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08/06/2026 4:42 pm
TL;DR: AWS AgentCore adds session isolation, identity controls, secure token vaults, VPC-only networking, and Zero Trust verification for AI agents, but the article argues that runtime governance still breaks once developers start building and scaling agentic systems, according to Clutch Security. The unresolved problem is not platform hardening, but the assumption that credentials and privilege remain reviewable and predictable after independent agent decisions begin.
NHIMG editorial — based on content published by Clutch Security: The Agentic AI Security Paradox, AWS AgentCore Gets It Right but It's Not Enough
Questions worth separating out
Q: How should security teams govern AI agents that can create and reuse credentials?
A: Treat each agent as a non-human identity with a lifecycle, not just a workload.
Q: Why do AI agents complicate traditional IAM and PAM controls?
A: Traditional IAM and PAM assume that privilege is stable enough to provision, review, and retire on a human schedule.
Q: What breaks when agent behaviour is monitored only at the platform layer?
A: What breaks is visibility into how legitimate access is used after it is granted.
Practitioner guidance
- Inventory every agent-created identity Track credentials, tokens, and service accounts that AI agents create or consume across development, staging, and production.
- Bind each agent session to a revocation point Define where a session begins and where it must end for every agent workflow.
- Monitor for behaviour that exceeds intended scope Create detection logic for tool chaining, unexpected write actions, secrets propagation, and access to systems outside the original task description.
What's in the full article
Clutch Security's full blog post covers the operational detail this post intentionally leaves for the source:
- The article’s platform-by-platform breakdown of AgentCore security features and how they map to enterprise deployment decisions.
- The discussion of developer implementation risks, including how placeholder secrets and copy-paste patterns become production exposure.
- The three-layer shared responsibility model with more detail on implementation security and runtime governance responsibilities.
- The operational framing for continuous NHI discovery and behavioural analysis across agent workloads.
👉 Read Clutch Security's analysis of AWS AgentCore and agentic AI identity risk →
AgentCore and AI agent governance: is your identity model ready?
Explore further
08/06/2026 6:28 pm
Agentic AI creates an identity governance gap, not just a security gap. The article is right to separate platform hardening from runtime governance because the underlying problem is that agents behave as credential consumers across many systems, not as static workloads. When access can be created, reused, and recombined in the flow of execution, traditional review cadences lose much of their explanatory power. Practitioners should treat this as a governance design problem, not a point product problem.
A few things that frame the scale:
- 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so, according to AI Agents: The New Attack Surface report.
- Only 33% of organisations report their AI agents have accessed inappropriate or sensitive data beyond their intended scope, which shows how quickly runtime behaviour can outpace governance.
A question worth separating out:
Q: How do IAM teams know whether agentic AI is actually under control?
A: Look for evidence that every agent identity is discoverable, every session has a clear end point, and every high-risk action is observable in context. If the team cannot trace credentials from issuance to retirement, or cannot explain unusual tool use, the programme is not yet governing agentic identity.
👉 Read our full editorial: AWS AgentCore exposes the governance gap in agentic AI identity
