Topic starter
14/05/2026 6:58 pm
TL;DR: AI agents are increasingly logging into systems, accessing sensitive data, and acting on behalf of users, with SailPoint citing research that 80% of organisations have already seen agents act beyond intended scope and 96% of technology professionals view them as a growing security threat. Governance now has to track ownership, permissions, and purpose, not just accounts and entitlements.
Editorial analysis by NHI Mgmt Group, based on content published by SailPoint: “Expanding AI agent governance: Introducing the Databricks connector to the SailPoint ecosystem”.
By the numbers:
- 80% of organisations have already seen their AI agents perform unintended actions, including inappropriate data sharing and unauthorized system access.
- 96% of technology professionals identify AI agents as a growing security threat.
Key questions
Q: What breaks when agentic AI is governed like a normal application account?
A: Security controls break down because agentic systems do not behave like fixed-function applications.
Q: Why do AI agents increase identity risk even when the login succeeds?
A: A successful login only proves that the agent reached the system.
Q: How do teams know whether AI governance is actually working?
A: Look for evidence that every AI interaction can be traced end to end, from identity and intent to output and enforcement.
Practitioner guidance
- Define human ownership for every AI agent Require each agent to have an accountable business and technical owner before it can be connected to production data platforms or certified in governance workflows.
- Scope agent permissions to explicit purpose Review whether each agent's access matches its current task, data sensitivity, and approved workflow rather than the broad role it inherited at deployment.
- Inventory agents inside data platforms Use connector-fed discovery to maintain a current inventory of AI agents, the systems they touch, and the permissions they hold across environments.
Bottom line: AI agents are creating governance blind spots because traditional IAM visibility is not enough to explain their runtime behaviour or purpose.
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14/05/2026 7:01 pm
Account visibility is no longer a sufficient governance model for AI agents. Traditional IAM can tell you whether an entity has credentials, but it cannot by itself explain whether an AI agent is acting within its assigned purpose. That gap matters because agent behaviour can remain technically authorised while still being operationally unsafe. Practitioners should treat agent identity as a governed runtime subject, not a static account record.
A few things that frame the scale:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should organisations govern AI agents alongside human identity and device access?
A: Organisations should treat AI agents as a separate identity class with their own entitlement boundaries, logging expectations, and approval model. Human IAM controls often assume interactive sign-in and review cycles, which do not fit autonomous or programmatic access. The safer approach is to define actor-specific policy and verify which access paths can be delegated without expanding trust unnecessarily.
👉 Read our full editorial: Databricks agent governance exposes the limits of IAM visibility