Topic starter
12/06/2026 12:02 am
TL;DR: AI adoption reached 99.6% among surveyed companies, up from 86.8%, while 68% report using AI for helpdesk and chatbots and 67% for security threat detection, according to JumpCloud’s Q3 IT Trends 2025 report. The governance problem is no longer adoption itself, but how AI is embedded into identity, access, and operational controls without expanding risk.
Editorial analysis by NHI Mgmt Group, based on content published by JumpCloud: “AI is Here”.
By the numbers:
- AI adoption has surged to 99.6% among the companies JumpCloud surveyed, up from 86.8% in its previous findings.
Key questions
Q: How should security teams govern AI-enabled workflows that can act on their own?
A: Treat them as identity-governed execution paths, not just software features.
Q: Why does broad AI adoption create identity risk even when the tools are familiar?
A: Because the risk comes from how AI is wired into workflows, not from the novelty of the tool itself.
Q: What are the best practices for separating AI observation from AI action?
A: Use different controls for systems that observe data and systems that can change state.
Practitioner guidance
- Inventory AI-enabled workflows Catalog every workflow where AI can read, recommend, or act on identity, support, or security data so the privilege boundary is explicit.
- Separate read-only and action-capable use cases Distinguish AI that summarizes or classifies from AI that can trigger operational changes, and apply different approval and audit requirements to each.
- Tie AI access to lifecycle ownership Assign a business owner, technical owner, and review cadence for each AI-enabled workflow so offboarding and recertification are not ad hoc.
Bottom line: AI adoption is no longer the differentiator. Governance discipline is, because embedded AI changes the access and accountability model inside ordinary workflows.
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This topic was modified 4 days ago by NHI Mgmt Group
12/06/2026 9:01 am
AI adoption has crossed the threshold where governance lag becomes the primary risk. Once nearly every organisation is using AI in some form, the debate is no longer about experimentation. The issue is whether identity, access, and lifecycle controls were redesigned for AI-enabled operations or are still being stretched beyond their original scope. Practitioners should treat adoption saturation as a trigger for governance recalibration, not as proof of maturity.
A few things that frame the scale:
- Organisations that describe themselves as confident in their AI deployment actually experience a 72% security incident rate, compared to 33% for those who remain cautious, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How can IAM teams tell whether AI and NHI governance are actually unified?
A: Look for one policy model, one identity inventory, and one review process that covers people, workloads, and agent-linked access. If each actor type is handled in a separate tool or review cycle, the programme is still fragmented even if the platform claims a single control plane.
👉 Read our full editorial: AI adoption hits 99.6%, but identity governance is lagging
This post was modified 4 days ago by NHI Mgmt Group