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From Manual to Mature: The Steps to Steer the SOC to Success

This guest blog was contributed by Michael Monte, Senior Director of Security Field Engineering, Anvilogic What’s keeping CISO’s up at night? Well, on any given night it could be a number of things—the overwhelming data volume their organization is working to manage, a shortage of skilled cybersecurity professionals, outdated SOC architectures and strategies, or the staggering cost/prevalence of cyber attacks (2,200 cyber attacks per day, with each attack costing an average of $9.44M).  

The common denominator for these challenges is that effective management of the SOC (Security Operations Center) can help put a lot of these issues, and the CISO, to bed. 

Navigating this intricate web of challenges to ensure the security of an organization's data and assets doesn’t have to be manual. AI can provide insights to solve detection-related challenges and help prioritize the actions to take that result in the most scalable and cost-effective approach. 

How To Avoid Detection Blind Spots  

No wonder the SOC faces blind spots - if you looked in your rear-view mirror to see what was coming and didn’t change lanes for another five minutes, the changing conditions could lead to a crash. Similarly, when SOC teams use data to make decisions based on static or outdated data instead of real-time data, detection gaps will result due to the environment dynamics. 

But, it’s not just the static state of data that is a problem, the sheer level of data collected is so massive that it’s like drinking from a fire hose. So much of this data isn't useful for building threat detections and can be a distraction to the true story that analysts should be tuned into. 

At the same time, common security data sets like Endpoint Detection and Response (EDR) data, cloud storage access, and undetected VPN activity are often siloed, making it difficult to gain a comprehensive view of the full security landscape.

Static data, siloed data, and data surges – no wonder SOC teams struggle to identify what data is needed to build the RIGHT detections and are challenged to maintain and dynamically understand changes in their detection landscapes. They require guidance on what to prioritize to reduce the largest, most critical risks. 

Organizations can address these challenges by implementing solutions that support multiple platforms for threat detection and integrating their Security Information and Event Management (SIEM) with a data lake to manage high-volume datasets.

How To: Assess Your SOC’s Maturity

Apart from the data deluge that makes navigating detections difficult, the SOC faces another challenge: the scarcity of methods to gauge ROI, enhance SOC maturity, or validate the value of their efforts. 

The solution: SOC teams can take three steps to better understand their environment and mature operations: 

  1. Arm detection engineers with analytics to understand their environment. Where the data sits is not important, what matters is that it can be analyzed in one location after it is normalized to a schematized format, enriched, and tagged.

  2. Not all detections are created equally. Determine which detections will move the needle most towards SecOps maturity amid numerous options labeled “high priority”. 

  3. Lastly, the SOC should be assigned a “maturity score.” This is like the credit score for the SOC. In the same way that that three-digit number showcases your creditworthiness, this score signals the security posture, from visibility, to detections and productivity, based on subcomponents.   

In a sea of conflicting priorities, fire drills, and a culture of constant urgency, SOC teams can’t do it all. Leveraging AI, SOC teams can get the navigation they need to ensure that they are focusing on what really is burning, and not just the smoke. 


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