Barriers in AI implementation for cybersecurity FIGURE 13 What implementation hurdles does your organization face in embracing AI for cybersecurity? (select all that apply) Insufﬁcient knowledge and/or skills 20% 40%AI-generated security responses must be validated by a human before implementing Insufﬁcient funds Unclear business caseUncertainty about risk 60% Responses (%)0%54% 41% 39% 36% 33% As organizations navigate the integration of AI into their security operations, the balance between automation and human judgement becomes increasingly critical. While AI excels at automating repetitive, high-volume tasks, its current limitations in contextual judgement and strategic decision- making remain clear. Over-reliance on ungoverned automation risks creating blind spots that adversaries may exploit. This evolving dynamic is reshaping the role of cybersecurity professionals, highlighting the importance of adapting skill sets to meet new demands. According to the World Economic Forum’s The Future of Jobs Report 2025, “networks and cybersecurity” are among the top three fastest-growing skills projected for 2030 – alongside AI and big data and technological literacy – reinforcing the urgency for targeted upskilling and continuous learning.6 Rather than replacing human expertise, AI is enabling specialists to shift their focus towards strategic oversight, governance and policy while delegating routine operational tasks to automation. This transition demands new skill sets, blending technical proficiency with strategic and ethical considerations, and underscores the growing importance of AI literacy across security teams. The priorities for organizations are clear: invest in AI literacy and secure-use skills, and embed governance and validation, without creating new single points of failure. A collaborative model, anchored in security-by-design principles, emerges as the recommended path forward – enabling organizations to harness AI’s advantages while mitigating vulnerabilities and ensuring innovation strengthens, rather than compromises, cybersecurity. How industries are adopting AI for cybersecurity The adoption of AI tools to augment cybersecurity capabilities varies across industries, reflecting sector- specific risk profiles and operational needs. The energy sector emphasizes intrusion and anomaly detection (according to 69% of respondents who have implemented AI for cybersecurity); the materials and infrastructure sector prioritizes phishing protection (80%); and the manufacturing, supply chain and transportation sector reports greater use of automated security operations (59%). These differences not only reflect sectoral risk profiles and operating constraints but also collectively point to a maturing portfolio of AI-enabled cyber defence capabilities that spans detection, intelligence, analytics and orchestrated cyber defence. The differences in AI adoption for cybersecurity will be analysed in Section 3.6. Global Cybersecurity Outlook 2026 23