Topic starter
12/06/2026 9:58 pm
TL;DR: Enterprises are moving past prompt filtering toward runtime governance because agents reason, decide, and act inside connected business systems, not just at the input layer, according to Zenity. That shift matters because current security programmes were built for static controls, while autonomous behaviour changes what can be observed, certified, and contained.
Editorial analysis by NHI Mgmt Group, based on content published by Zenity: “The Vendor to Beat, Built Before the Category Had a Name”.
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 AI agents need separate runtime authentication and authorisation controls?
A: Because agent governance answers who approved the agent and what it should do, while runtime controls decide whether the agent can actually act in a specific system.
Q: What are the signs that AI governance is failing in the enterprise?
A: Common warning signs include rapid growth in AI use without matching policy coverage, sensitive files being copied into personal accounts, and a large share of AI apps carrying high or critical risk.
Practitioner guidance
- Define runtime authorization boundaries Specify which actions an agent may take after a prompt is accepted, including record changes, message sends, approvals, and transaction initiation.
- Inventory every connected system Map the CRM, email, finance, and workflow platforms each agent can reach, then rank them by business impact if the agent acts incorrectly.
- Separate prompt safety from execution safety Keep prompt filtering and model safety as input controls, but add independent controls for action approval, scoped tool use, and session-level monitoring.
Bottom line: AI agent governance fails when teams stop at prompt controls and never govern the actions an agent can take inside enterprise systems.
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13/06/2026 12:26 am
Prompt filtering is not a governance model for autonomous behaviour: It was designed for controlling inputs, not for constraining actions after an agent has context and tool access. That assumption fails when the actor can decide and execute at runtime, because the dangerous decision is often made after the prompt has been accepted. The implication is that identity governance has to move from content screening to execution control.
A few things that frame the scale:
- 88% of organisations have embedded AI agents in their workflows, according to KPMG's 2026 report.
A question worth separating out:
Q: Should organisations govern AI agents differently from chatbots?
A: Yes. Chatbots mainly expose content risk, while AI agents create action risk because they can decide, select tools, and execute in live systems. Governance has to focus on delegated authority, business-process containment, and runtime boundaries rather than conversation safety alone.
👉 Read our full editorial: AI agent governance is shifting from prompts to runtime control