Published on January 4th, 2025
Introduction
Security Operations Centers (SOCs) are the first line of defense against cyber threats. However, analysts in these centers face a growing problem—alert fatigue. A large number of alerts are false positives, overwhelming security teams with notifications that don’t signal actual threats. This leads to stress, confusion, and missed opportunities to address real security incidents. As attacks grow more advanced, such as those using Living off the Land Binaries and Scripts (LOLBAS), traditional detection methods are proving ineffective. This article discusses why SOCs need a new approach to threat detection and how AI can offer a crucial solution.
The Burden of Alert Fatigue
A recent report revealed that a fifth of all alerts in SOCs are false positives. This is a significant issue. False positives waste valuable time and resources, draining the morale of security teams. When analysts are overloaded with alerts, they may not have the time to investigate each one thoroughly. As a result, critical security incidents can be overlooked or delayed, weakening the organization’s defenses.
One key reason for the flood of alerts is the way analysts work. They often treat each alert individually, without seeing the bigger picture. This approach leads to missed patterns that may indicate a larger, ongoing attack.
Detection Failures in Modern Attacks
Modern cyber-attacks are more sophisticated and harder to detect. For example, attackers using LOLBAS can infiltrate systems without introducing new code. This makes it difficult for traditional detection tools to spot them since they use legitimate system functions for malicious purposes.
Furthermore, SOC analysts often work on individual alerts without considering their relationship to one another. While this approach is useful for single events, it can prevent analysts from spotting coordinated attacks. Attackers can move through a network undetected, causing more damage as time goes on.
A New Approach: AI and Hypergraphs
To overcome these challenges, SOCs need to move away from the traditional alert-by-alert model and embrace new methods, such as AI. AI tools, including machine learning, agents, graphs, and hypergraphs, can improve how threats are detected, analyzed, and responded to.
Hypergraphs are a powerful tool for threat detection. They connect different observations, such as alerts from various security tools, into a unified view of potential threats. Hypergraphs map out event chains, scoring them based on factors like the frequency of suspicious activity. For example, if suspicious activity occurs repeatedly on the same workstation, the hypergraph gives it a higher score, suggesting it could be part of a larger attack.
By linking these detections to frameworks like MITRE ATT&CK, hypergraphs help SOC teams track the progression of an attack and better understand its scope. This broader perspective allows analysts to recognize whether an alert is part of an escalating threat, reducing the risk of missing vital indicators.
Augmenting the Analyst with AI
AI doesn’t replace the analyst; instead, it augments their ability to detect and respond to threats. Instead of dealing with hundreds of isolated alerts, analysts can use AI to focus on understanding patterns and complex chains of events. Machine learning helps prioritize the most critical threats, ensuring that analysts focus their efforts on the most pressing issues.
Generative AI can assist in incident response by suggesting potential actions for remediation. This helps ensure that responses are both consistent and effective, speeding up the resolution of security incidents.
The most important benefit of this approach is the reduction in false positives. AI can analyze data from multiple sources and correlate alerts, making it easier to distinguish between real threats and harmless activities. This minimizes the noise that typically floods SOCs, allowing teams to focus on what truly matters.
The Benefits of AI in SOCs
- Reduced Alert Fatigue: AI handles much of the alert analysis, freeing analysts to focus on higher-level tasks and dramatically reducing alert volume.
- Better Detection of Sophisticated Threats: Modern attacks, including those using LOLBAS, can be detected more effectively with advanced correlation techniques like hypergraphs.
- Faster Response: AI helps prioritize alerts and speeds up decision-making, allowing for quicker incident response and damage mitigation.
- Increased Efficiency: As AI tools improve, they become more accurate over time, reducing the need for manual intervention and lowering operational costs.
The Need for CISO Leadership
For CISOs to unlock the full potential of AI in their SOCs, they must first recognize that traditional methods of detection are no longer enough. They must be open to exploring new technologies beyond Large Language Models (LLMs). While LLMs have their uses in generating reports, they are not suitable for solving the fundamental issues in threat detection. By integrating machine learning, hypergraphs, and other AI techniques, CISOs can transform their SOCs into more proactive and efficient operations, capable of dealing with even the most sophisticated threats.
Conclusion
As cyber threats become more advanced, SOCs need to rethink their approach to detection. The combination of AI, machine learning, and hypergraph analysis offers a powerful solution to the issues of false positives, alert fatigue, and missed attacks. By augmenting analysts with AI-driven insights, SOCs can operate more efficiently, reduce the burden on their teams, and respond faster to emerging threats. However, this transformation requires a shift in mindset. CISOs must move beyond seeing AI as just a buzzword and instead use it as a vital tool in defending against cyber threats. With the right leadership, AI can help SOCs tackle the challenges they’ve faced for years and strengthen an organization’s security defenses.
