Transforming Entry-Level Jobs: AI’s Impact on the Talent Pipeline

Artificial intelligence (AI) is doing exactly what security teams hoped it would do: eliminate the repetitive, low-value work that has long burned out junior analysts. But in solving this problem, it may be creating another one that could have a long-lasting impact.

Log review. Alert triage. Drift detection. Basic investigation. These tasks were how generations of defenders traditionally learned the cybersecurity trade — how they built intuition, pattern recognition, and the “muscle memory” that senior leaders rely on during times of crisis. Now that AI is absorbing the grind, some say organizations risk accelerating efficiency at the cost of developing foundational expertise.

The result is an emerging paradox. AI is elevating today’s analysts, yet it may leave tomorrow’s leaders without the hands-on experience they need. As Visa CISO Subra Kumaraswamy notes, even with AI doing the repetitive work, teams still have to learn about “the art and science of defense.”

That raises the strategic question security leaders now face: If automation is taking over the grunt work, who trains the next generation of defenders?

The Apprenticeship Layer Is Eroding

The concern isn’t limited to security. In a recent 60 Minutes interview, Anthropic CEO Dario Amodei warned that AI could “wipe out” many entry-level white-collar jobs—the very roles that traditionally develop future experts.

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“We are already seeing the reduction in entry-level roles,” says Deidre Diamond, founder of recruiter CyberSN. “Where we used to see five hires, we see two, maybe three. It won’t be long before all of these roles are eliminated due to automation and/or AI.”

An ISC2 survey earlier this year found 52% of cybersecurity professionals believe AI will reduce the need for entry-level staff, but another 31% believe it will also create new types of entry- and junior-level roles to offset these reductions.

While efficiency gains are welcome, Diamond warns that the risk isn’t purely in foundational skills.

“The gaps I worry about are not just technical. They’re cultural and strategic,” she says.

Repetitive tasks like log review and alert triage are how analysts learn what normal and abnormal look like. Without that exposure, “future leaders may lack the intuitive sense of systems, data flows, and attacker behavior patterns that help senior leaders make quick, grounded decisions in crises,” Diamond says.

Organizations may also see a shrinking pool of “homegrown” talent, with fewer early-career analysts gaining the kind of pattern recognition that only repetition and exposure can teach.

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AI Removes the Noise, but Not the Need for Judgment

Gary Brickhouse, CISO of GuidePoint Security, sees the shift differently. In his view, AI is not removing essential learning — it’s accelerating it.

“AI clears the noise,” he says. “It elevates the talent that we have. It’s not replacing their talent.”

Instead of staring at a million logs and searching for a single anomaly, junior analysts can now see the outcomes of investigations sooner and focus on higher-value thinking early in their careers, Brickhouse adds.

“From an entry-level perspective, it makes their job easier in the context that it takes away the noise,” he says. “Now they can look at outcomes.” 

Brickhouse also sees AI becoming a teaching engine. 

“They can query AI and say, ‘Hey, you identified this thing. Why did you do that?'” he says. “I think it gets them up to speed faster.”

Still, he cautions that the entry-level path can’t simply be left to chance. 

“We just have to be mindful about, OK, well, what does the right career path coming in look like now?” he says.

Replacing Grunt Work With Deliberate Practice

Visa has already rearchitected how early-career analysts learn, Kumaraswamy says. Its model is built around three pillars: experience, exposure, and education, with curiosity as the core trait.

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“I believe ‘experience’ has the most impact on analyst growth,” he says. 

To create that experience, Visa gives analysts hands-on opportunities through hackathons, CISO challenges, and intentional rotations across prevention, detection, and response. The company also uses a “90/10 model,” where team members spend 10% to 20% of their time outside their home domain working with subject-matter experts. The goal is cross-pollination — a detection analyst gaining a prevention perspective or a responder seeing unfamiliar technologies firsthand.

Simulated cyber ranges and tabletop drills are equally important. These environments “can repeat alert triage, patching, log review, and incident response at scale,” helping junior staff build capability faster, Diamond says.

Kumaraswamy describes the same philosophy: mixing offense and defense so analysts see how attackers bypass controls and how defenders close gaps. After every drill or real incident, Visa’s teams analyze what was missed and why, turning each event into new playbooks and targeted exercises.

New Entry-Level Role: Fewer Seats, More Complexity

Diamond believes junior roles will transform. Instead of clearing noise for senior analysts, they’ll step into complexity sooner, working alongside automation from the start.

“‘Junior’ will still exist, although less of them,” she says.

Diamond sees three areas rising quickly:

Preparing future defenders also means starting earlier in the pipeline, Brickhouse adds, noting that his own son completed a four-year cybersecurity academy in high school, leaving him “two steps ahead” of other new college students.

AI isn’t hollowing out security careers, but it is hollowing out the work that used to train them. Organizations that embrace automation without redesigning how people learn risk developing future leaders with gaps in intuition and judgment.

Employers “must own efficiencies with this innovation, and they must deliberately create leadership development pathways, or they will not retain talent,” Diamond says.