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 daily, emphasizing the gravity of organizations' threats. The need for innovative solutions in SOC management is more critical than ever, motivating managers to seek more efficient and forward-thinking solutions.
Challenges: Disorganized Data, Inefficiencies, and Detection Difficulties
One of the most intricate aspects of achieving success for SOC teams is their ability to manage data effectively. 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.
The introduction of AI has significantly enhanced SOC operations by improving detection mechanisms for new applications and cloud environments.
Extracting value from security teams' 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 their data at a particular point in time. They track metrics across data visibility and TTPs, leading to detection gaps. They are challenged to maintain and dynamically understand changes in their detection landscapes. They often don't know the data needed to build the proper 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 manage all these factors? Successful SOC teams are finding SecOps success through integrating AI technologies, such as machine learning (ML), Natural Language Processing (NLP), Anomaly Detection, and Data Science, in SOCs. This leads 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. This empowers 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. You can start by optimizing your data storage, which also aligns with cost efficiency, mitigates vendor dependency, helps identify areas of improvement, and prioritizes them.
SOC teams need tailored guidance on what threats to prioritize. AI analyzes your specific environment and priorities to recommend which threats to address first. These recommendations are based on data-driven insights, ensuring that SOC teams make informed decisions on allocating their resources effectively.
AI's ability to correlate alerts with objects of interest, such as persistent threats, is crucial for understanding the sequence of events and potential attack vectors. This capability is especially valuable for complex threats involving multiple stages or tactics. AI can automatically identify patterns and relationships between various alerts, helping SOC teams quickly grasp the bigger picture of an ongoing security incident, allowing for a more effective response to sophisticated threats.
Furthermore, AI helps automate data feed analysis 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 enhancements are needed. This improves the quality of threat data and reduces unnecessary logs and storage costs. SOC teams can be confident that their data feeds are optimized for maximum effectiveness. AI can access and process distributed data sources through advanced algorithms, ensuring that relevant information is aggregated and analyzed in real-time. This enables SOC managers to help the team prioritize to better identify potential threats and anomalies without compromising current workflows, data security, or 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. If every email that came into your inbox was marked with high importance, how would you know which ones to turn your attention to first? The value of “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 and detections across all potential alerts to build a narrative of what is critical. It doesn’t matter where the data sits; what matters is that it can be analyzed in one feed and that the analytics on the data is based on the current environment and the current state.
Step 2: Show them what/how to prioritize so the detections get the “most bang for their buck.” In other words, think back to that inbox full of high-importance emails. If you knew the two you could reply to first, that would 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—it signals how well an enterprise's security posture is based on data visibility and quality, 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.
