Artificial Intelligence is the current darling of the tech world. From autonomous vehicles & fraud detection, to creating action figure images for your LinkedIn, its influence appears to extend to every corner of the digital landscape. Cybersecurity is no exception.
AI is increasingly being integrated into tools that focus on both defence and offence, such as automated malware analysis and predictive threat detection. Even in penetration testing, a domain traditionally dominated by human expertise, AI is beginning to play a significant role.
But is AI changing the landscape for penetration testing, or is it merely the latest development in a long journey of technological advancements? Let’s explore how AI is making an impact, its limitations, and what the future might hold.
A Familiar Path
If you have spent time in offensive security, you know that automation isn’t new. Tools like vulnerability scanners, automated exploit frameworks, and reconnaissance scripts have been around for some time and are staples in a penetration tester’s toolkit. While AI may seem like a game changer, it is more of an evolution of these existing tools, enhanced by machine learning and data analysis.
Rather than a complete revolution, it represents a natural progression. However, the pace of development is rapid, and the impact is substantial.
Where AI Excels: Automating the Repetitive
AI is making notable advancements in specific areas of penetration testing. Tasks that are repetitive and structured, such as scanning networks for vulnerabilities or conducting routine reconnaissance, can now be performed faster and with greater accuracy. With AI, it is possible to map attack surfaces, prioritise threats based on context, and adapt to changes in infrastructure in real time. Additionally, AI simplifies the rollout of simulations; helping create and run realistic phishing campaigns or endpoint attacks that mimic genuine threats.
One of AI’s significant advantages lies in compliance requirements. For organisations that need to demonstrate regular vulnerability assessments or maintain specific security controls, AI-driven tools can generate comprehensive, auditable reports with minimal effort. This automation not only enhances consistency and speed but also facilitates compliance with industry standards and regulations such as ISO 27001, PCI DSS, and SOC 2. Consequently, resulting in reduced overhead, improved visibility, and allowing human testers to concentrate on the more strategic and creative aspects of their work.
Where AI Falls Short: The Human Factor
Despite its strengths, AI has clear limitations that may never be fully resolved. At the core of effective penetration testing is human creativity. The ability to think laterally, connect unexpected attack vectors, or exploit misconfigurations that don’t follow a standard template remains a uniquely human skill. Even the most advanced AI systems require careful prompting and supervision from experienced professionals. On their own, these tools often struggle to understand nuances or adapt to unpredictable environments.
One critical issue is AI’s tendency to “hallucinate,” producing outputs that are confident but inaccurate or misleading. In the context of penetration testing, this can result in misidentifying vulnerabilities, incorrectly assessing risks, or fabricating technical details. These hallucinations can pose a real danger when reports are relied upon for remediation or compliance, potentially leading teams to address non-issues while overlooking actual risks. Without expert human validation, such errors can create a false sense of security.
Hallucinations can be particularly problematic during asset discovery when AI tools are used to map external infrastructure and identify relevant systems. In some cases, AI may mistakenly associate unrelated or third party-owned assets with the target organisation. This not only skews testing results but also introduces legal and ethical risks, such as inadvertently scanning or attempting to exploit systems that the organisation does not own or control. Without human oversight to verify these assets, AI-driven tools may operate on faulty assumptions, which can escalate risk rather than reduce it.
Additionally, AI lacks awareness of organisational context. It doesn’t comprehend business priorities, legacy decisions, or cultural nuances that influence risk. A vulnerability that appears critical in one environment may be irrelevant in another. Understanding this requires experience and human insight.
While AI can help meet compliance requirements by generating audit-friendly reports, this alone is insufficient for ensuring security. Compliance may demonstrate that certain checks were performed, but it does not guarantee that an organisation is protected from real-world threats. Attackers do not care about checklists; they exploit gaps, grey areas, and human weaknesses. This is where creative, contextually aware human-led testing makes a significant difference.
AI also cannot navigate ethical and legal considerations. Determining the scope of a test, managing permissions, and ensuring operational safety all require sound judgment and accountability, qualities that AI lacks.
Moreover, while AI can assist with exploitation, it cannot independently conduct sophisticated red team operations. Tasks such as simulating insider threats, bypassing physical controls, or customising attack sequences in real-time require strategic planning, improvisation, and nuanced human interaction. Additionally, when it comes to identifying and exploiting zero-day vulnerabilities, those not yet known to the vendor or public, AI lacks the foresight and creativity necessary to discover them without human guidance. High-impact, novel attack vectors often arise from unconventional thinking, deep experience, and targeted exploration, areas where human testers still excel.
The Hybrid Future: AI + HI
Rather than viewing AI as a replacement for human testers, it should be seen as an enabler.
The most effective approach is a hybrid one: artificial intelligence (AI) working in tandem with human intelligence (HI). AI can handle scale, automation, and repetitive tasks with unmatched speed, while humans provide insight, adaptability, and strategic direction.
Think of it more as a collaborative effort, where tools support rather than replace expert judgment.
Conclusion: Beyond Tools, Toward Partnership
AI isn’t the end of penetration testing; it’s a powerful new phase in its evolution.
When used wisely, AI enhances the capabilities of security professionals, helping them test deeper, move faster, and uncover more subtle flaws. But the human element remains irreplaceable. It’s not just about testing systems, it’s about understanding people, processes, and risk in context.
In the end, the best security outcomes won’t come from choosing between AI or humans. They’ll come from working together.
