Shadow AI and SASE: what are your controls actually stopping?

TL;DR: Shadow AI creates the same visibility and data leakage problems as shadow IT, but with faster exploitation and higher stakes, according to Todyl. SASE can reduce exposure through web filtering, SSL inspection, ZTNA and download scanning, yet the real governance challenge is controlling what connects to client environments and what leaves them.

Editorial analysis by NHI Mgmt Group, based on content published by Todyl: “How SASE Reduces Your Clients' Attack Surface and Blocks Shadow AI”.

Key questions

Q: What breaks when shadow AI is not detected at the traffic layer?

A: When shadow AI is invisible at the traffic layer, security teams lose the ability to stop live data from reaching external models, prove which model handled which payload, or enforce consistent policy.

Q: Why does shadow AI increase data leakage risk in engineering teams?

A: Because developers often paste code, secrets, or internal diagrams into external tools to move faster, and those inputs can leave the enterprise boundary without the logging or deletion guarantees teams expect.

Q: What are the signs that shadow AI controls are failing in practice?

A: Shadow AI controls are failing when sensitive data moves through copy-paste, uploads, or API calls outside sanctioned workflows, yet the organisation has no alerting or enforcement at the point of transfer.

Practitioner guidance

• Block unapproved AI and LLM destinations Use web filtering to deny access to consumer AI services that are outside approved workflows, and maintain a policy list that reflects actual business use rather than broad category labels.
• Enforce identity-bound access at the session layer Tie access decisions to explicit user identity and least privilege so corporate credentials cannot be reused to create unsanctioned AI accounts or sessions.
• Inspect encrypted sessions for sensitive uploads Enable SSL inspection where appropriate so encrypted traffic cannot hide sensitive data moving into AI tools through legitimate-looking domains.

Bottom line: Shadow AI is not just an acceptable-use issue. It creates an access and data-exit path that can expose sensitive information before teams notice.

Explore further

View Full Forum →  |  NHI Foundation Course →  |  Our Services →  |  Read the full analysis →