Shadow AI Controls and Enterprise Guardrails
Understand how to define and enforce boundaries for sanctioned AI use through an enterprise AI gateway. Learn to detect and respond to shadow AI risks by implementing identity, authorization, and egress controls. Explore strategies for maintaining auditability and applying separation of duties to protect regulated data and comply with security policies.
Supply-chain controls apply only when traffic flows through the reviewed model and tool inventory. Unsanctioned tools, personal API keys, and ad hoc agent routes can bypass those controls and send regulated data outside enterprise logging, authorization, and policy enforcement. Shadow AI is therefore both an operating-model problem and a control-placement problem.
The architectural decision in this lesson is to define the boundary for sanctioned use. Route all generative AI traffic that processes or may access regulated data, internal code, customer identifiers, or corporate credentials through the AI gateway. This routing enables consistent identity, policy-enforcement, egress-control, and audit-evidence requirements. Limit exceptions to explicitly registered workloads with documented compensating controls. Require measurable evidence that each exception provides equivalent logging, egress controls, authorization, and data-handling protections. ...