Topic starter
06/06/2026 2:02 am
TL;DR: AI agents move beyond RPA by making context-aware decisions, using multiple tools, and operating with broader system access, which raises the risk of excessive permissions and unintended actions, according to Oasis Security. The governance problem is no longer task automation but runtime identity control across sensitive systems and data.
Editorial analysis by NHI Mgmt Group, based on content published by Oasis Security: “Beyond RPA: Implementing Secure AI Agent Access”.
Key questions
Q: What breaks when AI agents are given access that was designed for RPA workflows?
A: RPA-style access breaks because fixed workflows assume predictable steps, while AI agents can change tool use and action order at runtime.
Q: Why do AI agents create more risk than traditional automation?
A: AI agents create more risk because they can interpret context, choose actions, and invoke tools autonomously.
Q: How can teams tell whether AI access is actually under control?
A: Look for evidence that access is limited by purpose, not just by account.
Practitioner guidance
- Define agent-specific access boundaries Document exactly which systems, data sets, and tools each AI agent may use, then remove any entitlement that is not required for the agent’s current runtime purpose.
- Enforce policy at execution time Apply control checks when the agent requests a tool call, credential, or permission change, not only when the identity is created or reviewed.
- Inventory AI agents and their credentials Build a complete inventory of agent identities, tokens, API keys, certificates, and connected services so hidden access paths do not accumulate outside governance.
Bottom line: AI agents change the access problem because they can reason, choose tools, and act outside the fixed path that RPA controls were built for.
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This topic was modified 20 hours ago by NHI Mgmt Group
06/06/2026 3:29 am
AI agent access is not an RPA subcase. RPA governance assumes fixed task flow, while AI agents can choose actions and tools at runtime. That means the old control model is structurally underfit because privilege is being consumed by a decision-making identity, not a script. The practitioner conclusion is that access architecture must be based on runtime behaviour, not process diagrams.
A few things that frame the scale:
- Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, and that 15% of day-to-day work decisions will be made autonomously.
A question worth separating out:
Q: What should teams check before putting an AI agent into production?
A: Teams should verify three things before production: the agent has a unique identity, its permissions are minimal and explicitly approved, and its actions are fully auditable. They should also confirm that any privacy control used for analytics is layered on top of, not instead of, the access model.
👉 Read our full editorial: Secure AI agent access beyond RPA and the new identity risk
This post was modified 20 hours ago by NHI Mgmt Group