Job Data Shows AI Skills Are Becoming Part of Everyday Security Work
Gilles Castro
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October 28, 2026
(Image: Shutterstock)
The cybersecurity skills conversation has long focused on one question: How do we bring more people into the profession?See Also: 79% of SOCs Use AI. Only 36% Have It in Their Workflows.
That question still matters. But artificial intelligence is forcing employers, educators and security professionals to confront another more troubling one: Are we training people for the work they will actually be doing?
The answer is changing quickly.
CyberSN’s October report on U.S. cybersecurity job postings found that 14.6% of postings from January through September 2026 mentioned AI or machine learning and is rising every quarter.
The shift is even clearer in some specialties. Nearly one-third of “research” job postings mentioned AI or machine learning, while 23.3% of “offense” postings did. “Cyber data scientist” postings grew more than 200% from the same period in 2025, although they started from a small base.
As ISMG’s Shaun Waterman pointed out last week, a new SANS Institute report found that more cybersecurity jobs now require AI expertise. Among the new job roles are AI/ML Security Engineer, AI Red Team Engineer, AI Governance and Security Risk Lead and AI Security Architect. ISC2 also launched a Cyber AI Workforce initiative focused on the intersection of cybersecurity and AI.
Training Has to Match the Work
The labor market is sending a clear message. AI knowledge is moving from a specialist skill toward a requirement woven through cybersecurity roles. The biggest problem is the speed of that transition.
We need to align training with the new realities of the workplace, and it needs to be at an exponential speed, which we are nowhere near.
Traditional cybersecurity education often teaches the technical mechanics of an attack or defense. Students learn how an attacker gets into a system, how defenders detect activity and how analysts investigate an incident. Those foundations still matter. But education also needs to address where AI and automation fit inside those workflows.
Security analysts may use AI to triage alerts, summarize threat intelligence or accelerate investigations. Penetration testers may use an AI assistant during research. Security engineers may build automated workflows around detection and response.
Training can’t treat those activities as an optional module added after the core curriculum. Students need to learn how AI changes the work itself. That means teaching professionals how to use AI within a cybersecurity role, how to evaluate its output and when human judgment needs to override automation.
Automation Changes Entry-Level Work
All of this matters most for people entering the field of cybersecurity. Many traditional entry-level tasks are well suited to automation. Alert enrichment, log summarization, basic analysis and repetitive investigative steps can increasingly be handled or accelerated by AI-enabled tools.
But that doesn’t eliminate the need for entry-level talent. It changes what employers will expect from them.
Rob T. Lee, chief AI officer and chief of research at the Sans Institute, recently compared it to helping swimmers swim faster: “If you put flippers on, you’re going to be able to swim it faster,” Lee said. “That’s what AI is going to enable you to do, but you’re still hiring a swimmer.”
CyberSN counted 235,786 U.S. cybersecurity job postings from January through September 2026, 26.4% fewer than during the same period in 2025. The report cautions against treating job postings as a census of employment and says 2025 was the strongest year in its four-year dataset.
Still, the combination of lower posting volume and growing AI requirements raises the stakes for people entering the field. Knowing security concepts alone may no longer be enough. Candidates increasingly need to show they can apply those concepts in environments shaped by AI and automation.
That should change how educators design labs and exercises. Students need practical exposure to AI-assisted security workflows, not just lectures about AI.
Human Skills Become More Important
The growth of automation also changes the value of human expertise. Security providers and internal teams can’t compete by offering only tasks that software can automate. They need people who can interpret context, communicate risk, make decisions and deal with situations that don’t fit neatly into an automated workflow.
Security professionals will still need technical depth. But employers will place more value on judgment, problem-solving, communication and the ability to understand why an automated system reached a conclusion.
That’s especially important for MSSPs and other IT service providers. If an offering consists mainly of work AI can perform, providers need to pair automation with services that require human interaction and problem-solving skills.
Cyber Education Can’t Stand Still
The new training model needs to change at every career stage. Entry-level professionals need hands-on technical practice that reflects AI-enabled environments. Experienced practitioners need opportunities to learn new workflows as tools change. Security leaders need exercises that help them understand how AI affects operations, workforce planning and risk.
We’ll still need technical labs, tabletop exercises and scenario-based learning built around the way organizations operate today.
The goal isn’t to turn every cybersecurity professional into an AI engineer. The goal is to make sure they understand how AI changes their role.
Cybersecurity education has always had to keep pace with technology, but AI makes the gap between learning and work too hard to ignore. Professionals who thrive in this environment won’t be the ones who simply know how to use an AI tool. They will understand where automation helps, where it fails and where human expertise still matters.
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