No—not as a complete job replacement, based on the evidence currently available. AI can help with parts of cybersecurity analysis, including data analysis and network anomaly detection, but a cybersecurity analyst’s work also includes investigating incidents, assessing vulnerabilities, recommending controls, communicating findings, and planning for recovery. The current sources support task assistance, not a claim that AI can independently take over the full role.
What a cybersecurity analyst does
For U.S. occupational information, the closest Bureau of Labor Statistics (BLS) profile is “information security analysts.” BLS describes them as workers who plan and carry out measures to protect an organization’s computer networks and systems. The job is a bundle of responsibilities, not a single activity that can be automated in isolation.
- Monitor networks for security breaches and investigate incidents.
- Check for vulnerabilities and maintain protective software.
- Research security trends, prepare reports, and recommend improvements.
- Develop security standards, support users, and test disaster-recovery plans.
Because these duties vary by organization, the effect of AI will depend on which tasks a role includes and how the employer uses the technology.
Which cybersecurity tasks AI may assist with
NIST says AI may support cybersecurity work such as data analysis and network anomaly detection. Participants at NIST’s first Cyber AI Profile workshop also discussed defensive applications including anomaly detection and incident response. That supports a view of AI as a tool for analysis and response—not proof that any particular product can reliably perform an analyst’s entire job. NIST’s June 2025 workforce article describes AI as a potential way to support and improve cybersecurity work.
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In practice, an AI tool may help surface patterns or prioritize information for review. A responsible analyst or team still needs to determine whether the output is accurate, relevant to the organization, and sufficient to justify an action. The sources do not establish a universal level of autonomy or a standard threshold for when human review is required.
Why human judgment and oversight still matter
NIST workshop participants emphasized testing, transparency, accountability, training, and human-in-the-loop processes for AI used in cybersecurity. They raised questions about false positives and false negatives, the provenance of data, how model behavior and decisions can be understood, and how systems should be tested. These are stakeholder priorities reported in workshop reflections, not a universal standard or proof that every AI system has the same shortcomings. The first workshop reflection and the second workshop reflection describe these concerns.
For an organization deciding whether to use an AI tool, the relevant question is not simply whether it can produce an answer. It is whether people can evaluate the evidence behind that answer, understand the consequences of an error, and take responsibility for the resulting decision.
Questions to ask when evaluating an AI tool
- What task is it handling? Assess a specific use, such as alert triage, anomaly detection, or report drafting, rather than assuming it can replace an entire role.
- How was performance measured? Ask about false positives and false negatives and whether the evaluation reflects the organization’s environment.
- Can analysts inspect the evidence? Look for transparency about data provenance, model behavior, and how outputs are produced.
- What happens if it is wrong? Consider the consequences and define a human review and escalation path appropriate to the task.
- Who is accountable? Establish who governs the tool’s use and owns decisions made with its output.
NIST workshop discussions identify these as important considerations, but do not provide a universal scoring benchmark or controlled comparison of commercial products.
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AI can create cybersecurity risks as well as help defend against them
AI has a dual-use character. Participants in NIST’s first Cyber AI Profile workshop discussed ways AI could support defense and ways adversaries could use it to scale or automate attacks, including phishing, data poisoning, and model inversion. The workshop reflection reports these themes; it does not quantify how often such attacks occur. Organizations also need to consider how to secure AI systems themselves. NIST’s workforce discussion frames the changing skill set around three related areas: understanding AI’s strategic and organizational implications, securing AI against attacks and AI-enabled threats, and using AI to support cybersecurity work.
Will AI take cybersecurity analyst jobs?
There is no reliable figure in the available sources for the share of cybersecurity analyst roles AI will eliminate. BLS’s current U.S. projection is for 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, increases the need for enhanced security and contributes to projected growth because analysts will be needed to secure new technologies. These are projections for the occupation as a whole, not an estimate of jobs AI will create or eliminate. The BLS occupational profile provides the figures and its explanation.
For someone deciding whether to pursue the field, the projection is useful context, not a guarantee of a particular job outcome. The likely practical challenge is adapting as the work changes: analysts may need to assess AI-generated information and help protect AI-enabled systems alongside more familiar security responsibilities.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Education and skills for aspiring analysts
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 people enter with a high school diploma and relevant industry training and certifications, and that employers may prefer professional certification. These are typical pathways, not requirements that apply to every employer or candidate.
Best Value
BLS identifies analytical, communication, creative, detail-oriented, and problem-solving skills as important. NIST’s NICE Framework provides a way to think about how cybersecurity work and skills evolve; NIST has discussed incorporating AI-related tasks, knowledge, and skills into relevant existing or new roles. Neither a framework nor a particular course or credential guarantees employment.
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