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AI Can Speed Up Cybersecurity. It Can't Replace Technical Judgment.
As security teams adopt AI tools, organizations need people who understand the systems behind the alerts, the risks behind the recommendations and the decisions that still require human judgment.
ITSEC Asia
AI can summarize a security alert in seconds, help analysts investigate suspicious activity and reduce time spent on repetitive tasks. That is useful, especially for teams dealing with more alerts than they have hours in the day. But faster analysis is only valuable if someone can recognize when the result is wrong.
A recent UK government study offers a reason to look carefully at that balance. Published on 29 September 2026, Cyber Security Skills in the UK Labour Market 2026 found that 57% of businesses had a basic technical cybersecurity skills gap, up from 49% in the previous study. The report also found that 47% of people responsible for cybersecurity in businesses and charities lacked confidence in dealing with cyberattacks and had not outsourced the function. Meanwhile, 70% of cybersecurity businesses said staff were using AI in their daily work, compared with 53% in the previous study. GOV.UK
The findings describe the UK market, but they raise a question relevant to any organization adopting AI: are teams building the expertise needed to use these tools well?
The fundamentals behind the automation
AI-assisted security tools can help with log analysis, alert prioritization, vulnerability assessment and threat investigation. Yet each task depends on underlying knowledge. An analyst needs to understand what normal network activity looks like before identifying an anomaly. A vulnerability tester must understand the system being assessed before deciding whether a finding presents a genuine risk.
Without that foundation, automation can create a new problem: people may become faster at processing recommendations without becoming better at judging them.
Security teams should therefore keep several capabilities central to their development plans:
- Networking and system fundamentals: Understand traffic flows, operating systems, identity controls and common attack paths.
- Analytical reasoning: Check evidence, challenge assumptions and distinguish a meaningful signal from a false positive.
- AI-assisted investigation: Learn how to validate AI-generated findings, recognize uncertainty and document decisions.
- Risk communication: Explain technical findings in terms of operational impact, business exposure and appropriate next steps.
These capabilities work together. An AI-generated explanation may sound convincing, but confidence in the wording is not evidence that the conclusion is correct.
Measure capability, not tool familiarity
The same distinction should influence cybersecurity training. Completing an AI security course or learning a particular interface can be useful, but neither automatically demonstrates readiness for operational work.
Training exercises should test whether learners can investigate an alert, correlate evidence from different sources, identify what remains unknown and decide when to escalate. When AI tools are involved, assessments should also examine whether learners can verify outputs and explain why they accepted or rejected a recommendation.
Organizations can start by identifying recurring tasks in their security operations, mapping the technical knowledge each task requires and testing those skills through realistic scenarios. This makes training more relevant to the work people will actually perform.
The approach also helps managers identify where AI can assist and where additional supervision remains necessary. Automation should change how a task is performed, not remove accountability for the outcome.
Building the next generation of security professionals
For Indonesia's cybersecurity workforce, the opportunity is to combine stronger technical foundations with practical exposure to AI-enabled security work. Learners need room to experiment, make mistakes safely and explain their decisions. Employers need a clearer picture of what their teams can do beyond the tools they have learned to operate.
ITSEC Cyber & AI Academy can support this development through structured learning in cybersecurity and AI, helping learners build knowledge relevant to professional practice. The objective is to develop people who can use modern tools while retaining the technical judgment needed to question their results.
Ready to develop practical cybersecurity and AI skills? Explore learning opportunities at ITSEC Cyber & AI Academy.
References
- UK Department for Science, Innovation and Technology. Cyber security skills in the UK labour market 2026. Published 29 September 2026. Read the official report
