Topic starter
10/06/2026 12:13 am
TL;DR: As AI agents move from generating content to taking actions, traditional controls such as DLP, RBAC, and prompt filters lose visibility into runtime behavior and cumulative context, according to Lasso Security. Intent security shifts the decision point from what the model said to whether the action belongs in context, which is now essential for governing production agents.
Editorial analysis by NHI Mgmt Group, based on content published by Lasso Security: “Why Agentic AI Needs Intent Security”.
Key questions
Q: What breaks when security teams only filter prompts in agentic AI systems?
A: Prompt filtering only reduces conversational abuse.
Q: Why do autonomous agents change identity governance more than chatbots do?
A: Because the risk moves from generated content to real access.
Q: What are the signs that an agentic AI workflow is drifting out of scope?
A: Look for actions that remain internally consistent but no longer match the user goal, the application’s intended purpose, or the surrounding workflow.
Practitioner guidance
- Define runtime intent boundaries Specify which agent actions are acceptable for each workflow, and document the user goal, application purpose, and business context that must align before execution.
- Inspect cumulative context before high-risk actions Check conversation history, retrieved data, and prior tool outputs when the agent is about to approve refunds, update records, or trigger workflows.
- Separate content risk from behavior risk Treat suspicious text, suspicious intent, and unsafe runtime action as different signals, because a safe-looking prompt can still produce an unsafe result.
Bottom line: Agentic AI changes the core control problem from filtering content to governing runtime behavior across tools, workflows, and context.
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This topic was modified 5 hours ago by NHI Mgmt Group
11/06/2026 1:55 am
Intent security is now an identity governance problem, not just an AI safety problem. The article shows that the decisive risk is not what an agent says, but what it is authorised to do at runtime across enterprise systems. That moves the governance burden from content review into action control, with implications for IAM, PAM, and access decisioning. Practitioners should treat agent behaviour as an access problem first and a model problem second.
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.
- Only 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.
A question worth separating out:
Q: What should organisations measure to detect drift in agent behaviour?
A: Organisations should measure whether the agent’s actions still match the user’s goal, the intended workflow, and the normal pattern for that agent or role. Changes in action sequence, tool use, or side effects are stronger governance signals than prompt content alone. That is how behavioural drift becomes visible.
👉 Read our full editorial: Why agentic AI needs intent security for runtime behavior control
12/06/2026 3:30 am
Intent security is the first serious control model for agentic behaviour because content controls stop at the prompt. Once an agent can call tools, trigger workflows, and chain decisions across systems, the security question changes from whether the model produced risky text to whether the resulting action belongs in context. That is a governance shift, not a tuning exercise. The practical conclusion is that runtime behaviour must become a first-class control plane for agentic AI.
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.
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How do teams decide where to block agent activity?
A: Teams should block agent activity at the stage where the risk appears. Prompts need input filtering, tool calls need execution checks, and outputs need disclosure review. That staged approach prevents a single control from being asked to do three different jobs and missing all three.
👉 Read our full editorial: Why agentic AI needs intent security for runtime behavior control
This post was modified 5 hours ago by NHI Mgmt Group