Agentic AI security controls: are your workflows actually governed?

TL;DR: Agentic AI security now has to cover prompt injection, tool misuse, context poisoning, and agent-to-agent exploitation because systems can browse, call APIs, and make sequential decisions, according to Lasso Security. Traditional appsec and data protection models are no longer enough once the system can act mid-task and compound risk across tool chains.

NHIMG editorial — based on content published by Lasso Security: AI Security Best Practices: How to Build Secure AI Workflows

Questions worth separating out

Q: How should security teams govern AI agents that can browse, call APIs, and act independently?

A: They should treat those systems as governed identities with runtime risk, not as passive applications.

Q: Why do agentic AI workflows break traditional least-privilege models?

A: Because least privilege is often defined at provisioning time, while agents make decisions at runtime across multiple steps.

Q: What breaks when prompt injection or context poisoning affects an AI agent?

A: The agent can act on malicious instructions that were never part of the original request.

Practitioner guidance

• Inventory every AI agent and shadow workflow Build a live inventory of agents, tools, integrations, and delegated sub-agents so security teams can see what is actually running.
• Enforce runtime least privilege on every tool call Restrict each agent to the minimum tool set required for the task and verify every write, export, or API invocation at runtime.
• Validate retrieved content and tool responses continuously Inspect documents, feed outputs, and API responses for hidden instructions before the agent can act on them.

What's in the full article

Lasso Security's full blog covers the operational detail this post intentionally leaves for the source:

• Step-by-step examples of agentic workflow hardening across retrieval, tool use, and delegated execution paths
• Detailed discussion of static, dynamic, and high-agency red teaming modes for AI systems
• Operational examples of runtime guardrails and intent-based controls in production workflows
• Expanded mapping of AI security practices to governance, compliance, and audit evidence

👉 Read Lasso Security's guide to secure AI workflows in the agentic era →

Agentic AI security controls: are your workflows actually governed?

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