Topic starter
01/08/2026 9:00 pm
TL;DR: Detection-only runtime protection leaves prompts, tool calls and API actions to be reviewed after execution, according to Impart. Inline policy decisions are what keep AI-native requests from slipping through before anyone can intervene.
Editorial analysis by NHI Mgmt Group, based on content published by Impart: “What Is Complete Runtime Protection?”.
Key questions
Q: Why do AI agents connected through MCP create more compliance risk than ordinary APIs?
A: AI agents create compliance risk because their actions are dynamic, context-driven, and harder to attribute than conventional API traffic.
Q: What are the signs that runtime AI controls are not enough?
A: Common warning signs include unexplained tool usage, policy drift across sessions, repeated dependency on broad inherited access, and incidents that can only be explained after the fact.
Q: What breaks when runtime protection is detection-only?
A: Detection-only controls leave the final decision to humans after the request has already been processed.
Practitioner guidance
- Define inline enforcement points for each reachable surface Map where prompts, API calls, MCP tool requests and agent actions are actually accepted, then place blocking logic at those exact points rather than only in downstream logs.
- Scope AI agent actions to explicit allowed behaviours Limit what an agent can execute, what tools it can call and what data it can surface so runtime policy constrains action before execution reaches production systems.
- Inspect MCP traffic as a governed request path Treat tool calls, returned context and server responses as security-relevant events, because misuse can occur through either the request or the data the server returns.
Bottom line: Modern applications now expose several security-relevant surfaces at once, so the control problem is broader than the web tier WAFs were built for.
Glossary terms: Runtime Protection, Runtime Security, Model Context Protocol, Policy as Code
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This topic was modified 4 hours ago by NHI Mgmt Group
01/08/2026 9:07 pm
Inline enforcement is now the deciding control boundary for AI-native applications. Detection after execution is useful for investigation, but it does not stop prompt injection, agent misuse or MCP abuse once the request has already been accepted. In practice, this shifts the security question from whether an event was logged to whether the system could have been blocked at the point of action. Practitioners should therefore design controls around decision time, not alert time.
A few things that frame the scale:
- Half of companies using generative AI will deploy agentic AI by 2027, according to Deloitte's 2025 Technology Predictions.
A question worth separating out:
Q: How should teams govern inline protection across LLMs, agents and MCP servers?
A: Teams should assign ownership for each surface, define what may be blocked in production and test whether policy decisions are enforced before execution. The governance model should treat prompts, tool calls and agent actions as distinct control points, with the same accountability standard applied across each layer of the stack.
👉 Read our full editorial: Complete runtime protection for LLMs, MCP, APIs and agents