Heimdal: AI Risk Management in 2026: Bridging the Enterprise Control Divide
Heimdal — 2026 — AI & Technology
The Heimdal "State of AI Risk Management in 2026" report, based on a survey of 1,000 IT professionals across the United Kingdom and the United States, reveals a stark confidence gap between executives and frontline practitioners, widespread generative AI adoption that outpaces security readiness, and severe operational overload among IT/security teams that impedes control implementation. The report cites real-world incidents involving third-party AI integrations and agentic tools to illustrate how visibility without enforcement fails to prevent data leakage and privilege escalation. It advises marketing and CX leaders to prioritize a comprehensive AI inventory and third-party audits, enforce integrated technical controls across access, execution, action chains, and privileges, and reduce practitioner workload through consolidation and automation to achieve measurable risk reduction.
Key Statistics
- 1,000 IT professionals across the United Kingdom and the United States were surveyed for the report.
- 29% of US executives report high confidence that AI risk is under control, compared with 7% of frontline IT practitioners.
- In the UK, 18% of executives and 11% of practitioners report high confidence that AI risk is under control.
- ChatGPT is present in 7 out of 10 IT estates and Microsoft Copilot is present in 6 to 7 out of 10 IT estates.
- Only approximately 4 out of 10 teams believe their existing security stack is adequately prepared to manage AI-driven risks.
Key Takeaways
- Conduct a comprehensive inventory of all sanctioned and unsanctioned AI tools, including direct employee use and third-party SaaS integrations, and audit OAuth/API grants on a regular (e.g., quarterly) cycle.
- Require routine, detailed risk briefings from IT and security practitioners so executive dashboards reflect operational realities and highlight unresolved vulnerabilities.
- Deploy an integrated control plane combining CASB/DNS, App Control, AppFencing, and PEDM to enforce policies, break unsafe agentic action chains, and prevent data leakage and privilege escalation.
- Prioritize data leakage prevention for AI endpoints by treating AI tools as distinct endpoints and implementing automated DLP mechanisms that block sensitive data transfers to unsanctioned services.
- Reduce practitioner overload through consolidation of overlapping security tools and automation of repetitive low-value tasks, freeing skilled staff to manage strategic AI risk controls.
Cite as: Heimdal. (2026). Heimdal: AI Risk Management in 2026: Bridging the Enterprise Control Divide. Retrieved from https://research.agilebrandguide.com/research/heimdal-ai-risk-management-in-2026-bridging-the-enterprise-control-divide