automation and orchestration are used to reduce alert fatigue in SOCs. Applies machine learning for data processing and task automation, allowing analysts to focus on critical threats. Orchestration aids in efficient incident response and decision-making.
● Advanced Threat Intelligence: Employs automated, multi-sourced threat intelligence with analytics to prioritize alerts and minimize false positives. Utilize machine learning and AI to improve threat detection accuracy based on historical data.
● Real-Time Security Controls Assessment: Implement continuous monitoring to evaluate the effectiveness of security measures in real-time. This approach allows for immediate identification of security gaps and swift adjustments to maintain robust defense mechanisms. By assessing security controls organizations can ensure ongoing compliance and rapidly respond to evolving threats.

Addressing the issue at its roots and eliminating false positives promises a unique solution, one that can remarkably enhance SOC efficiency and cost-effectiveness. It may seem like SOCs have their work cut out for them, but knowing where to start to tackle the problem is half the battle, especially if it means business uptime, reduced risk exposure and increased confidence in the organization’s overall security.