Prowler’s 2026 Report Finds a Gap Between Cloud Security Teams’ AI Ambitions and Adoption

Summary

A Prowler and Kickstand survey of 633 cybersecurity professionals found that cloud security teams face heavy workloads and limited trust in autonomous AI, while prioritizing practical help with detection, triage, and compliance.

Key points

The survey, conducted in December 2025 and early 2026, included 633 cybersecurity professionals across nine countries; Prowler reports a 95% confidence level and a ±4% margin of error.
Respondents reported handling an average of 71 security incidents per team each week, while 42% cited skills shortages as a top operational challenge.
Only 18% said they had achieved autonomous AI at scale; 46% expressed doubts about autonomous AI systems, and 53% cited cost or complexity as a barrier.
Teams most often prioritized AI for threat detection and prioritization (57%), incident triage and response (45%), and compliance reporting and audits (44%).
The report identifies fragmented security data and a lack of context as operational problems, including difficulty connecting misconfigurations, permissions, network exposure, and vulnerabilities into attack paths.
Among organizations that had not adopted agentic AI, 67% said evidence of cost savings would influence adoption, while 47% wanted proven faster remediation.

Article Details

Publisher: Prowler, in partnership with Kickstand
Report Period: December 2025 and early 2026
Scope: Survey on cloud security operations, AI adoption, visibility, and priorities across nine countries.
Sample Size: 633 cybersecurity professionals; 95% confidence level with a ±4% margin of error.
Key Statistics: Respondents reported an average of 71 security incidents per team per week, or more than 3,600 annually.
18% reported having achieved autonomous AI at scale.
46% said they lacked trust in autonomous AI systems.
42% cited skills shortages as a top operational challenge.
53% identified AI cost and complexity as barriers to adoption.
Recommendations: Automate targeted workflows such as triage, context gathering, and compliance documentation to reduce analyst toil.
Invest in real-time visibility that provides contextual interpretation across cloud domains.
Preserve institutional knowledge in security tools so investigation history and remediation practices remain available.
Choose solutions that integrate with existing ecosystems rather than requiring wholesale platform migration.
Prioritize transparency and auditability so teams can verify AI-driven checks and recommendations.