Veeam Research: Who is Responsible for Rogue AI Behaviour?

2026-06-04 · Source: AI Magazine · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management · Depth: Fundamental Awareness, short

Summary

Research from Veeam's "Data and AI Trust Gap" report, based on a global survey of 600 senior executives, highlights a significant disparity between rapid AI adoption and organizational readiness. While 88% of organizations are already using or piloting AI agents, only 7% are considered truly AI-ready, meaning they incorporate ambition, visibility, and governance. A striking 95% of respondents report data-related challenges are slowing their AI progress. The study reveals a leadership misalignment, with 65% of CEOs believing they have a complete AI system inventory compared to 48% of technical leaders. Fragmented ownership, coupled with 95% reporting shadow AI usage, results in organizations with shared ownership being 47% less likely to detect "rogue AI behavior." Only 40% of leaders are confident in isolating and reversing AI-related failures, even as regulatory scrutiny, like the EU AI Act, intensifies.

Key takeaway

For AI/ML Directors or VPs of Engineering evaluating AI scaling strategies, recognize that rapid deployment without robust data governance and clear accountability introduces significant reputational and operational risks. You must prioritize building a "Data and AI Trust layer" to ensure data security, compliance, and precise recovery capabilities. This foundation accelerates safe AI at scale, mitigating "rogue AI behavior" and compliance concerns like the EU AI Act.

Key insights

Rapid AI adoption outpaces organizational readiness, creating a "trust problem" due to governance gaps and data challenges.

Principles

• AI readiness requires ambition, visibility, and governance.
• Fragmented ownership hinders AI risk detection.
• Trusted, compliant data is key to AI value and growth.

In practice

• Define clear ownership for agentic AI risks.
• Provide approved alternatives to shadow AI.
• Prioritize accurate, accessible, up-to-date data.

Topics

• AI Governance
• Data Trust
• AI Risk Management
• Shadow AI
• Regulatory Compliance
• Enterprise AI Adoption

Best for: CTO, Director of AI/ML, VP of Engineering/Data, Executive

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Magazine.