In the evolving landscape of cybersecurity, Security Operations Centres (SOCs) face unprecedented challenges managing an ever-expanding array of alerts from diverse sources — ranging from cloud environments and endpoints to identity systems and operational technology. Against this complex backdrop, AI-powered SOC platforms have emerged promising speedier triage, intelligent remediation, and reduced noise. However, a deeper look reveals a critical distinction in the AI architectures behind these platforms that greatly influences their efficacy and adaptability: pre-trained AI models versus adaptive AI systems.
Pre-trained AI models are built through extensive training on historical security data focused on specific use cases such as phishing or malware detection. These models act as specialised assistants, highly effective when handling repeatable, well-understood alert types. They can accelerate triage and automate common workflows, offering immediate operational benefits where threat profiles remain predictable. Yet, this strength is also a vulnerability. The static nature of pre-trained models means they struggle with alerts outside their predefined scope and require resource-intensive, manual updates to cover emerging threats—a process described as slow and often insufficient for today’s dynamic threat environment.
This fundamental limitation is echoed across AI applications beyond cybersecurity, with experts noting that pre-trained models can suffer from domain shifts, fixed architectures, and considerable computational demands for fine-tuning. Such models often face challenges adapting to highly niche or rapidly changing data, making their application in the fluid and varied SOC environment problematic. Additionally, concerns regarding bias, privacy, and the inherent inflexibility of pre-trained models highlight the risks of relying solely on such static systems in high-stakes security contexts.
In contrast, adaptive AI represents a transformative approach designed to handle unknown and novel alerts in real-time. It operates through a coordinated system of specialised AI agents that perform dynamic research, semantic classification, and iterative triage akin to senior human analysts. When encountering unfamiliar alerts, these AI agents autonomously investigate by leveraging the latest threat intelligence and reputable sources online, constructing tailored triage procedures on the fly without requiring prior training on the specific alert type. This continuous learning and flexibility equip SOCs to stay ahead amid evolving threats, closing blind spots that pre-trained systems may overlook.
The sophistication of adaptive AI is further enhanced by employing multiple large language models (LLMs), each optimised for different tasks—from parsing structured logs and understanding narrative incident tickets to generating custom remediation scripts. This multi-LLM strategy increases robustness, reduces single-model bias, and offers a resilient, context-aware response capability that outperforms mono-model designs constrained by narrow training sets.
From a business perspective, adaptive AI offers swift time-to-value by covering all alert types immediately without vendor dependency for incremental model updates. This capability translates directly into faster threat detection, quicker responses, and diminished analyst fatigue as routine investigative work is automated, allowing security teams to focus on critical and strategic incidents. Additionally, adaptive AI SOC platforms integrate automated response actions and cost-effective logging solutions, eliminating the need for complex playbook configurations and reducing the high costs and vendor lock-in associated with traditional Security Information and Event Management (SIEM) systems.
While pre-trained AI platforms still find relevance in environments with stable and narrow threat profiles, the consensus among cybersecurity practitioners is that such scenarios are rare. The real-world demands of modern SOCs—marked by diverse data sources, evolving attack techniques, and the need for rapid adaptation—underscore the necessity of adaptive AI frameworks. These frameworks blend automation with human-like research and decision-making processes to deliver comprehensive, scalable security coverage.
However, it is important to balance AI automation with human oversight, especially considering risks like false positives, alert fatigue, and the ethical implications of automated decision-making. Adaptive AI systems, by empowering analysts with enriched context and high-confidence insights, aim to strike this balance, enhancing operational efficiency without sacrificing the nuanced judgment essential in cybersecurity.
Radiant’s adaptive AI SOC platform exemplifies these principles, promising enterprise-grade coverage across all alert types from diverse security tools, coupled with integrated remediation and forensic capabilities. While the platform claims to reduce mean time to response from days to minutes and offers cost-effective logging, the overarching insight is clear: breaking free from the limitations of pre-trained models towards adaptive AI is becoming essential for SOCs striving to match pace with today’s complex threat environment.
In summary, choosing the right AI approach for SOCs is not merely a technical decision but a strategic imperative. Adaptive AI, with its continuous learning and flexible, multi-agent intelligence, provides a pathway to more resilient, efficient, and future-ready security operations—addressing the multifaceted challenges contemporary SOCs face where pre-trained AI tools fall short.
Source: Noah Wire Services
