Automating the SOC Without Losing the Human in the Loop

Security operations have always been a high stakes balancing act. The need for rapid detection and response runs up against alert overload, talent gaps and siloed toolsets. Many organizations have turned to automation to manage the load, but traditional approaches haven't always been scaled as expected.

Security Orchestration, Automation and Response (SOAR) platforms, for example, have provided valuable frameworks for codifying workflows and reducing manual effort. However, for many teams, especially those with limited engineering support, SOAR implementations can become overly rigid, requiring significant time to maintain as tools, APIs and threat models evolve. This doesn't make SOAR obsolete; in fact, many mature SOCs benefit from it. But it does highlight the need for complementary approaches that offer more flexibility, especially for smaller or resource-constrained teams.

What’s becoming clear is that a middle ground between full automation and manual triage is not only possible, but preferable: a space where machines do more of the heavy lifting, while humans stay firmly in control.

The emergence of agentic workflows 

Agentic workflows refer to a new approach that integrates artificial intelligence (AI), specifically, large language models, into the daily cadence of SOC operations. Rather than relying on static automation scripts or rule-based playbooks, LLMs are used to guide, summarize and adapt security investigations in real time. 

These workflows are enabled by new interoperability standards, such as the Model Context Protocol (MCP). MCP allows security tools to expose their capabilities in a format that LLMs can understand and interact with. In practice, this means an LLM can query a SIEM, gather logs, analyze trends and propose next steps — all within a single, intuitive interface. The model can assist an analyst in interpreting complex data, identifying anomalies or suggesting follow-up actions, while the human remains the final decision-maker. 

This collaboration enables security teams to streamline repetitive tasks, enrich data interpretation and reduce the burden of tool-switching and cognitive overload. It is not about replacing security analysts.  

Benefits for analysts and leadership alike 

The practical value of this approach is different depending on your role in the organization; however, the benefits are both operational and strategic. For frontline analysts, agentic workflows offer a dramatic shift in how they interact with security data.

Rather than jumping between multiple tools, manually collecting logs and reconstructing context from raw data, an analyst can use a chat-based interface to ask direct questions of their environment. The system can retrieve data, correlate relevant signals and provide a structured summary. This frees  the analyst to focus on interpretation and escalation. 

It also accelerates triage and increases accuracy. Contextual cues that might have been missed during a rushed manual review now appear automatically. Analysts can also explore hypotheses more thoroughly, ask the system to investigate related indicators, or simulate different response scenarios without additional overhead. 

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From a leadership perspective, this model helps address two persistent challenges: scaling effectiveness without increasing headcount and reducing dependency on highly customized automation infrastructure. Semi-automated workflows powered by LLMs require fewer customized integrations and can adapt more easily to changing environments. This is particularly valuable for organizations that struggle with the operational overhead of traditional automation platforms. 

Other benefits include enhanced visibility and governance. Since every LLM-guided workflow is executed transparently, and with human review built in, leaders can maintain oversight while giving teams more autonomy. And as regulatory and risk expectations evolve, having auditable, explainable decision paths becomes a strategic advantage. 

Why now and why it’s more accessible than ever

This isn't a vision of the distant future. Recent developments in AI model performance, combined with open standards like MCP, have made these capabilities real and practical — even for teams without large automation budgets. 

The barrier to entry is lower than ever. Small and mid-sized SOCs can adopt agentic workflows without the need for in-house developers or expensive orchestration stacks. Many LLM interfaces can be deployed using existing infrastructure, and MCP-enabled tools are becoming more common across the ecosystem. 

The initial implementation steps are straightforward: start by connecting LLMs to your security data sources, particularly SIEM or log aggregation platforms. Begin with use cases like log summarization, initial triage, or alert enrichment. These are areas where repetitive manual work can be offloaded safely. Then, incrementally layer in additional tools such as endpoint detection platforms or vulnerability scanners. All the while, maintain clear boundaries: human analysts review, validate and direct each step. This approach doesn't eliminate your current investments but instead complements them, often making existing platforms easier and faster to use. 

Keeping humans in the loop 

Despite the excitement around AI, there are good reasons to keep humans involved. LLMs are powerful reasoning engines, but they are also probabilistic systems prone to error without context or oversight. They excel at identifying patterns, drafting summaries and suggesting options. However, they are not consistent with making critical risk decisions in isolation.

Agentic workflows embrace this reality. They are designed to enhance, not override, human judgment. Analysts remain the authority. LLMs gather data, form hypotheses and ask for direction; they don’t act independently. For leadership, this structure supports responsible innovation while maintaining governance. It also ensures that automation doesn’t become a black box, and that every decision can be traced, audited and explained. 

A strategic shift for modern security

The shift toward agentic workflows is more than just tools. It reflects a broader evolution in how we define effective security operations. 

Agentic workflows empower humans with better interfaces and reduce the barriers between questions and answers. The workflows create systems that are dynamic, interpretable and resilient to change. This shift is about recognizing that while AI can do more of the heavy lifting, people remain central to security outcomes. 

Whether you’re a CISO looking to stretch your team’s capabilities, or an analyst looking for better ways to manage your alert queue and avoid fatigue, the rise of human-in-the-loop automation offers a compelling path forward.

We may never want, or need, a fully autonomous SOC but we do need smarter, faster, more adaptable ones. And that starts with workflows that put the human front and center with support from intelligent, transparent automation.