Topic starter
11/07/2026 8:30 pm
TL;DR: AI adoption is expanding across productivity, customer engagement, and security use cases, while the same research says 27% of executives are already investing in AI-enabled cybersecurity and 75% of companies use AI in network security, according to GlobalSign-cited survey findings. The governance problem is no longer whether AI matters, but whether identity, access, and monitoring controls can keep pace with how fast it is being embedded into operations.
Editorial analysis by NHI Mgmt Group, based on content published by GlobalSign: “15 principales estadísticas y tendencias de la IA en 2023”.
Key questions
Q: What breaks when AI adoption outpaces governance?
A: What breaks first is attribution.
Q: Why do access sprawl and AI workflows create more identity risk?
A: Because they multiply the number of places where credentials, approvals, and delegated actions can occur without clear ownership.
Q: What are the signs that shadow AI is becoming a governance problem rather than a productivity aid?
A: The clearest signs are widespread use outside IT visibility, repeated sharing of corporate data with GenAI tools, and no consistent approval path for new applications.
Practitioner guidance
- Define AI system ownership and approval paths Assign a business owner, technical owner and security reviewer to every AI system that touches internal data or operational workflows.
- Inventory AI tools as non-human actors Track chatbots, copilots, analytics tools and embedded AI features in the same register used for other non-human identities.
- Scope permissions for AI-assisted workflows Give AI-enabled workflows only the minimum data sources, APIs and downstream actions needed for the specific use case.
Bottom line: AI adoption is creating governance pressure faster than many organisations can assign ownership, access scope and auditability.
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11/07/2026 8:59 pm
AI adoption is becoming an identity governance problem before it becomes an automation problem. The article is strongest where it shows AI moving into productivity and security functions at the same time. That shift means organisations are granting machine-mediated access into business processes faster than they are defining ownership, authorisation and logging. Practitioners should treat AI rollout as a governance design exercise, not a tools-first deployment exercise.
A question worth separating out:
Q: Should AI governance sit with security, IAM or the business?
A: It should be shared, with security setting control requirements, IAM managing identities and permissions, and the business owning approved use cases and risk acceptance. AI governance fails when it is treated as a side project because the controls span multiple teams. Clear ownership, access review and change control are the minimum coordination points.
👉 Read our full editorial: AI adoption is outpacing security governance models