What SOC Analysts Actually Want From AI

When asked about the #1 expected benefit of agentic AI, security leaders didn’t say faster detection or better MTTR. They said quality of life. This finding comes directly from 450 CISOs and cybersecurity leaders surveyed in the recently published 2026 AI SOC Leadership Report.

There’s no shortage of AI in today’s security operations center (SOC). Generative AI. LLM copilots. Agentic workflows. Custom-built agents. Vendor-driven automation. The SOC is saturated with intelligence, at least in theory. And yet, ask SOC analysts how things feel on the ground, and the answer is far more complicated.

Nearly four in five organizations are now using AI in their SOCs in some capacity, and many have embedded it across workflows. While AI adoption has surged, operational clarity has not kept pace. Instead of simplifying operations, AI has introduced a new layer of complexity: more tools, more outputs, more decisions to validate. This is the paradox at the heart of the modern SOC: 

To understand why, you have to look past adoption metrics and into what analysts are actually experiencing, and more importantly, what they actually want.

AI Is Everywhere, But It’s Fragmented

On paper, the SOC has embraced AI. In practice, it’s stitched together from disconnected parts. The SOC now runs an average of 7 AI-powered SOC tools, and 80% of teams rely on fragmented point solutions. These tools operate independently, each with its own interface, logic, and version of reality.

No single system can see the full picture, so analysts rush in to fill the gap. SOC staff become the integration layer, manually correlating signals, validating outputs, and reconciling conflicting conclusions across tools. Operational overhead, the very thing AI was supposed to eliminate, has been reintroduced.

This is not a failure of AI capability, but a failure of architecture.

The Analyst Experience: From Operator to Orchestrator

AI is reshaping the role of the SOC analyst. Previously, analysts were the execution layer, spending their time triaging alerts, enriching data, and running playbooks. AI now handles much of that processing. In its place, a new layer of work has emerged: oversight, validation, and decision-making.

On average, analysts now spend 8.6 hours per week reviewing AI-generated outputs. At first glance, that can look like inefficiency: a full workday spent checking the machine’s work. But that interpretation misses the shift that’s actually happening.

Analysts are moving from execution to judgment. From doing the work to deciding what matters. If AI does the lion’s share of the previously manual, repetitive tasks, SOC capacity expands. AI saves more than the 8.6 hrs per week that humans spend on oversight.

This is progress, and this is only the early innings. Nearly 9 in 10 security leaders say AI has improved workload and reduced burnout. But this progress comes with a condition: the oversight-for-execution trade-off only works if it’s efficient.

When AI outputs are opaque, inconsistent, or fragmented, oversight becomes a source of friction. When reasoning is clear and context is unified, oversight becomes strategy.

What Security Leaders Say Their Analysts Need Most

Strip away the noise, the AI hype, and dashboards, and a clear picture emerges of what analysts actually need. When 450 security leaders were asked what would most improve SOC operations, the answers weren’t about faster models or more automation. They pointed to the conditions their teams need to actually do their jobs.

1. Better Quality of Life

At its core, the SOC remains a human system. And the leaders running these teams are explicit about what would improve it: 

• Fewer repetitive, manual tasks
• Better workload distribution and prioritization
• More sustainable work-life balance

These objectives reflect a daily reality of alert fatigue, context switching, and cognitive overload. AI has the potential to solve these problems, but only if it reduces friction, not adds to it.

2. AI They Can Trust

Trust is the defining constraint of AI in the SOC. Full stop.

Only a small fraction of leaders report zero concerns about AI. The vast majority point to issues like:

• Data privacy risks
• False negatives (missed threats)
• False positives
• Black-box decision-making

The common thread? Visibility. Transparency. Explainability. Analysts and cybersecurity leaders don’t just want answers. They want to understand how those answers were reached. In fact, 90% of security leaders say they need explainability to trust AI decisions.

Because in security operations, decisions carry consequences. And confidence comes from clarity.

3. Control Over Automation

Despite widespread belief in AI’s capabilities (here is your friendly reminder to download the 2026 AI SOC Leadership Report for the supporting details), most teams are cautious about letting it act autonomously.

Nearly all organizations are comfortable with some level of AI-driven action, and most draw a hard line at medium-severity incidents. Not only is the aforementioned trust factor at play, but also a lack of control.

Today’s tools often present a binary choice: AI acts, or humans act. That’s hardly a choice when the stakes are high.

What analysts actually want is a dial. They want to calibrate autonomy based on:

• Severity
• Confidence
• Context

Low-risk, high-volume alerts? Let AI handle them end-to-end. High-risk, high-impact incidents? Keep humans in the loop.

Not all automation is, or even should be, equal. Analysts demand the flexibility to decide where the line is drawn, and to move it over time at their discretion. See also, judgment layer.

4. Fewer Tools, More Cohesion

Perhaps the most consistent signal across the data is this: 85% of security leaders would prefer a unified platform over multiple point solutions.

Let us be crystal clear: no one is suggesting replacing existing tools. EDRs, CNAPPs, and other security controls serve critical functions. The best are exceptional at their prescribed function. The issue is what sits above them, or rather, what does not.

Most SOCs today do not have a unified layer that:

• Sees across the full stack
• Correlates fragmented signals
• Provides consistent reasoning
• Enables coordinated action

So analysts jump into that void. And teams are back to manual, repetitive tasks in the effort to stitch together context spread across data siloes. The previously mentioned tradeoff between execution and oversight falters, diminishing the value of what AI could otherwise deliver.

The Real Bottleneck: Trust, Not Tech

One of the most striking findings in the data is the dichotomy between what teams believe AI can do and what they actually allow it to do. To wit, even though 97% believe AI can handle alert triage, only 35% are using it for that purpose.

This pattern repeats across the SOC. (Did you even download the 2026 AI SOC Leadership Report?) AI is widely trusted to analyze, investigate, and recommend. It’s far less trusted to act. 

Organizations lack confidence in how AI operates. Trust breaks down when:

• Decisions cannot be explained
• Data access is not governed
• Outputs cannot be verified
• Control boundaries are not clear

In other words, AI has the ability. Analysts just don’t trust it to do the right thing. 

The SOC Analysts Are Asking For: Unified, Explainable, Controllable

Despite the challenges, there is remarkable alignment on what the ideal SOC should look like. Across roles, industries, and geographies, the vision is consistent for a system that is:

• Unified across the entire security stack
• Explainable in every decision it makes
• Adaptive, learning from outcomes over time
• End-to-end, covering the full alert lifecycle
• Controllable, with adjustable levels of autonomy

This blueprint for the AI SOC is laid out clearly in the research findings and reflects a fundamental shift in how AI is expected to function within it.

The security industry has spent the last several years racing to embed AI into every corner of the SOC. That tinkering or adoption phase is over. The next phase will make that intelligence scalable, usable, and trustworthy for the enterprise.

Enterprises demand AI that:

• Shows its reasoning (transparency)
• Operates within clear boundaries (control, guardrails)
• Augments the SOC (capacity, throughput, efficiency)

Organizations that close these gaps, moving from fragmented tools to a unified AI SOC platform, from opaque outputs to transparent reasoning, and from brittle automation to adjustable autonomy, will unlock the outcomes that AI was always expected to deliver. Faster response. Lower risk. Higher analyst productivity.

The rest will continue to manage complexity, just with smarter tools. Smarter tools are only valuable when they make the system itself — in this case, the SOC — smarter.

That’s what SOC analysts actually want.