Will AI Take Over Cybersecurity?

Short answer: No — AI won’t replace cybersecurity professionals or run security programs on autopilot. But it will reshape how we defend, shifting routine tasks to machines while elevating the human role in judgment, governance, and strategic decisions. The future of cybersecurity lies in AI-augmented operations, not AI-dominated ones.

Summary: AI is transforming cybersecurity — not replacing it. Here’s how human-AI collaboration will redefine security operations, threat detection, and governance across industries like biotech and healthtech.

What “take over” really means (and why it’s misleading)

When people ask, “Will AI take over cybersecurity?”, they typically mean one of three scenarios:

1. AI as an Assistant. This is where we are now. AI systems help triage alerts, correlate logs, enrich investigations, draft rules, and perform heavy lifting — freeing analysts to focus on harder problems. According to recent industry analysis, “more than two-thirds agreed AI needs substantial human input to be highly successful.” (TechRadar)

2. AI as Agent. The next step: AI systems that take limited, pre-approved actions (for example, automatically closing low-risk alerts or isolating compromised assets), with human oversight. Some firms are piloting this today. (Security Boulevard)

3. AI as Owner. Fully autonomous security programs where humans only oversee or audit. This is not credible today, and very likely unacceptable to boards and regulators for years.

When people imagine AI “taking over,” they’re usually picturing option 3. But the reality is closer to options 1 and 2 — with humans still firmly in the loop.

The attack-defense battleground: AI on both sides

Attackers leveraging AI

• Generative AI is now used for more convincing phishing, voice/video deepfakes, and automated reconnaissance. A recent McKinsey review warns that as organizations adopt AI, they “risk inadvertently introducing new threats” — and attackers are racing ahead. (McKinsey & Company)

• External reports show state actors and criminal groups increasingly using AI to scale deception and intrusion. (AP News)

Defenders empowering AI

• AI helps sift through massive alert volumes, perform pattern recognition, reduce analyst fatigue, and accelerate triage. TechRadar notes that organizations “cut through the noise, reduce alert fatigue, and accelerate investigations” by combining human + AI. (TechRadar)

• Research also shows systemic value: AI-enabled programs that go beyond automation deliver better outcomes — but only when built on a foundation of visibility, inventory, and process. (McKinsey & Company)

But there’s a twist

AI isn’t just a tool — it’s an attack surface. Studies show how large language models and agentic systems can be hijacked or exploited (e.g., prompt injection, RAG backdoors). (arXiv)

Without strong governance, AI can amplify risk rather than reduce it. McKinsey highlights this “foundation problem”: without asset inventories and control frameworks, AI simply automates chaos.

For a hands-on look at how these attacks actually work (and how to defend against them ), see Hacking AI: Real-World Threats and Defenses with the OWASP AI Testing Guide.

Why cybersecurity still needs humans

• Context & judgment. AI can spot anomalies; humans decide material risk, regulatory impact, and ethical trade-offs.

• Governance & risk ownership. Frameworks like NIST’s AI Risk Management Framework and the EU AI Act embed oversight, audit trails, and accountability. AI alone can’t satisfy those. (Forbes)

• Complexity & change. Regulatory environments, third-party risk, and mission-critical systems evolve unpredictably — requiring strategic human adaptation.

• Ethics & bias. AI models inherit bias and lack transparency; humans remain essential for responsible deployment.

How this plays out in specialised sectors like biotech & healthtech

At Sekurno, we see two parallel truths in high-risk regulated industries:

• External attack surfaces remain weak. In our recent external, non-intrusive review of 50 biotech companies — spanning genomics, diagnostics, longevity, and precision health — we found insecure APIs, exposed staging environments, hard-coded secrets, and outdated software that could expose sensitive patient or genomic data.

• AI tools can help defenders move faster. Properly implemented, AI can map these surfaces, write detections, triage API abuse, and streamline compliance documentation — all critical when defending regulated data.

But this requires discipline: treat AI as infrastructure, apply hybrid human-agent models, monitor AI supply chains, and build governance frameworks from the start.

So, will AI “take over” Cybersecurity? No, but it will transform it

So, will AI take over cybersecurity in the next decade? Unlikely. But it will fundamentally transform how we operate. Here’s what to expect:

• Roles will change. Routine tasks — rule writing, alert triage, enrichment — will shift to machines. Humans will focus on architecture, oversight, and governance.

• Programs will evolve. From reactive “alert → investigation” loops to proactive “inventory → AI triage → human judgment → feedback.”

• Attack surfaces will expand. AI introduces new vulnerabilities (model abuse, agent hijacking, synthetic identity attacks) and amplifies existing ones (exposed APIs, misconfigured cloud).

• Governance becomes non-optional. AI-driven programs need risk registers, vendor controls, human-in-the-loop oversight, audit logs, and regulatory alignment. Many aren’t there yet, so AI can’t simply replace them.

If you want to see how security teams are already using generative AI — from red teaming to detection automation — check out How Generative AI Can Be Used in Cybersecurity.

AI in Cybersecurity: What to Adopt, Pilot, or Avoid

So how can security leaders decide where to safely integrate AI today? This matrix provides a practical roadmap. Use it as a guide for bringing AI into your security program — showing what’s ready to adopt now, what’s worth piloting in a controlled way, and what’s best to avoid for now. Each recommendation includes a quick rationale so you can act with confidence and stay focused on what truly delivers value.

Domain	Adopt (Now)	Pilot (Scoped)	Avoid (For Now)
Security Operations (SOC / Blue Team)	AI-assisted alert triage and case summaries — group related alerts, generate notes, and enrich IOCs. Cuts noise without changing workflows.	Integrate AI into SOAR playbooks — let LLMs handle context gathering but keep human approval before containment.	Autonomous SOC tools acting without human review — often inaccurate and risky.
Threat Intelligence	AI-powered summarization and entity extraction — digest threat feeds and highlight key actors and TTPs.	AI-generated threat briefs or detection mappings — promising for exec reports, but validate links and sources.	Fully AI-attributed campaigns or unsourced intel — unreliable and misleading.
Incident Response & Forensics	AI-based timeline and artifact summarization — accelerates report writing and evidence tagging.	AI-assisted triage of malware or memory dumps — good for scoping, needs expert review.	Automated containment or deletion by AI — may destroy evidence.
Offensive Security (Red Team / Pentesting)	AI for recon and report drafting — automate repetitive recon and documentation.	AI-guided exploit research — useful for brainstorming but must be verified manually.	“Autonomous pentesting” or exploit bots — mostly hype and ethically risky.
Application Security (AppSec)	AI-driven code review comments and vulnerability explanations — accelerate learning and triage.	AI-assisted threat modeling and fuzzing — can surface edge cases; validate all findings.	Replacing SAST/DAST with LLMs or auto-merging AI fixes — high false-positive risk.
Governance, Risk & Compliance (GRC)	AI-drafted policies, audit summaries, and control mappings — reduce manual workload.	AI-generated risk narratives — fine if reviewed by control owners.	Allowing AI to close or approve risk items — unsafe accountability gap.

Guardrails to Keep It Safe

• Human in the loop: AI drafts; analysts decide.

• Data hygiene: Redact secrets, personal data, and client identifiers before sending to any external model.

• Auditability: Log prompts, model versions, and decisions.

• Measure impact: Track precision/recall, MTTR, false positives, and report turnaround time.

• Change control: Treat AI tools like code — test before production.

  
  

Summary

In short: AI will change how cybersecurity works — not who drives it.The organizations that win will treat AI as an amplifier of human expertise, not a replacement for it.

Final thought

AI won’t do your security for you — but it will give your security team superpowers if you build the right foundations. The leaders of tomorrow will run AI-augmented security programs, embed human oversight as a core control, and treat AI not as a replacement, but as a force multiplier.