Security operations centers (SOCs) have spent years struggling under the weight of growing alert volumes, expanding attack surfaces, and chronic staffing shortages. Now artificial intelligence is adding a new complication: not just more information, but more machine-generated information that must itself be evaluated.
“There is an asymmetry here because you now have to parse through a lot of AI slop to get to, ‘Okay, is this real or not?’” Fernando Montenegro, vice president and practice lead at The Futurum Group, tells CSO.
His observation captures a growing concern among security leaders. AI is helping attackers and defenders move faster, but it is also creating new forms of cognitive burden for the humans tasked with separating signal from noise.
As AI accelerates vulnerability discovery and enables more automated reconnaissance and exploitation, defenders are increasingly responsible for overseeing systems whose outputs can be difficult to interpret or verify. The challenge is not simply more work. It is that the volume, speed, and complexity of that work are increasing simultaneously.
Yet experts who study and advise SOCs reject the idea that collapse is inevitable. Instead, they describe an industry entering a difficult transition that could reshape how security teams operate and how humans and machines share responsibility for defense.
The vulnerability surge is exposing years of security debt
One of the most immediate concerns is the possibility that AI dramatically increases the number of vulnerabilities organizations must identify and remediate.
Chris Crowley, a longtime cybersecurity instructor and SOC expert, argues that organizations are facing the consequences of years of accumulated technology debt.
“A lot of what we’re going to have to account for in the next couple of years is a technology debt of vulnerable software that has been deployed because it’s good enough to solve the problem, but then there are all these latent cyber issues, flaws, vulnerabilities that weren’t discovered prior to deployment,” he tells CSO.
AI-assisted vulnerability discovery has the potential to expose those weaknesses at a pace defenders have never experienced before.
“The compression of work that is being dropped on us is unprecedented,” Crowley says. “We’ve just been ignoring it for decades.”
He does not believe AI will necessarily create entirely new classes of vulnerabilities. Instead, he expects defenders to confront much larger volumes of familiar problems.
“We’re going to have 100 of these simultaneously,” he says, referring to the kinds of high-priority vulnerabilities security teams traditionally handle one at a time.
The AI challenge for many SOCs may be less a novelty problem than a volume problem. Security teams already know how to patch systems, prioritize remediation, and respond to critical exposures. What changes is the scale and speed at which those demands arrive.
Organizations with mature patching, prioritization, escalation, and response processes may struggle but adapt. Organizations that have treated security operations as a bare-minimum compliance function may find themselves overwhelmed.
For CISOs, Crowley says, that means treating “patch now” less as an occasional emergency state and more as a permanent operating posture. As AI accelerates vulnerability discovery, the distinction between routine maintenance and crisis response may continue to blur.
He compares the situation to disaster recovery planning. Organizations that wait until a crisis arrives to establish staffing plans, escalation paths, and remediation processes may discover there is not enough help available.
Cognitive overload may become the defining challenge
While vulnerability discovery receives much of the attention, Montenegro believes security leaders need a broader framework for understanding AI’s impact.
Organizations should think about AI through three lenses, he says: security for AI, AI for security, and security from AI. The first involves protecting AI systems. The second involves using AI to improve defensive operations. The third asks what happens when adversaries use AI against the organization.
For SOCs, all three categories are beginning to overlap.
As AI makes it easier to create reports, assessments, vulnerability submissions, and other operational artifacts, humans remain responsible for determining whether that information is accurate and useful.
“It becomes much easier to generate content,” Montenegro says, “but if you’re going to review that content as a human, the onus on you now is that much larger.”
The result is a new form of cognitive overload. Security professionals may spend increasing amounts of time evaluating machine-generated information instead of conducting higher-value security work.
Organizations can increasingly use AI to summarize reports, evaluate alerts, and assist with investigations, but humans remain responsible for validating the results.
“We’re not at the stage yet where people are comfortable” handing off critical decisions entirely to AI, he says.
That leaves defenders caught between two competing realities: AI is creating more information to process, but AI is also becoming one of the few viable tools for managing that growing workload.
For Montenegro, the principle should be to automate tasks, not roles. AI can absorb repetitive investigative steps, but organizations should be cautious about removing humans from the process entirely.
The risk, he says, is that if organizations hide too much complexity behind automated outputs, analysts may lose opportunities to develop the domain knowledge needed to advance.
“How is that professional who is reacting to those alerts growing as a professional?” he says.
The gap between mature and struggling SOCs may widen
Not every organization will experience the impact of AI in the same way.
John Hubbard, senior cybersecurity consultant and SANS instructor, believes the industry’s response will largely depend on how well organizations have prepared for operational stress before AI arrives at scale.
“I would roughly break security operations teams into two camps,” Hubbard tells CSO. “There are the ones that are definitely struggling, are already overwhelmed. And then some are doing really well.”
The struggling organizations tend to be understaffed, underfunded, undertrained, or dependent on ad hoc processes. Every incident feels different, forcing teams to improvise under pressure.
“Getting hit with something like this can certainly be an accelerant for burnout if they weren’t already experiencing it,” Hubbard says.
By contrast, mature security teams have already invested in processes, training, exercises, and automation. “The teams that are doing a really solid job now are probably not super overwhelmed because they’ve developed the processes and procedures to be ready for this kind of thing,” Hubbard says.
He compares successful SOCs to fire departments. Firefighters cannot predict exactly where the next emergency will occur, but they know how to respond because they have rehearsed those responses repeatedly.
“The teams that kind of can react like a fire department are the ones that are getting it right,” he says.
Those organizations conduct tabletop exercises, adversary emulation exercises, red-team assessments, and incident response drills. As a result, they can absorb additional workload without descending into panic.
Burnout remains the industry’s most difficult problem
Despite widespread concern about AI-enabled attacks, none of the experts view AI solely as a threat. Several argue that AI will become essential for helping defenders cope with the challenges it creates.
Jose-Marie Griffiths, president emerita and former CIO of Dakota State University, believes AI can help security teams sift through overwhelming volumes of information and identify the signals that matter most.
“People who work in SOCs are now seeing overwhelming volumes of data, and they’re getting fatigued,” Griffiths tells CSO.
AI can help automate portions of analysis, validate alerts, and improve visibility into complex environments. But Griffiths cautions that some AI-assisted vulnerability discovery tools are also producing large numbers of false positives.
That matters because false positives do not eliminate work. They create it. As organizations confront escalating volumes of findings, distinguishing genuine risk from erroneous results may become as important as discovering vulnerabilities in the first place.
The experts agree that technology alone will not determine outcomes. People will.
Crowley argues that cybersecurity professionals must recognize that uncertainty is intrinsic to the profession. “We are the group that deals with uncertainty,” he says. “That’s really and truly what cybersecurity is.”
That reality places responsibility on both individuals and organizations. Analysts need mechanisms for managing stress. Teams need to recognize when colleagues are approaching their limits. Managers need to establish healthy escalation practices and realistic expectations.
Hubbard rejects the notion that burnout is inevitable.
“It is not a foregone conclusion that security operations jobs have to be a painful grind that everyone hates,” he says.
He has seen organizations where employees remain engaged for years because leaders actively manage workload, create supportive cultures, and encourage open communication.
That includes making it safe for analysts to admit when they have reached their limits. “If people are unwilling to say, ‘I’m maxed out right now, and I’m going crazy,’ that’s going to be the thing that breaks a lot of teams,” Hubbard says.
Pay alone may not solve the problem. Crowley pointed to SANS/SOC survey findings showing that compensation ranked fourth among retention factors, behind meaningful work, training, and professional development.
The future SOC may look very different
Griffiths believes organizations will need to respond not only with better technology but with structural changes. Traditional tiered SOC models may need to evolve into more collaborative teams with diverse expertise working together in real-time.
“I think we’re going to have to eliminate the hierarchies a little bit and have teams of people with different expertise working together,” she says.
She also argues that organizations should invest in human expertise rather than simply increasing AI consumption. “Buy engineers, not tokens,” she says.
Professional networks and peer support will matter as much as any tool, Griffiths says, because defenders need trusted communities where they can compare notes, share practices, and avoid facing sustained pressure in isolation.
If there is a consensus emerging among experts, it is that AI is exposing weaknesses that already existed.
The staffing shortages, alert fatigue, burnout, and process failures affecting SOCs did not begin with generative AI. AI is simply amplifying them.
At the same time, AI is providing new tools that may help organizations manage those very challenges.
The future SOC may spend less time manually triaging alerts and more time validating automated findings, conducting threat hunting, and making strategic decisions. Human expertise may increasingly be paired with AI systems that act as operational partners.
The transition will not be painless. Some teams will struggle. Some practitioners may leave the field. Others will adapt and thrive.
“In a way,” Griffiths says, “we’re turning the whole SOC inside out.”
Montenegro sees the transition as a cybersecurity version of the Red Queen effect: defenders and attackers must keep running simply to stay in place.
Borrowing from science-fiction author William Gibson, Montenegro offered perhaps the simplest description of the industry’s current moment: “The future is already here. It’s just unevenly distributed.”
For security leaders, that future is arriving in the form of AI-generated vulnerabilities, AI-assisted investigations, and AI-enabled adversaries. The question is no longer whether security operations centers will change. It is whether organizations can adapt quickly enough to keep pace.