COMMENTARY: For years, the security industry has treated trust in automation as something to be earned slowly and cautiously. That instinct made sense when the pace of threats was manageable — when analysts could keep up with alerts, and investigations unfolded on human timelines. That world no longer exists.
Today’s SOC operates under a level of pressure that is fundamentally different. Attackers are faster, more adaptive, and increasingly automated. Defenders, meanwhile, are still expected to manually triage alerts, correlate signals, and make high-stakes decisions in real time. The gap between what organizations face and what human teams alone can handle is widening, and quickly.
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This is the context in which AI has moved from an interesting capability to an operational necessity. The question is no longer whether
AI belongs in the SOC, but how quickly organizations can adopt it in a way they can actually trust.
Trust is built through use
One of the biggest misconceptions about AI in security operations is that trust is something CISOs either grant or withhold upfront. In reality, trust is built the same way it is for any critical system: through controlled rollout, validation, and feedback.
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The most effective organizations aren’t rolling out AI across the SOC overnight. Instead, they control the automation and then see how AI handles every type of investigation, then decide which ones to trust enough to fully automate, running AI-driven investigations in parallel with human processes, then compare outcomes, and give feedback. Where the system performs well, they expand its role and where it falls short, they refine it.
This “verify, then trust” model reflects discipline and allows security teams to replace abstract concerns with measurable confidence. Over time, consistent results do more to build trust than any vendor claim ever could.
Human oversight is non-negotiable
Despite the momentum behind AI, CISOs are not looking to hand over control of the SOC, nor should they. Human oversight remains essential, particularly when decisions move beyond initial triage into deeper investigation and response. There is also a more subtle risk at play: as AI systems become more capable, their outputs can appear authoritative, especially to less-experienced analysts. Without proper guardrails, that can lead to over-reliance on results that may be incomplete or, in some cases, incorrect. Keeping humans firmly in the loop ensures that AI remains a tool for judgment and not a substitute for it.
Explainability: The price of admission
If there is one clear line CISOs are drawing, it’s that black-box AI has no place in the SOC. Security operations demand accountability so that when an AI system flags an incident, prioritizes a threat, or recommends a course of action, teams need to understand why. What data informed the decision? What logic was applied? Can the outcome be verified?
In this environment, explainability is not a “nice to have” but a critical piece of the security puzzle. Even highly accurate systems will struggle to gain adoption if they can't show their reasoning because trust depends as much on transparency as it does on performance.
From tool to infrastructure
Another shift underway is how organizations think about AI operationally. Early experimentation often treated AI as a bolt-on capability, something to layer onto existing workflows, however that mindset is changing.
CISOs are increasingly recognizing that AI must be governed like any other piece of core infrastructure. That means gaining the trust and confidence in it to be able to deploy it enterprise-wide with a reasonable amount of risk. It also means preparing for a future where AI is subject to greater regulatory scrutiny, particularly around data usage and decision accountability. But this level of rigor takes time because meaningful adoption is rarely measured in weeks; it often unfolds over months of iteration and refinement. But that investment is what separates isolated wins from sustainable impact.
The workforce shift is real, but not what you think
Few topics generate more anxiety than the impact of AI on SOC teams. The narrative that automation will replace analysts is persistent, and largely misguided. What AI is actually doing is changing the nature of the work. Tier 1 and 2 tasks are being handled by AI, allowing analysts to focus on escalated investigations and incident response. In that sense, AI is raising the baseline for what it means to be both effective and efficient in the SOC.
This shift introduces challenges, particularly around developing entry-level talent. If foundational tasks are handled by machines, organizations need to rethink how new analysts gain experience. But that is a workforce development issue and not an argument against AI adoption. The reality is that human expertise is becoming more important, not less, but the difference now is that it is being applied at a higher level.
Pressure is mounting
While CISOs are taking a measured approach, they are not operating in a vacuum. Boards and executive teams are increasingly pushing for AI adoption, often driven by expectations of efficiency gains and cost reduction.
In many cases, those expectations outpace reality. AI in the SOC is not a plug-and-play solution, and it does not deliver immediate transformation without careful implementation. This creates a tension that CISOs must actively manage by balancing the need to move forward with the responsibility to do so safely. In this environment, CISOs are responsible for aligning ambition with execution, ensuring that AI initiatives are grounded in real outcomes rather than hype.
The inevitable shift
For all the valid caution, the direction is clear. The scale and speed of modern threats are forcing a fundamental rethink of how security operations are conducted. AI is not replacing the SOC, but it's most certainly redefining how it functions.
Over time, as systems become more reliable and trust is established, AI will recede into the background. It will no longer be a distinct capability, but an embedded one that is part of the fabric of everyday operations, much like earlier generations of machine learning. However, getting to that point requires concrete actions be taken now. CISOs who wait for absolute certainty will find themselves reacting rather than leading, while those who engage early — who test, validate, and refine AI within their own environments — will be the ones who shape how it is used.
In the end, trusting AI in the SOC is not a leap of faith, it’s a response to necessity. The sooner organizations begin building that trust on their own terms, the better prepared they’ll be for what comes next.
