Survey Finds AI Adoption Outpacing Enterprise Security Visibility

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Summary

Wiz’s survey of 100 senior IT and security decision-makers found widespread AI deployment but limited inventory and governance: 55% use agentic systems, while only 17% have a comprehensive AI asset inventory. The report recommends stronger visibility and controls.

Key points

  • The survey covered 100 senior IT and security decision-makers; 55% said their organizations deploy autonomous agentic systems, and 31% run more than 25 AI systems in production.
  • Only 17% reported having a comprehensive, up-to-date AI asset inventory; 52% discovered unauthorized AI applications in the past year.
  • Respondents cited unauthorized access to sensitive data (70%), unmonitored autonomous decisions (54%), and overprivileged identities (53%) as leading agent-security concerns.
  • Governance and cross-functional ownership were the top reported AI-security bottleneck (26%); 43% extend legacy cloud tools to AI, while 37% rely on homegrown scripts.
  • The report recommends treating AI systems as production infrastructure, mapping assets and exposure paths, limiting agent credentials, and requiring human review for high-impact actions.
  • Wiz also promotes its own AI security products and services in recommendations for testing, code analysis, remediation, and runtime detection.

Article Details

Publisher
Wiz IO
Report Period
2026; survey timeframe not specified
Scope
AI adoption, security readiness, and operational concerns among technical organizations
Sample Size
100 senior IT and security decision-makers, including CxOs, VPs, and directors
Key Statistics
  • 55% of surveyed organizations reported actively deploying autonomous agentic systems.
  • 17% reported having a comprehensive, up-to-date AI asset inventory.
  • 52% reported discovering unauthorized AI applications within their perimeters over the past year.
  • 70% identified unauthorized access to sensitive data as an agentic security concern.
  • 26% cited governance and cross-functional ownership complexity as their primary AI security bottleneck.
Recommendations
  • Apply enterprise configuration, patching, logging, and admission controls to AI model endpoints, registries, and pipelines.
  • Continuously map AI assets to owners and prioritize exposed attack paths.
  • Restrict agent credential scopes and require human verification for high-impact production actions.
  • Use external AI penetration testing to assess live perimeters and validate reachable attack paths.
  • Analyze AI code before production, accelerate verified remediation, and use runtime detection and automated triage to contain compromised workloads.

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