Execution Controls

Execution controls are mechanisms that govern when, where, and how AI systems can operate within an organization's infrastructure. They include approval workflows, deployment gates, resource limits, and runtime monitoring to ensure AI models run only under authorized conditions and within defined parameters. For enterprise AI governance, execution controls prevent unauthorized model deployment, reduce risk of uncontrolled AI behavior, and provide audit trails that demonstrate compliance with internal policies and regulatory requirements.

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ResearchGlobal2026-09-10

MCP Prompt Injection Is an Execution-Control Problem, Not a Content-Safety One

NHI Governance published an analysis reframing prompt injection in the Model Context Protocol as an execution-control failure rather than a content-safety failure. The analysis argues that malicious instructions injected into an agent's context can directly trigger real-world side effects if tool permissions and pre-execution authorization controls are absent. Enterprise teams are advised to separate read and write tool permissions, require explicit approval before sensitive actions, keep secrets out of agent context, and treat tool behavior changes as governed security events.

prompt injection MCP agentic AI tool permissions secrets management NHI Governance execution controls