Discover Shadow AI

Use Case

Continuously identify unknown AI applications, models, agents, MCP servers, and connected services wherever they appear. Correlate each discovery with ownership, usage, provenance, and risk intelligence so security teams can govern AI adoption without relying on incomplete manual inventories.

‍Challenges

Shadow AI now extends far beyond unapproved chatbots. AI resources can enter through employee endpoints, coding assistants, development platforms, open-source repositories, agent frameworks, and MCP infrastructure, creating several recurring challenges:

• Unknown AI applications and services operate outside security review
• Models, agents, and MCP servers lack clear ownership or provenance
• Traditional inventories miss AI-specific components and runtime connections
• Manual review cannot keep pace with rapidly changing AI ecosystems

Solution

PointGuard AI provides a comprehensive solution that turns Shadow AI discovery into a continuous risk and governance workflow:

1. Discover AI across the enterprise. Use AI Discovery & Inventory and Workforce AI Usage Control to identify models, agents, applications, MCP assets, and external services across code, platforms, endpoints, and runtime activity.

2. Build a dynamic inventory. Consolidate discoveries into a current system of record with ownership, business purpose, connected applications, approval status, and lifecycle context.

3. Assess component risk. Apply PointGuard AI risk intelligence to evaluate open-source models and MCP servers for security, operational controls, provenance, and adoption maturity.

4. Route assets into governance. Use AI Governance workflows to review, approve, restrict, investigate, or remediate newly discovered resources based on policy and risk.

This approach creates a continuous path from discovery to risk assessment, accountability, and enterprise governance.

Risks Addressed

The solution addresses these OWASP and NIST threat controls:

OWASP Top 10 for LLMs

• LLM02:2026: Sensitive Information Disclosure
• LLM03:2026: Excessive Agency
• LLM04:2026: Supply Chain
• LLM05:2026: Data and Model Poisoning

OWASP Top 10 for Agentic Applications

• ASI02: Tool Misuse and Exploitation
• ASI03: Identity and Privilege Abuse
• ASI04: Agentic Supply Chain Vulnerabilities
• ASI10: Rogue Agents

NIST AI RMF

• GOVERN 1.4: establish transparent risk policies and controls
• GOVERN 6.1: address risks associated with third-party AI entities
• MAP 1.1: document intended use, context, lifecycle, and risk
• MANAGE 4.1: implement post-deployment monitoring and change management