shadow ai discovery governance radar

The problem

Employees often deploy custom GPTs, MCP servers, and unofficial AI bots without IT oversight, creating massive blind spots for data privacy and security. These "shadow AI" assets bypass standard DLP and governance protocols, leaving sensitive company data exposed to unvetted third-party providers.

What it does

• Scans code repos, configuration files, and API gateways to inventory unofficial AI agents and bots.
• Maps asset ownership, data access levels, and specific permissions (Read/Write/Admin).
• Categorizes assets into types like Custom GPTs, MCP Tools, and Copilots.
• Performs risk scoring based on data exposure, compliance violations (GDPR/HIPAA), and authentication strength.
• Generates structured remediation plans to bridge identified governance gaps.

Why this beats prompting it yourself

General prompts lack the structured logic required to cross-reference network traffic, cloud logs, and registry files for AI-specific signatures. This skill provides a systematic framework for classification and risk scoring that ensures no asset is missed, transforming vague concerns into an actionable governance registry.

Use cases

• Conducting a pre-audit inventory of AI tools before a security compliance review.
• Identifying high-risk custom GPTs that have write access to sensitive CRMs.
• Mapping the footprint of MCP (Model Context Protocol) tools across developer environments.
• Establishing a formal AI registry by surfacing and vetting existing shadow deployments.

Known limitations

Discovery is limited to data sources the agent has authorized access to scan. It cannot detect AI usage within encrypted personal traffic or on unmanaged hardware.