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14/05/2026 2:34 pm
TL;DR: Shadow AI tools and swarms of AI agents are expanding the non-human workforce while creating visibility and control gaps across service accounts, agent access, and SOC workflows, according to SailPoint’s Gartner SRM 2026 session lineup. The governance problem is now operational, not hypothetical: identity context has to be built into controls before autonomous systems outpace review processes.
NHIMG editorial — here’s why we think this discussion matters
Questions worth separating out
Q: How should security teams govern shadow AI before it spreads across the enterprise?
A: Start by discovering every AI tool, agent, and workflow that can act with execution authority, then map the NHI credentials behind it.
Q: Why do AI agents create more risk than traditional automation jobs?
A: AI agents can change actions, tool use, and data access dynamically, which makes their privilege profile less predictable than a fixed script or batch job.
Q: What is the difference between standard IAM review and NHI governance for agents?
A: Standard IAM review usually checks whether a subject has access, while NHI governance also tracks lifecycle, purpose, credential exposure, and runtime behaviour.
Practitioner guidance
- Map AI tools to accountable owners Create an inventory of sanctioned and unsanctioned AI tools, then assign owners for the service accounts, API keys, and tokens they use.
- Add identity context to SOC triage Feed entitlement, ownership, and purpose data into detection workflows so analysts can see whether an agent or service account acted within scope.
- Apply time-bound access to autonomous systems Use just-in-time approval and short-lived credentials for high-risk agent actions instead of persistent tokens.
For a practical control lens, align the work with the NIST AI Risk Management Framework?
👉 Register for SailPoint's Gartner SRM 2026 sessions on shadow AI and identity →
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14/05/2026 2:36 pm
A few things worth adding from our research at NHI Mgmt Group.
Shadow AI is an NHI governance problem before it is an AI policy problem. Once an unsanctioned agent can call tools, read data, or trigger workflows, it behaves like a non-human identity with reach across systems. That means discovery, ownership, and privilege scoping must come first, or policy discussions remain theoretical. Practitioners should treat any unmanaged agent as a live identity risk.
A few things that frame the scale:
- 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: How can SOC teams use identity context to improve response to agent activity?
A: SOC teams should correlate alerts with the identity’s owner, intended purpose, and assigned privileges before escalating. That lets analysts distinguish expected automation from misuse, spot privilege abuse faster, and reduce alert fatigue caused by machine-driven activity that lacks context.
👉 Read our full editorial: Shadow AI and non-human workforce governance at Gartner SRM 2026
