Topic starter
11/06/2026 11:23 pm
TL;DR: Agentic AI can set goals, plan multi-step actions, and execute with minimal human input, while generative AI remains reactive and prompt-bound, according to Lasso Security. That distinction matters because the security problem shifts from output quality to runtime authority, and access review processes assume access persists long enough to be reviewed.
Editorial analysis by NHI Mgmt Group, based on content published by Lasso Security: “Agentic AI vs Generative AI: Key Differences and Pros & Cons”.
Key questions
Q: What breaks when teams govern agentic AI the same way they govern generative AI?
A: They apply prompt and output controls to a system that can also choose actions.
Q: Why do AI prompts create identity and data-security risk?
A: AI prompts create risk because they can carry sensitive content outside the original system’s protection boundary.
Q: What are the signs that an AI-driven security workflow is too autonomous?
A: Warning signs include tool actions that are difficult to reconstruct, decisions made without clear ownership, escalating exceptions that nobody reviews, and agents that can move from analysis to containment without explicit policy boundaries.
Practitioner guidance
- Separate governance by actor type Treat prompt-bound generative systems and action-capable agentic systems as different control problems.
- Inventory autonomous tool paths Map every API, workflow, connector, and privileged action an agent can reach at runtime.
- Reassess static credentials for agent workflows Replace long-lived secrets where agentic systems rely on them, or bound their use so the credential cannot outlive the task.
Bottom line: Generative AI and agentic AI fail differently, so they need different governance models and different control points.
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This topic was modified 5 days ago 2 times by NHI Mgmt Group
This topic was modified 1 day ago by NHI Mgmt Group
12/06/2026 8:00 am
Agentic AI and generative AI are not variations of the same governance problem. Generative AI is a content system, while agentic AI is an action system. That split changes which control layer matters most: prompt safety and output validation for the former, runtime authority and tool governance for the latter. Practitioners should stop mapping both to the same guardrail pattern because the identity risk sits in different places.
A few things that frame the scale:
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
- 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: How should IAM teams govern AI-assisted identity workflows?
A: Treat AI-assisted identity workflows as governed control paths, not simple productivity tools. Define which tasks the assistant may recommend, draft, or execute, then keep approval rights and exception handling with named humans. The essential control is traceability, so every machine-generated action can be reviewed, challenged, and linked back to a responsible operator.
👉 Read our full editorial: Agentic AI vs generative AI: why governance must split
This post was modified 1 day ago by NHI Mgmt Group