Topic starter
06/09/2026 3:45 pm
TL;DR: Shadow AI is unsanctioned AI use that often hides inside SaaS features, browser tools, API connections, and MCP gateways, so LEVO argues discovery must cover both tool sprawl and identity sprawl. The governance gap is no longer visibility alone, but whether teams can separate safe enablement from high-risk containment before sensitive data moves into unreviewed AI paths.
Editorial analysis by NHI Mgmt Group, based on content published by LEVO: “Shadow AI Discovery Playbook: Find Unapproved AI Use Before It Becomes a Breach”.
Key questions
Q: How should security teams govern Shadow AI in SaaS applications?
A: Security teams should govern Shadow AI by classifying AI-capable SaaS tools, deciding what data each tool may process, and enforcing those decisions centrally.
Q: Why does shadow AI create risk even when there is no obvious external traffic?
A: Because many AI tools now run locally, inside IDE extensions, containers, or MCP-connected workflows that do not depend on a visible outbound session.
Q: What are the signs that an AI project has become shadow AI?
A: The main signs are orphaned agents, unmanaged MCP connections, and AI logic appearing in branches or services without a clear owner or approved governance path.
Practitioner guidance
- Define approved and restricted AI use cases Publish an allowlist of approved AI services, models, and connectors, then classify restricted data types that can never flow into unapproved tools.
- Inventory AI features inside SaaS platforms Review tenant settings, admin toggles, and user-level enablement so hidden AI features do not inherit sensitive data access by default.
- Map AI-linked tokens and grants Find OAuth grants, API keys, and service accounts tied to AI apps or AI features, then remove shared or long-lived credentials.
Bottom line: Shadow AI becomes a governance problem when AI features inherit access through SaaS, browsers, APIs, and MCP-connected workflows.
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06/09/2026 3:45 pm
Shadow AI discovery is now an identity governance problem, not a policy enforcement problem. Policies describe acceptable use, but they do not reveal which accounts, tokens, and delegated grants are actually moving data. Once AI is embedded in SaaS and browser workflows, the real control point shifts to identity inventory and access scope. Practitioners should treat discovery as a live governance process, not a static policy register.
A few things that frame the scale:
- 76% of organisations cite shadow AI as a definite or probable problem, up from 61% in 2025, according to HiddenLayer's 2026 AI Threat Landscape Report.
A question worth separating out:
Q: When should organisations block an AI app instead of approving it?
A: Block an AI app when its access scope, data handling, or downstream integrations exceed the organisation's risk tolerance and cannot be constrained with policy. If the app touches sensitive systems, lacks credible security posture, or can widen access dynamically, approval should wait until controls can be enforced at runtime.
👉 Read our full editorial: Shadow AI discovery needs identity-aware controls, not just policy