By: Michael Monte, Senior Director, Security Field Engineering, Anvilogic
Anyone who has ever managed a Security Operations Center (SOC) is ready for their talent show audition, the winning talent: juggling a dozen spinning plates, represents the relentless challenge of managing multiple responsibilities of SecOps in real-time. The analogy underscores the intricacies of overseeing diverse complexities, multi-tasking, and constant adjustments to ever-changing events and priorities while ensuring seamless team coordination and productivity. Maintaining data security is the highest concern in this fast-paced environment, followed closely by overall team satisfaction/performance and reporting.
Notably, the 2023 Cybersecurity statistics indicate a staggering 2,200 cyber attacks per day, emphasizing the gravity of the threats facing organizations. With each attack costing an average of $9.44M, effective SOC management is critical. Given the overwhelming data volume, a persistent shortage of skilled cybersecurity professionals, and dealing with antiquated foundational SOC architectures/strategies, managers seek more innovative and efficient solutions to navigate this intricate web of challenges and ensure the security of their organization's data and assets.
Challenges: Disorganized Data, Inefficiencies, and Detection Difficulties
One of the most intricate aspects of achieving success for SOC teams lies in their ability to effectively manage data. Initially, consolidating data from various network locations appeared convenient, but proved cumbersome, error-prone, time-consuming, inefficient, and costly when moving data between the cloud and company networks.
In traditional SOCs, the complexity intensifies as teams dynamically track progress in response to the ever-changing threat landscape. The need to create detection mechanisms for new applications and cloud environments presents challenges in defining the right direction for comprehensive data and detection coverage.
For security teams, extracting value from their data is challenging in and of itself. Logging repositories store extensive data, different for every organization, in various formats, languages, systems, and levels of importance to security teams. This results in a cluster of information that makes it hard to see what you have and what’s missing. Consequently, SecOps teams engage in repetitive analysis, leaving them in a perpetual state of reactive responses.
The current status quo for security operations centers is that they have milestones and goals based on the data they have at a particular point in time. They are tracking metrics across data visibility and TTPs in a static state, leading to detection gaps in an ever changing, dynamic, environment. They are challenged to maintain and dynamically understand changes in their detection landscapes. They often don't know what data is needed to build the RIGHT detections. They require guidance on what to prioritize to reduce the largest, most critical risks. They rarely have a way to measure ROI and improvement to SOC maturity or prove the value of their work.
The good news? It is possible to cut through the noise, and the efforts to identify the most critical issues don’t have to be manual.
AI is a factor in the SecOps Formula for Success
How do teams get a handle on all of these factors? Successful SOC teams are finding SecOps success through the integration of AI technologies, such as machine learning (ML), Natural Language Processing (NLP), Anomaly Detection, and Data Science in SOCs, leading to improved efficiency through automated data analysis and real-time threat detection.
AI's ability to rapidly process and analyze vast amounts of data results in more accurate threat identification, minimizing false positives and reducing response time to genuine security incidents. That helps empower teams and managers to anticipate potential threats and implement preemptive measures that help strengthen the overall resilience of their security infrastructure.
How To Incorporate AI to Scale SecOps in a SOC
The deployment of AI empowers analysts by offering insights for detection, enabling informed decisions on the highest priority actions to result in the most scalable and cost effective approach. Start by optimizing your data storage which also aligns with cost-efficiency, mitigates vendor dependency, helps identify areas of improvement, and determines prioritization.
Data management is crucial for SOC operations as they enable comprehensive analysis and timely threat detection. AI plays a pivotal role in this process by facilitating data correlation and assisting with data analysis & development of detections without necessitating the relocation of all data to a central repository.
SOC teams need tailored guidance on what threats to prioritize. AI analyzes your specific environment and priorities to offer recommendations on which threats to address first. These recommendations are based on data-driven insights, ensuring that SOC teams make informed decisions on how to allocate their resources effectively.
AI helps automate the analysis of data feeds to identify coverage gaps and areas for improvement. By continuously assessing data sources, AI can recommend which data feeds are essential for threat detection and where there are visibility gaps that are crucial for detections and investigations. This not only improves the quality of threat data but also reduces unnecessary logs and storage costs. SOC teams can be confident that they have the necessary visibility to be successful in identifying adversarial threats.
AI's ability to assist with alert correlation is pivotal. With the assistance of AI, you can automatically identify patterns and relationships between various alerts, helping SOC teams quickly grasp the bigger picture of an ongoing security event, allowing for a more effective response to sophisticated threats.
Through advanced algorithms, AI can access and process distributed data sources, ensuring that relevant information is aggregated and analyzed in real-time, enabling SOC managers to help the team prioritize better, so that the firm identifies potential threats and anomalies without compromising current workflows, data security or incurring unnecessary storage costs. This approach allows for a more agile and responsive security framework, fostering effective decision-making based on a comprehensive understanding of the distributed data landscape.
Your Maturity Score Matters: The “Credit Score” for The SOC
Aside from getting a handle on data, another challenging facet of the SecOps formula for success is understanding what to prioritize. Think about if every email that came into your inbox was marked with high importance, how would you really know which ones to turn your attention to first? The value for “high importance” would be thoroughly watered down.
Arm your detection engineers and analysts with the analytics to understand what is going on in their environment:
Step 1: Don’t look to centralize raw data in one location for security analytics, but instead, centralize the alerts, where they can easily be managed, allowing for better visibility into your events of interest or suspicious events derived from your vendor alerts & detections across various logging repositories, so that you can easily build a narrative of what is actually critical. It doesn’t matter where the data sits; what matters is that it can be analyzed in one location after it is normalized to a schematized format, enriched, and tagged the way you want it.
Step 2: Dynamically track your detection landscape, so that you understand your detection gaps, and build detections that are “most bang for their buck.” In other words, think back to that inbox full of high importance emails. If you knew the two that you could reply to first that would really move that needle and help your SecOps maturity, that would be invaluable information in a sea of exclamation marks that all appear equal.
Step 3: Assign a “continuous maturity score.” This is like a living breathing credit score for the SOC - where the score signals how well the security posture of an enterprise is based on data visibility and quality, to detections and productivity.
You can’t boil the ocean, and trying to do so might result in a current taking you under. So, SOC teams should leverage the power of AI to help guide their ship in the right direction to navigate stormy seas with GPS, versus weathered and out of date paper maps.