SOCs are failing by design

Security Operations Centres (SOCs) are the frontline defence for organisations against cyber threats. However, the traditional model of SOCs is fundamentally flawed. Designed to manage and investigate cybersecurity alerts, SOCs often reduce highly skilled analysts to mere triage operators. This not only leads to analyst burnout but also leaves organisations vulnerable to sophisticated attacks that slip through the cracks.

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The alert-centric trap

Traditional SOCs are built around an alert-centric model. Metrics like "number of alerts per day," "true positives vs. false positives," and "average time to triage" dominate the followed KPIs. While these metrics provide quantifiable data, they miss the bigger picture: the effectiveness of the organisation's overall security posture.

Focusing on alerts turns SOCs into reactive units, constantly chasing after the next notification rather than proactively strengthening defences. This reactive approach is a failing by design. Analysts spend their days sifting through endless alerts, many of which are false positives, leaving little time for strategic initiatives like threat hunting, vulnerability management, or improving security protocols.

At the core of the problem is the investigation process. Each alert demands attention, requiring analysts to piece together disparate pieces of information from various sources—IPs, domains, user activities, and more. This manual process is time-consuming and prone to human error. Moreover, it doesn't scale. As organisations grow and the volume of data increases, the ability of human analysts to keep up diminishes.

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Automating investigation with AI

Artificial Intelligence offers a transformative solution to these challenges. By automating the investigation process, AI can handle the heavy lifting, freeing analysts to focus on more strategic tasks.

• Automated Data Enrichment: AI can automatically gather and correlate data from various sources—SIEMs, EDRs, network logs, and more—to build a comprehensive picture of each alert.
• Graph-Based Correlations: Using graph-based AI, relationships between entities (IPs, domains, users) can be visualised and analysed to identify patterns and connections that might be missed manually.
• Dynamic Defence Optimisation: AI doesn't just investigate; it learns. By analysing patterns across multiple alerts, AI can provide insights into recurring vulnerabilities, enabling organisations to proactively adjust their security posture.
• Concise Reporting: AI-generated reports can present findings in a clear, concise manner, highlighting the most critical information and providing a transparent view of the investigation path and conclusions.

With AI handling the routine investigations, the role of the SOC analyst shifts from a reactive triage worker to a proactive security strategist. Analysts can focus on:

• ‍Threat Hunting: Actively seeking out potential threats before they become incidents.
• Security Posture Improvement: Implementing changes to policies, configurations, and practices to enhance overall security.
• Incident Response Planning: Developing and refining response strategies for when significant incidents occur.

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Overcoming adoption challenges to build the future of SOCs

Transitioning to an AI-driven SOC model isn't without challenges. Concerns about job displacement, trust in automation, and integration with existing systems must be addressed.

• Augmentation, Not Replacement: AI is a tool to enhance analysts' capabilities, not replace them. It handles the mundane tasks, allowing analysts to apply their expertise where it matters most.
• Transparency and Explainability: AI systems should provide transparent insights into how conclusions are reached, building trust with the human team
• Seamless Integration: Solutions should integrate with existing tools and workflows, minimising disruption and facilitating adoption.

The integration of AI into SOC operations represents a paradigm shift. It's an opportunity to redesign SOCs to be proactive, strategic units that enhance an organisations security posture rather than just reacting to alerts. By automating investigations and leveraging AI-driven insights, SOC teams can:

• Enhance Security Posture: Use data-driven insights to strengthen defence proactively.
• Reduce Alert Fatigue: Cut down the overwhelming number of alerts that require manual review.
• Improve Response Times: Quickly identify and respond to genuine threats.
• Empower Analysts: Allow security professionals to focus on high-impact work.

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The traditional SOC model is unsustainable in the face of ever-growing cyber threats and data volumes. By embracing AI and reimagining the role of the SOC, organisations can move from reactivity to proactivity. This not only improves security outcomes but also makes better use of the valuable human talent within security teams.

It's time to rethink the SOC and embrace the transformative power of AI.