Topic starter
12/06/2026 9:50 pm
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.
Editorial analysis by NHI Mgmt Group, based on content published by Lasso Security: “AI Security Best Practices: How to Build Secure AI Workflows”.
Key questions
Q: What breaks when organisations rely on static access control for dynamic agentic workflows?
A: Static access control breaks down because agentic workflows change as business logic and execution paths change.
Q: Why do agentic workflows increase security risk even when each tool is approved?
A: Approved tools do not guarantee safe behaviour when the sequence of actions is influenced by poisoned context or delegated instructions.
Q: How do teams know whether AI runtime enforcement is actually working?
A: Look for low-latency enforcement, consistent decisions across languages and encodings, complete telemetry, and repeatable blocking of risky content under load.
Practitioner guidance
- Establish continuous AI asset discovery Inventory every AI agent, tool integration, model dependency, and delegated workflow path, including shadow AI and developer-deployed assistants that bypass normal review.
- Enforce runtime tool scope Apply inline authorization checks to every API call, data retrieval, file write, and delegated instruction so the agent cannot exceed its defined task boundary.
- Validate retrieved content and tool responses Treat documents, feed outputs, and agent-to-agent messages as untrusted inputs and inspect them before they can influence later steps in the workflow.
Bottom line: Agentic workflows change the security problem from protecting model outputs to governing action, privilege, and delegation during execution.
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This topic was modified 3 days ago by NHI Mgmt Group
25/06/2026 5:29 pm
Runtime control is the governing layer that agentic AI forces into view. Once an AI system can choose actions across tools and services during execution, the old assumption that authorisation is a provisioning problem no longer holds. Security decisions now have to happen inside the task, not just before it starts. Practitioners should treat workflow execution as a governed identity event, not just an application event.
A few things that frame the scale:
- Only 23% of IT leaders were very confident in their organisation's ability to manage security and governance for GenAI deployments, according to a 2025 Gartner survey of 360 IT leaders.
A question worth separating out:
Q: What should enterprises do when an AI agent attempts actions outside its intended task?
A: Block the action, log the intent mismatch, and review whether the agent’s policy boundary is too broad for the business process it supports. The goal is to contain scope drift before it becomes an unauthorized data export, system change, or external communication.
👉 Read our full editorial: AI security for agentic workflows now depends on runtime controls
This post was modified 3 days ago by NHI Mgmt Group