Topic starter
06/06/2026 11:20 am
TL;DR: AI agents behave like users but decide their own next steps, which means static entitlements, standing credentials, and admin-time approvals leave gaps in control, according to Ping Identity. Runtime identity shifts governance to execution time, tying each agent action to a real delegate, context, and fine-grained rule.
Editorial analysis by NHI Mgmt Group, based on content published by Ping Identity: “Runtime Identity: Why AI Agents Need More than Credentials”.
Key questions
Q: How should security teams govern AI agents that browse and transact on behalf of users?
A: Security teams should govern AI agents as delegated actors with narrow, task-scoped permissions, not as enhanced browsers.
Q: Why do static entitlements create risk for AI agents?
A: Static entitlements create risk because they are granted before the agent decides how to complete the task.
Q: What breaks when an agent is treated like a normal service account?
A: What breaks is the assumption that the account only performs one bounded function.
Practitioner guidance
- Define agent identities as first-class records Register each AI agent with a unique identity, named purpose, and explicit delegate relationship so the control plane knows who it acts for and what it may do.
- Move authorization to the moment of action Evaluate each agent action against current context, delegate, and requested operation instead of relying only on standing entitlements issued at setup.
- Set human approval gates for high-risk actions Require human-in-the-loop approval where the agent is about to perform irreversible or sensitive operations that exceed routine delegated scope.
Bottom line: AI agents are governed differently from static workloads because they can make independent runtime decisions that outgrow the original access grant.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
06/06/2026 12:04 pm
Static entitlement models collapse when the actor can decide its own next step. The article describes non-deterministic AI agents that can alter their approach mid-task, which means access that looked acceptable at setup can become unsafe during execution. The broken premise is that least privilege can be fixed at provisioning time for a task whose path is not fixed. Practitioners should treat runtime decision authority as the governance boundary, not the original grant.
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: Should organisations use the same access model for customer bots and employee copilots?
A: No. They can share an identity-aware architecture, but the access rules should reflect who they serve, what data they may reach, and which actions need escalation. Customer bots, employee copilots, and personal agents represent different risk profiles even when they use the same systems.
👉 Read our full editorial: Runtime identity for AI agents: why static entitlements fall short