AI in the SOC

Two Schools of Thought: Autonomy vs Augmentation

Gartner frames the AI SOC landscape as a dichotomy: providers pursuing full SOC replacement versus those building AI products to augment existing staff. Of these two approaches, only augmentation aligns with real-world security operations. It helps analysts triage alerts, investigate faster, enrich context, and summarize incidents with better consistency, all while keeping humans in the loop, even if their day-to-day efforts change. Autonomy, by contrast, presumes reliable, affordable, end-to-end decisioning without continuous human oversight, something the technology and organizational constraints don't support today. 

Why the “Autonomous SOC” Doesn’t (And Won’t) Work

There's been a surge of AI SOC vendors claiming that human analysts are no longer necessary, yet many of these same companies aren't frantically hiring humans to build out their own SOCs. At first glance, it may seem simple: just hire analysts and set up 24-hour shifts. In reality, it's far more complex. Post-automation security investigations are only one aspect of what a well-functioning Security Operations team does. 

Businesses want access to experts, especially as the lines blur between a Level 3 SOC analyst and a full-fledged DFIR specialist. They need direct support from SOC teams for questions only humans can answer. Behind the scenes, teams of security content engineers and customer service experts help businesses navigate context and reporting that eventually reaches senior leadership. With hundreds of tickets a day, ranging from complex issues like event flow disruptions caused by a third party's firewall update breaking the log parser into the SIEM, humans have a crucial role to play. How many security teams have found themselves piecing together an investigation only to discover that critical data has been missing from their SIEM for six months? 

We're all comfortable using apps for routine self-service tasks, banking, managing cell phone accounts, and few of us want to call those companies. But when things truly go wrong, businesses need access to real experts: individuals with not just technical skills, but crisis management experience earned from thousands of investigations. 

Additionally, because the cost of false negatives is unacceptable, security leaders will not implement solutions without clear oversight and governance. Even where AI can execute parts of a workflow, organizations still need process controls, quality checks, and human judgment for complex and novel investigations. Even when supplemented by deterministic logic, a fully autonomous model cannot guarantee consistent outcomes (that is, remediation actions with business impact) for every scenario. Reachback to a human security expert remains an indispensable failsafe. 

It cannot be ignored that accuracy risks persist. Hallucinations, incorrect assumptions, and mis-prioritizations can introduce material risk without robust safeguards. Deterministic models leveraged where applicable layered with human-in-the-loop oversight is essential to catch and correct errors. 

In short, fully autonomous SOCs ask organizations to replace the human judgment and accountability that are core to cybersecurity operations with AI that is still emerging. That trade-off isn't just impractical, it's undesirable. 

Why AI in the SOC Is Non-Negotiable

At the same time, ignoring AI is not an option. Used correctly, AI augments human teams and delivers measurable improvements where SOCs need them most: 

Workload reduction: AI can take on repetitive, high-volume tasks like alert triage, dynamic enrichment, and report generation, freeing analysts to focus on higher-value work. 

Process consistency: AI helps standardize tasks across varying skill levels, bridging gaps in tool syntax and operating procedures, so teams perform more consistently. 

Improved alert quality: AI can bring in external threat intel, control telemetry, and asset context to reduce false positives and prioritize more effectively. 

Faster decision-making: Attack timelines, path mapping, and context-rich summaries help analysts get to scope, impact, and containment faster. 

Knowledge retention: AI working “with” humans captures operational insights over time, mitigating churn and preserving institutional knowledge. AI can detect patterns that humans miss and recommend rules and remediations to account for them. 

Where to Deploy AI Augmentation First

Focus on high-impact, high-friction workflows that benefit from AI support and human oversight: 

Alert triage: False positive reduction, dynamic enrichment, and contextual prioritization (threat intel, exposure, asset use). 

Augmented investigations: Natural language querying, attack path/timeline visualization, and suggestive query generation to speed root-cause analysis. 

Incident and case summarization: Auto-generated executive and GRC-ready reports that consolidate findings with clear context. 

Hypothesis generation: An always-on analysis of patterns and behaviors can surface new detections, new investigative flows, and new remediations for human approval.  

Operational oversight: AI that learns desired procedures and flags process deviations or underperformance for leaders to address. 

Response recommendations: Context-aware guidance and playbook generation, with optional integration-driven execution under human control. 

The Bottom Line 

BlueVoyant's SOC conducts two million human-led investigations each year, even after automating 96 percent of cases with nearly perfect accuracy. It's clear that relying solely on tools to solve these problems isn't feasible. We'll continue pushing our automation rate higher as the right technology becomes available, but reaching 100 percent is unrealistic. In security investigations, there's always the challenging "last mile"—and the last mile is arguably the most important. Automation and expertise are always running, and running faster than ever, but BlueVoyant delivers better, actionable outcomes that are customer-centric. While that last mile may evolve, eliminating it altogether is wishful thinking. 

Autonomous SOCs are not the right goal. The future is human-led, AI-augmented operations that deliver consistent, measurable improvements without sacrificing oversight and accountability. Security teams that embrace AI in targeted, outcome-driven ways will scale faster, reduce risk more effectively, and retain hard-won institutional knowledge, even as threats and tooling evolve. 

If you'd like to compare notes on AI adoption in the SOC or share the challenges you're prioritizing, get in touch—we're compiling practitioner insights to inform the next posts in this series.