AI in SOC: Where Should Security Teams Look to Apply It?

The combination of this visibility paired with an AI-powered SOC assistant has caught the attention of security leaders
As threats evolve, AI's continuous learning ensures robust protection that can prove invaluable for security operations centres

AI technologies offer ways to boost productivity in business functions across various sectors. Cybersecurity, far from being an exception to the fact, stands to be one of the biggest beneficiary of the acceleration of AI.

AI is transforming cybersecurity through enhanced threat detection, automated operations, and adaptive defences. Analysing vast data, AI pinpoints malicious patterns and potential attacks with superior accuracy. It automates tasks like vulnerability scans and incident response, reducing team workloads.

Yet, with the growth of AI and its ease-of-use now allowing less sophisticated bad actors abilities to hack, the only way cybersecurity teams can keep up is to fight fire with fire. 

As threats evolve, AI's continuous learning ensures robust protection. This potential is invaluable for security operations centres (SOCs) monitoring and responding to cyber threats in real-time.

SOCs, responsible for defending against cyber threats, today face a relentless stream of attacks while juggling complex security tools, massive data volumes, and a shortage of skilled analysts. 

In this environment, a purpose-built GenAI assistant designed as a security platform could empower teams to operate with the speed required to counter attackers.  

However, AI's effectiveness hinges on the quality of data it analyses. Fortunately, SOC operations are undergoing modernisation, delivering unprecedented visibility into security events across the enterprise. 

The combination of this visibility paired with an AI-powered SOC assistant has caught the attention of security leaders.

 XDR and AI’s combination

The growing adoption of extended detection and response (XDR) platforms lies at the core of SOC modernisation efforts. XDR solutions correlate security telemetry across domains like identities, endpoints, SaaS applications, email, and cloud workloads, providing unified detection and response capabilities.  

XDR platforms can leverage AI to correlate cross-domain security signals, accounting for the full attack chain to identify threats with high confidence. This contrasts with traditional automated detection solutions often relying on a single compromise indicator. The increased fidelity AI provides significantly improves signal-to-noise ratio, resulting in fewer false positives requiring manual investigation.

Notably, more data available for AI analysis translates to greater effectiveness. Thus, achieving broad XDR coverage is crucial to unlocking AI's full potential.

Purpose-built GenAI for SOC

Integrating GenAI into the SOC could transform how security analysts operate. They could leverage GenAI to summarise incidents, assess impact, provide actionable recommendations for faster investigation and remediation, and generate post-response reports. 

Guided assistance could also help analysts at all levels develop skills for complex tasks like threat hunting and malware reverse engineering. With AI-driven threat intelligence, analysts could inquire about emerging threats and their organisation's exposure in natural language, gaining contextualised insights to aid response efforts.

In randomised controlled trials of its Copilot for Security, Microsoft found security professionals were 22% faster across tasks when using the tool. Furthermore, 97% of participants wanted to use Copilot for similar future tasks.  

While opportunities abound, execution must reinforce that AI will augment, not replace, human analysts in the SOC, despite studies stating to the contrary. This requires thoughtfully integrating GenAI into existing workflows while ensuring accuracy and transparency. SOC teams must maintain control over investigating, remediating, and restoring assets.

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Advancing AI in SOC

In this rapidly evolving space, a forward-looking implementation strategy can help innovative organisations leverage current AI capabilities while preparing to seamlessly adopt future innovations.

An effective strategy should account for high-risk areas, cybersecurity maturity, existing architecture, tools, budgets, and other factors. While phased implementation minimises disruption, organisations should consider ensuring broad XDR coverage to optimise AI investments.  

Leading organisations will take a human-centric approach, centring on analysts' needs. Tracking and measuring AI's SOC impact can refine use cases and user experience. Organisations could compare team metrics six months before and after GenAI adoption, tracking areas like mean time to respond, incidents per day, and average resolution times. 

This can help security teams understand where AI provides value, and focus their efforts there, rather than apply a blanket approach, which could see wasted resources in both implementation and monitoring.


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