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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.
NHIMG editorial — based on content published by Lasso Security: Agentic AI vs Generative AI: Key Differences and Pros & Cons
By the numbers:
- When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes.
- 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate.
Questions worth separating out
Q: How should security teams govern AI systems that can take actions, not just generate content?
A: Security teams should classify AI systems by runtime authority, not by whether they use an LLM.
Q: Why do agentic AI systems create more risk than generative AI for IAM teams?
A: Agentic AI creates more risk because it can act independently inside a workflow, which moves the problem from output review to action governance.
Q: What do security teams get wrong about AI agent guardrails?
A: Teams often focus on prompt filtering and content moderation while ignoring the downstream tools and accounts the agent can use.
Practitioner guidance
- Separate reactive AI from agentic AI in your inventory Catalogue every AI use case by whether it only returns outputs or can initiate actions, call tools, or change state.
- Map runtime permissions to specific agent tasks List each API, data source, plugin, and workflow an agent can touch, then remove anything that is not required for a named business function.
- Require approval gates for high-impact actions Place human review in front of revoking credentials, isolating systems, changing policy, or modifying access paths.
What's in the full article
Lasso Security's full article covers the operational detail this post intentionally leaves for the source:
- A side-by-side breakdown of where generative AI remains reactive and where agentic AI becomes autonomous in enterprise workflows
- Concrete cybersecurity examples of autonomous containment, compliance enforcement, and remediation patterns
- A risk table showing what can go wrong when agentic systems are allowed to act without sufficiently narrow decision boundaries
- The vendor's implementation framing for combined guardrails across prompt isolation, output validation, and context-based access control
👉 Read Lasso Security's analysis of agentic AI vs generative AI in cybersecurity →
Agentic AI vs generative AI: what IAM teams need to know?
Explore further
12/06/2026 8:00 am
Autonomy changes the identity problem from prompt control to action control. Generative AI can usually be governed as an output system, but agentic AI requires governance over decisions, tools, and execution timing. That means the core identity question is no longer only what the model says, but what it is allowed to do in the enterprise. Practitioners should treat runtime authority as the primary control surface.
A few things that frame the scale:
- 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface report.
- 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation, according to SailPoint research.
A question worth separating out:
Q: How can organisations tell whether an AI agent policy is actually working?
A: Look for evidence that the policy blocks or narrows real runtime actions, not just that it is documented. A working policy reduces over-permissioned access, prevents unauthorised tool use, and leaves audit trails that show why an action was allowed or denied. If the agent can still move beyond scope, the policy is not effective.
👉 Read our full editorial: Agentic AI vs generative AI: why governance must split
