Topic starter
24/10/2026 8:50 pm
TL;DR: Agentic AI turns valid credentials into an attack path because agents can plan, use tools, and act without step-by-step prompting, explains Anjuna. Runtime governance, short-lived access, and full session traceability become central once software can make decisions and execute actions at runtime.
Editorial analysis by NHI Mgmt Group, based on content published by Anjuna: “Agentic AI vs Generative AI: The Critical Security Guide”.
Key questions
A: Autonomous agents create more risk because they can turn a single bad prompt into a chain of actions across systems.
Q: What are the signs that AI governance is failing in the enterprise?
A: Common warning signs include rapid growth in AI use without matching policy coverage, sensitive files being copied into personal accounts, and a large share of AI apps carrying high or critical risk.
Q: What breaks when an AI agent can act without step-by-step prompting?
A: Traditional IAM breaks because it assumes access is tied to a request a human can approve or review in time.
Practitioner guidance
- Map every autonomous agent to a governed identity Inventory each agent, the tools it can call, the data it can reach, and the credentials it can present.
- Issue short-lived credentials for task-bounded access Replace standing tokens with short-lived credentials that expire after the task completes.
- Log the full agent action chain Capture the plan, tool choice, intermediate decisions, observed results, and final action for every session.
Bottom line: Agentic AI changes the control problem because software can plan, use tools, and act without step-by-step human prompting.
Glossary terms: Agentic AI, Tool Drift, Trusted Execution Environment, Prompt Injection (Agentic)
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This topic was modified 7 days ago by NHI Mgmt Group
25/10/2026 11:12 am
Agentic AI creates an assumption collapse, not just a larger attack surface: access review processes were designed for actors whose privileges persist long enough to be reviewed. That assumption fails when an autonomous agent acquires and uses access inside a task boundary, then changes state or scope before a reviewer ever sees the session. The implication is that governance has to move from periodic certification toward runtime authorisation and constrained execution.
A few things that frame the scale:
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: Should organisations use confidential computing or runtime controls for agent security?
A: They should use them when the agent handles sensitive data or can perform irreversible actions, because software-only controls leave the execution environment too exposed. Runtime isolation is most useful when the control problem includes credential use, tool execution, and a need to protect the agent’s decision context from infrastructure operators.
👉 Read our full editorial: Agentic AI needs runtime guardrails, not gen AI assumptions
This post was modified 7 days ago by NHI Mgmt Group