Scaling up a security operations center (SOC) is inevitable for many organizations. How AI supports growth without overloading analysts.
Scaling up a security operations center (SOC) is inevitable for many organizations. Although it might sting, keeping pace with business growth, increased threat volume and complexity, or compliance and regulatory demands requires enhancing and expanding SOC capabilities. Traditionally, SOC scaling efforts have translated to increased burdens on already-overworked analysts. However, the transformative potential of Artificial Intelligence (AI) is poised to reshape this trajectory.
The Problem with Traditional SOC Scaling
For many analysts, the news that they need to scale operations is daunting. Most modern SOCs already receive thousands of alerts, stretching analysts to capacity. Adding the tools necessary to scale a SOC inherently results in more alerts – often more than analysts can handle.
Moreover, adding new technologies and tools to a SOC without proper integration can create siloed systems. These siloes mean that, in addition to investigating more alerts, analysts are forced to manage multiple platforms, further compounding their already massive and unsustainable workload.
To make matters worse, many organizations will be reluctant or unable to hire the staff necessary to scale operations – CISOs are facing flat or falling cybersecurity budgets, and the cybersecurity skills gap remains persistently high. As a result, many organizations attempt to scale operations without bringing new staff on board and push existing analysts beyond their limits.
Aside from the obvious well-being impacts on analysts – which are severe and unignorable – traditional SOC scaling practices can compromise an organization’s security. If analysts are burned out and overstretched, they’re more likely to miss alerts, misinterpret data, or otherwise make a costly mistake. And who could blame them? After all, they’re only human. AI, however, isn’t.
A Brave New World: Scaling SOCs with AI
Strategic and thoughtful implementation of AI can help SOCs scale up without overloading analysts. In fact, building an AI SOC can reduce analyst workloads, increase efficiency, and improve performance. Let’s look at how.
Reducing Alert Overload
As noted, modern SOC environments generate an extraordinary number of alerts. The higher a SOC scales, the more alerts analysts will receive. Integrating AI into SOCs can streamline the investigation process and reduce alert overload by:
· Prioritizing Alerts: Machine learning (ML) algorithms can prioritize alerts based on their importance, ensuring that analysts can focus their efforts and resources where they are needed most.
· Reducing False Positives: Analysts can use AI to analyze historical data to learn patterns of legitimate and malicious activity to filter out false positives and reduce noise.
· Correlating Alerts: AI tools can correlate alerts from disparate sources – including those added as part of scale-up efforts – to identify patterns indicating a larger attack campaign, grouping related alerts into a single incident.
Automating Repetitive Tasks
Many of the tasks analysts must perform are menial, repetitive, and don’t require human intervention. AI can automate these tasks, for example, by collecting and parsing large amounts of information, triaging alerts, correlating incidents, integrating threat intelligence, and even automating response actions. Although these are relatively simple tasks, they can be extremely time-consuming, so eradicating the need for analysts to perform them significantly reduces their workload.
Improving Decision-Making and Response Times
Improving decision-making and response times are key when scaling a SOC. AI can help realize these goals by analyzing vast amounts of data and identifying patterns far faster than human analysts could. As a result, analysts receive more accurate, timely alerts and can respond more effectively.
For example, AI-driven systems can automatically recognize attack indicators such as unusual traffic spikes, unauthorized access attempts, or malware signatures. These systems can then automatically trigger defensive actions (such as blocking IPs or isolating affected devices) or present analysts with clear, actionable insights to help them determine the most appropriate response. This capability reduces the risk of human error and speeds up incident response, preventing potential damage from spreading or escalating – all while minimizing the need for manual effort.
Continuous Learning and Improvement
AI is famed for its ability to learn independently over time. It’s the capability that has dominated dystopian sci-fi films, but it’s also the capability that can make the most difference in a cybersecurity context. Essentially, the longer your AI SOC is in operation, the better it will perform, refining detection capabilities, improving the efficacy of threat response, and further reducing workloads for analysts.
Looking Ahead: AI for Long-Term SOC Growth
It’s important to recognize that SOC scaling is not a one-hit job; it’s an ongoing process. Just because you need to scale your SOC now, doesn’t mean that you won’t need to again in the future. AI is the perfect tool for keeping pace with ongoing SOC growth, allowing organizations to scale up operations while keeping costs and workloads down. Integrate AI into your SOC – your analysts, customers, and finance team will thank you.
About the Author: Josh Breaker-Rolfe is a Content writer at Bora. He graduated with a degree in Journalism in 2021 and has a background in cybersecurity PR. He’s written on a wide range of topics, from AI to Zero Trust, and is particularly interested in the impacts of cybersecurity on the wider economy.
Follow me on Twitter: @securityaffairs and Facebook and Mastodon
Pierluigi Paganini
(SecurityAffairs – hacking, SOC)
