Will AI Replace Cybersecurity Professionals? A Senior Security Expert Answers

Every few weeks, a new tool claims it can triage alerts, run a pentest, or write an audit report without a human. So I asked a colleague a direct question: will AI take over security operations, and will people in this field become rare? I put it to Taher Amine, a Senior Information Security professional. His answer was balanced, honest, and worth sharing. This post builds on his view with current data, role-by-role analysis, and a practical way to prepare.

Table of Contents

1. The question I asked
2. Offensive and defensive security: where AI already works
3. Why governance is a different game
4. Which roles face the most change
5. The real divide: adapt or fall behind
6. Practical example: give an AI agent a safe identity on Linux
7. Conclusion
8. FAQ

The question I asked

I work as a cybersecurity engineer, and I notice the same worry in almost every training session and community chat. People ask whether they are building a career that a software agent will do for free in five years.

I wanted a grounded answer, not marketing. So I asked Taher three things: will AI handle governance, offensive security, and defensive security, will human involvement become very rare, and will auditors, SOC analysts, and CISOs disappear? I also asked why he thinks so, because reasoning matters more than the verdict.

Taher’s short answer

Taher opened with a clear position. He expects AI to handle a very significant share of security operations, particularly on the offensive and defensive sides. He already sees the early signs in automated detection, investigation, vulnerability assessment, and incident response.

Then he drew a line around governance. In his words:

“Governance is not just about analyzing risks or recommending controls. It’s also about business decisions, authority, accountability, legal obligations, and accepting the consequences of those decisions.”

His closing thought was careful: he would not say “completely,” but he would not say “never” either.

Offensive and defensive security: where AI already works

Taher’s claim that AI is already useful in technical operations matches what the market shows. Two areas stand out: the security operations center (SOC) and automated offensive testing.

Defense: the SOC alert flood

A typical SOC drowns in alerts. One 2026 industry survey from a managed detection vendor reports that alert fatigue is the top challenge for 76% of organizations, and that 64% of SOCs still run detection and triage in a heavily manual way. Treat those numbers as vendor-reported, but the pattern matches what analysts describe everywhere.

AI fits this problem well. It pulls context, enriches indicators, correlates events across SIEM and EDR tools, and closes obvious false positives. Vendors claim manual triage often covers only a fraction of incoming alerts, while AI triage reviews all of them. Whatever the exact figure, the direction is clear: the first line of the SOC is the most automatable part of the job.

Offense: autonomous pentesting

Offensive security shows the same trend. In 2025, an autonomous tool called XBOW reached the top of HackerOne’s US leaderboard after it submitted roughly 1,060 vulnerabilities, including SQL injection, SSRF, XXE, and remote code execution.

Two details matter here. First, the XBOW security team reviewed each finding before submission, because HackerOne’s policy on automated tools requires it. Second, XBOW’s lead researcher warned against trusting a language model’s claim that a flaw exists. His team validates findings with deterministic checks instead, such as a working proof of concept.

That second point supports Taher’s view. AI scales the work, but humans still design the guardrails.

What machines still miss

AI performs best on tasks with clear patterns and fast feedback. It struggles when the situation depends on unwritten context. Three examples show the gap:

• Business impact. A tool can rank a vulnerability by technical severity. It cannot always tell that the “low” finding sits on the one server that runs payroll.
• Rules of engagement. A red team needs a human to agree on scope, timing, and what is off limits.
• Novel situations. Models learn from past data. A new attack chain or a strange internal process can confuse them.

Why governance is a different game

Taher’s strongest argument concerns governance. I think it is also the part most people overlook when they imagine a fully automated security team.

Authority and accountability

Governance means someone with authority decides how much risk the business accepts. That person signs the decision, answers to the board, and faces the consequences if it goes wrong.

AI can analyze risk and recommend controls, often well. But a model cannot stand in front of a regulator or a court. Accountability stays with a human or a legal entity. Taher put it simply: accountability does not transfer to software.

Legal obligations

Laws and contracts name responsible people and roles. Data protection rules, sector regulations, and customer agreements all expect an accountable owner. Auditing follows the same logic.

One 2026 analysis of AI audit agents makes the point clearly: when an AI performs audit procedures, the independence and professional judgment requirements still apply to that work, and a vendor contract cannot hand the responsibility to the vendor. Big audit firms say the same in their own reports, stressing that professional judgment and skepticism remain central as AI takes on more tasks.

Governing the AI itself

Taher added a point that I find the most forward-looking: the more autonomy we give AI, the more we must govern that autonomy.

The data backs him up. OWASP published its Top 10 for Agentic Applications in December 2025. It lists risks such as goal hijacking, tool misuse, and identity and privilege abuse. One industry survey cited by identity-security researchers found that 92% of respondents consider governing AI agents critical, yet only 44% have put policies in place. The same survey reports that 80% of organizations saw agents act outside their intended scope.

So AI does not remove governance work. It creates new governance work: who owns each agent, what it can touch, and who reviews its actions.

Which roles face the most change

Taher did not give a blanket yes or no. He separated roles by how much of the work repeats. I find that a useful lens, so I turned his comments into a table.

Role	Exposure to automation	What AI takes over	What stays human
Tier-1 SOC analyst	High	Alert review, enrichment, closing false positives	Supervising agents, hunting, tuning detections
Pentester (routine web tests)	Medium to high	Scanning, common exploit chains, reporting	Scoping, creative attack paths, business context
Auditor	Medium	Evidence collection, control testing, spotting inconsistencies	Independence, professional judgment, final opinion
CISO	Lower	Risk analysis, control recommendations	Leadership, priorities, trade-offs, accountability

The SOC analyst

Taher said that an analyst who spends most of the day reviewing alerts is far more exposed than someone who sets security strategy. I agree. Vendors describe the shift as moving the analyst from “queue processor” to “supervisor of agents.”

The job does not vanish overnight. It changes. Analysts who learn threat hunting, detection engineering, and incident leadership move up. Analysts who only close tickets risk being squeezed out.

The auditor

AI can collect evidence, test controls, and flag gaps. But Taher noted that an audit is not a checklist. The auditor brings independence, judgment, and responsibility for the conclusion.

I would add one practical consequence: auditors will also audit AI systems. Boards will ask who checks the agents that check everything else. That opens a new line of work for people with both audit and technical skills.

The CISO

Taher accepts that AI may soon analyze risk better than many professionals. Even so, he points out that a CISO leads people, negotiates priorities between departments, makes hard calls with incomplete information, and answers to the organization.

Those tasks depend on trust and authority, not on processing speed. I expect CISOs to use AI heavily, but I do not expect the role to disappear.

The real divide: adapt or fall behind

The line from Taher that stayed with me most is this one: the real change will not simply be AI replacing professionals. It will be professionals who know how to work with AI replacing those who do not adapt.

He also warned us not to underestimate how disruptive the transition could be.

What the workforce data says

The 2025 ISC2 Cybersecurity Workforce Study surveyed more than 16,000 professionals. Most did not see AI as a job killer. About 73% expect it to create more specialized skills, 72% expect it to demand a more strategic mindset, and 66% expect broader skill sets across teams. AI ranked as the most needed skill, cited by 41% of respondents.

Those numbers describe opinions, not guarantees. Still, they show that practitioners expect their jobs to change rather than end. The disruption Taher mentions will hit hardest where people stop learning.

Skills worth building now

If you work in security, these skills protect your career against automation:

• Detection engineering. Write and tune the rules that AI agents rely on.
• Scripting and automation. Python and Bash let you build, test, and limit agents.
• Identity and access control. Agents behave like non-human identities with privileges. Least privilege matters more than ever.
• Risk communication. Explain technical risk in business terms to executives.
• Audit and AI assurance. Learn how to test whether an AI system does what its owner claims.
• Critical review. Learn to challenge AI output and catch confident mistakes.

A simple 90-day plan

You do not need a big project. Try this:

1. Weeks 1 to 4: Pick one repetitive task in your job and automate part of it with a script or an AI assistant. Keep notes on what the AI gets wrong.
2. Weeks 5 to 8: Read the OWASP Top 10 for Agentic Applications and map each risk to a system in your environment.
3. Weeks 9 to 12: Write a one-page policy for AI tool use at your organization: approved tools, data limits, and who reviews outputs.

Practical example: give an AI agent a safe identity on Linux

Taher’s warning about governing autonomy sounds abstract, so here is a concrete case. Suppose you let an AI triage agent run read-only checks on a Linux server. Do not reuse your own account. Give the agent its own identity, a short list of allowed commands, and full logging.

Step 1: create a dedicated service account

bash

# System account with no login shell and no password
sudo useradd --system --shell /usr/sbin/nologin \
  --home-dir /var/lib/ai-triage --create-home ai-triage

Step 2: allow only specific read-only commands

Use exact command lines instead of wildcards. A wildcard can let the agent smuggle in extra options. Edit the file with visudo so a typo cannot lock you out.

bash

sudo visudo -f /etc/sudoers.d/ai-triage

# /etc/sudoers.d/ai-triage
Cmnd_Alias AI_READONLY = \
    /usr/bin/ss -tulpn, \
    /usr/bin/last -n 50, \
    /usr/bin/journalctl --no-pager -u sshd -n 200

ai-triage ALL=(root) NOPASSWD: AI_READONLY

# Keep a separate record of everything this account runs
Defaults:ai-triage logfile=/var/log/sudo-ai-triage.log
Defaults:ai-triage log_input, log_output

Check the syntax and review what the account can do:

bash

sudo visudo -cf /etc/sudoers.d/ai-triage
sudo -l -U ai-triage

Step 3: watch for permission changes

Add an audit rule so you know when anyone edits the agent’s permissions:

bash

echo '-w /etc/sudoers.d/ -p wa -k sudoers_change' | \
  sudo tee /etc/audit/rules.d/ai-agent.rules
sudo augenrules --load

# Later, review the events
sudo ausearch -k sudoers_change -i

This setup follows a simple principle that fits both people and machines: one identity per actor, the smallest possible access, and a clear trail. Real agent platforms need more controls, such as short-lived credentials and session isolation, but this small example shows the idea in practice.

Conclusion

AI will absorb a large share of technical security work. Alert triage, routine testing, evidence collection, and first-draft analysis already move toward automation, and that trend will continue. Taher Amine’s view, which I share, is that this does not mean humans vanish from the field.

Governance depends on authority, accountability, legal duty, and the willingness to accept consequences. Software cannot carry those, and the more autonomy we give agents, the more oversight they need.

Roles built on repetition will shrink. Roles built on judgment, leadership, and trust will grow. The safest career move is to learn how AI works, use it daily, and prepare to supervise it rather than compete with it.

FAQ

Will AI completely replace SOC analysts?

Not completely, but it will reduce the need for Tier-1 analysts who mainly review alerts. Teams will keep people to supervise agents, hunt threats, tune detections, and lead complex incidents.

Can AI perform penetration tests on its own?

For many web application tests, yes. Tools like XBOW already find and exploit common flaws at scale. Humans still define scope, review results, and handle creative or business-specific attack paths.

Will AI replace security auditors?

AI will take over evidence gathering and control testing. The auditor’s independence, professional judgment, and responsibility for the final opinion remain human, and professional standards expect that.

Could an AI act as a CISO?

AI can analyze risk and recommend controls well. A CISO also leads people, negotiates priorities, and answers to the board and regulators. Those duties need an accountable human.

Is cybersecurity still a good career choice?

Yes. Industry surveys such as the ISC2 workforce study show strong demand for skills, with AI itself near the top of the list. The field rewards people who keep learning.

Which skills should I learn to stay relevant?

Focus on detection engineering, scripting, identity and access management, risk communication, and AI assurance. Add the habit of reviewing AI output critically.

What is an AI agent’s “non-human identity”?

It is the account, key, or token that lets an agent act on systems. Treat it like any privileged account: give it an owner, minimal access, and full logging.

How should a small company start using AI in security?

Start with low-risk tasks like log summaries and alert enrichment. Use read-only access, keep a human reviewer, and write a short policy covering approved tools and data limits.