Shadow AI Agents Inside SaaS Products

Product teams add copilots, LLM integrations, and MCP servers to SaaS products quickly, often inside individual tenant workspaces and outside any central review. Security teams cannot answer basic customer questions about which AI features exist, what data they reach, or who owns them. PointGuard AI discovers AI assets across clouds, repositories, and runtime telemetry and maintains a continuously updated registry.

AI features ship faster than security and product teams can track them.

• Teams add LLM calls and agents without security review.
• Tenant-level configurations create AI features no one inventoried.
• MCP servers connect product agents to internal systems.
• Customer security questionnaires ask for an inventory that does not exist.

AI Discovery & Inventory and Agent Mission Control build and maintain a registry of every AI asset in the product.

1. Discover across vectors. Scan cloud accounts, code repositories, API traffic, and runtime telemetry for models, agents, prompts, and MCP servers.

2. Register each asset. Record owner, product area, tenant scope, data access, and model provider for every AI feature and agent.

3. Assess risk. Flag agents with broad data access, unvetted models, or unauthenticated MCP connections. Prioritize those that touch customer data.

4. Keep it current. Rescan continuously and alert owners when new AI assets appear, so the registry reflects what ships.

• OWASP Top 10 for LLMs: LLM03 Supply Chain and LLM06 Excessive Agency, covering unvetted model providers and agents shipped with broad authority.
• OWASP Top 10 for Agentic Applications: ASI04 Agentic Supply Chain Vulnerabilities and ASI10 Rogue Agents, addressing unreviewed agent components and agents running outside governance.
• NIST AI RMF: GOVERN assigns ownership; MAP inventories AI features; MEASURE assesses exposure; MANAGE prioritizes remediation.