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24/06/2026 10:58 pm
TL;DR: AI agents are increasingly being treated as identities with access to production systems, but many enterprises still manage them like anonymous service accounts, according to SafePaaS. That mismatch turns auditability, ownership, and lifecycle control into the real governance problem, not just access volume.
NHIMG editorial — based on content published by SafePaaS: access governance for AI agents
By the numbers:
- Between early 2024 and mid-2025, the number of non-human identities in the average enterprise grew sharply, often outnumbering human identities by more than 100 to 1.
Questions worth separating out
Q: How should security teams govern AI agent access in enterprise environments?
A: Treat AI agents as sponsored identities with owners, approved purposes, and reviewable entitlements.
Q: Why do AI agents create governance problems for traditional IGA programmes?
A: IGA was built around stable human roles, predictable review cycles, and clear organisational ownership.
Q: What breaks when AI agents are managed like generic service accounts?
A: Ownership, approval trails, and lifecycle visibility break first.
Practitioner guidance
- Create a sponsored identity model for every AI agent Assign each agent a named owner, business purpose, and risk rating so it cannot exist as an anonymous shared account in inventories or spreadsheets.
- Replace ticket-based provisioning with policy-led approvals Define access rules by system sensitivity, intended function, and approval authority, then automate entitlement decisions for repeatable AI use cases.
- Build a complete inventory of AI agents and related secrets Map which agents can reach production changes, customer records, and intellectual property, then connect that inventory to secret rotation and revocation paths.
What's in the full article
SafePaaS's full article covers the operational detail this post intentionally leaves for the source:
- A more explicit walkthrough of the AI agent access model and how sponsor ownership is documented in practice.
- The readiness checklist in full, including the questions teams can use to test their current IGA and IAM stack.
- The mini case example showing how centralized governance reduced high-risk AI accounts and audit findings.
- The by-the-numbers table with the article's own framing of growth, audit difficulty, and breach cost.
👉 Read SafePaaS's analysis of access governance for AI agents →
AI agent access governance: what IAM teams need to change?
Explore further
25/06/2026 7:48 am
AI agent governance is an identity problem, not a chatbot problem. The article is right to frame these systems as identities that need owners, approvals, and lifecycle control. The deeper issue is that most enterprises still inherit controls built for humans, then stretch them across agents that can touch production systems at machine speed. Practitioners should stop treating agent access as a niche automation issue and govern it as part of the core identity estate.
A few things that frame the scale:
- 1 in 4 organisations are already investing in dedicated NHI security capabilities, with an additional 60% planning to do so within the next twelve months, according to The State of Non-Human Identity Security.
- 85% of organisations lack full visibility into third-party vendors connected via OAuth apps, which shows how quickly delegated access can outgrow manual governance.
A question worth separating out:
Q: Who should be accountable for AI agent access reviews and offboarding?
A: Accountability should sit with the business sponsor, supported by IAM or IGA teams and enforced through privileged access controls where needed. The sponsor owns the use case, security owns the control design, and operations handle revocation when the agent is retired or repurposed. Without that split, no one can prove control.
👉 Read our full editorial: Access governance for AI agents is now an IGA priority
