A technical deep dive into the threat model, architecture, risk scoring methodology, and compliance framework for governing AI agent deployments in production environments.
As autonomous AI agents are increasingly deployed in production environments with access to critical business systems, a significant security gap has emerged: the absence of runtime governance infrastructure.
Existing AI security tools address the input layer including prompt injection, adversarial inputs, and model manipulation but provide no mechanisms to govern, constrain, or reverse the actions agents take once they have system access.
This paper introduces Vaultak, a runtime security framework that provides behavioral monitoring, pre-execution enforcement, declarative permission profiles, security policy management, and automatic rollback for AI agent systems.
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