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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsNot on the evidence available today. AI can help with cybersecurity analysis, including data review and network anomaly detection, but that is not the same as taking over the full analyst role. Analysts also investigate incidents, assess context and risk, maintain protective measures, communicate findings, and help organizations decide what to do next.
What does a cybersecurity analyst do?
For U.S. employment data, the closest Bureau of Labor Statistics (BLS) occupation is information security analyst. BLS describes these workers as planning and carrying out measures to protect an organization’s computer networks and systems. The work spans more than watching alerts: it includes investigating breaches, checking vulnerabilities, maintaining protective software, researching security trends, preparing reports, developing standards, recommending improvements, supporting users, and testing disaster-recovery plans.
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Because the job is a bundle of responsibilities, whether AI can replace an analyst depends partly on what “replace” means. Automating or assisting with one task does not establish that a system can take responsibility for the whole role.
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NIST describes AI as a potential aid for cybersecurity work, including data analysis and network anomaly detection. Participants in NIST’s Cyber AI Profile workshops also discussed defensive applications such as anomaly detection and incident response. These examples support a practical view of AI as a tool for processing information or helping teams respond—not proof that a particular AI product reliably performs an analyst’s entire job.
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- Data analysis: Help review large volumes of security-related information.
- Anomaly detection: Help identify activity on a network that merits investigation.
- Incident response: Support response work, with people assessing the evidence and deciding on appropriate actions.
AI’s role is not limited to defense. NIST workshop discussions also noted that AI can help adversaries scale or automate attacks, including phishing, data poisoning, and model inversion. Those workshop reflections report participant concerns; they do not quantify how often these attacks occur. Organizations also need to secure the AI systems they use.
Why does human judgment still matter?
An alert or recommendation is not the same as a reliable decision. NIST workshop participants emphasized measuring performance, including false positives and false negatives, and being transparent about data provenance, model behavior, and decisions. A false positive can consume time on activity that is not a threat; a false negative can leave a real threat unnoticed.
In its reflections on the first and second Cyber AI Profile workshops, NIST reported participant emphasis on human-in-the-loop processes and training, along with concerns about interpreting AI behavior, testing, accountability, and agentic AI. These are stakeholder priorities, not a universal review rule or proof that every AI tool has the same weaknesses. The sources do not establish a single threshold for when human review is required.
For an organization evaluating an AI tool, the relevant questions are specific to the task and its consequences:
- What work is the tool doing—alert triage, anomaly detection, reporting, or something else?
- How has its performance been measured, including false positives and false negatives?
- Can analysts inspect the evidence behind its output and understand how the system handles data?
- What happens if it is wrong, and who reviews or acts on the result?
- How are data provenance, governance, and accountability handled?
NIST’s workshop reflections raise these evaluation concerns, but do not provide a universal scoring benchmark or controlled comparison of commercial products.
Will AI take cybersecurity analyst jobs?
No reliable figure in the cited sources establishes what share of cybersecurity analyst roles AI will eliminate. The latest BLS profile cited here projects U.S. information security analyst employment to grow 21% from 2025 to 2035, with about 14,100 openings per year on average over that period. BLS says increased use of AI, along with e-commerce, contributes to demand for enhanced security and the need to secure new technologies.
Those are projections for the occupation as a whole, not a forecast of jobs AI will create or eliminate. They do not settle how AI will change particular tasks, employers, or roles.
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What skills and preparation are relevant?
BLS says information security analysts typically need a bachelor’s degree in a computer science field and related work experience. It also notes that some workers enter with a high school diploma plus relevant industry training and certifications, and that employers may prefer professional certification. Analytical, communication, creative, detail-oriented, and problem-solving skills are among the qualities BLS identifies as important.
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NIST’s NICE Framework is a workforce framework, not a prediction of which jobs AI will replace. In a June 2025 article, NIST described considering AI-related tasks, knowledge, and skills across relevant existing or new work roles. Its three useful lenses are the strategic and organizational implications of AI, securing AI against attacks and AI-enabled threats, and using AI in cybersecurity work. Training or certification can be part of preparation, but neither the framework nor BLS presents a specific credential as a guarantee of employment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the evidence does—and does not—show
NIST’s workforce article discusses framework development and workforce considerations; it is not an empirical study of job replacement. NIST’s Cyber AI Profile workshop reflections summarize participant viewpoints and priorities; they are not controlled tests of commercial AI systems. BLS figures cover the U.S. information security analyst occupation as a whole. Taken together, these sources support AI assistance in some cybersecurity tasks and continued occupational demand in the United States, but they do not quantify AI-driven displacement or rank specific tools.
Quick Recap
Sources
- NIST, “The Impact of Artificial Intelligence on the Cybersecurity Workforce,” June 12, 2025
- U.S. Bureau of Labor Statistics, Information Security Analysts
- NIST, NICE Framework
- NIST, first Cyber AI Profile workshop reflection, July 31, 2025
- NIST, second Cyber AI Profile workshop reflection
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