You can't secure AI you can't see
Discover every AI application and its component, validate automatically through quality gates, map everything it connects to, and expose shadow AI.
An AI application is never just a model
A single loan or support assistant may sit on application code, MCP servers, one or more LLMs, databases, RAG sources and policy documents. Knowing that full ecosystem is the starting point for securing and governing it.
Cygeniq discovers AI applications through connectors, builds an AI Bill of Materials (AI-BOM) for each one, and checks every component against quality gates. The result is a qualified inventory of approved and unapproved AI, in production and non-production, and shadow AI detected in one pass.
Most organizations cannot list the AI they run, let alone everything it connects to
That makes every downstream security and compliance effort guesswork.
AI applications are built fast, often outside central IT and security.
Each application connects to many components: models, MCP servers, databases, RAG sources that nobody has mapped.
There is no easy way to tell approved AI from shadow AI.
Without an inventory, risk assessment and compliance cannot start.
Five capabilities, one qualified inventory
Discovery of AI application and its component
Enable a connector (for example, Azure, git, hugging face) and Cygeniq scans the environment to find AI applications and verify their architecture.
AI Bill of Materials (AI-BOM)
A complete list of what each AI application is made of and connected to: models, MCP servers, databases, RAG sources and policy documents.
Asset profiling
Each asset is profiled: what it does, which model it uses, its context, input and output modalities, how it was trained and which controls apply.
Quality gates
Rulebooks applied at onboarding, for example, verifying that an MCP server is configured correctly.
Build-ready status and shadow AI detection
Applications that pass every gate are build-ready. Non-build-ready or unapproved AI found running in production is flagged as shadow AI.
Six steps from connector to shadow AI, flagged and ready for risk assessment
Connect
Enable the connector for the environment where AI is hosted.
Discover
Scan the environment to find AI applications and their architecture.
Build the AI-BOM
List every model, MCP server, database, RAG source and policy document connected to each application.
Apply quality gates
Check each component against the configured rules.
Classify
Mark each application build-ready or incomplete, approved or unapproved, production or non-production.
Flag shadow AI
Surface non-build-ready or unapproved AI running in production, ready for risk assessment.
Scanning an Azure environment
An enterprise enables the Azure connector to understand the AI running in its cloud.
The scan finds an AI application and builds its AI-BOM: four MCP servers, five database connections, five RAG connections and four policy document connections. A quality gate for MCP servers checks each one is configured properly, and one fails.
The application is therefore not build-ready, but it is already running in production, so it is flagged as shadow AI. The team fixes the MCP configuration, the application passes its quality gates and is approved, and it moves into risk assessment.
A foundation for everything that comes next
One qualified inventory
Every AI application and its components, approved and unapproved, in one place.
Shadow AI exposed
Unapproved or misconfigured AI in production is detected in one pass.
Configuration checked at the door
Quality gates catch problems during onboarding.
A foundation for everything else
Risk assessment, red teaming and compliance all start from an accurate inventory.
| Application | Environment | Approval Status | Build-Ready | Owner |
|---|---|---|---|---|
| Support Assistant | Production | Approved | Yes | Platform Team |
| Loan Copilot | Production | Unapproved | No | Unassigned |
| Internal Search Agent | Non-Production | Approved | Yes | Data Team |
| HR Policy Bot | Production | Pending | No | HR Ops |
Illustrative sample data for layout purposes only.
One inventory, read differently by every stakeholder
CISO
A complete picture of the AI attack surface.
CIO and Enterprise Architects
A clear map of how AI applications are built and connected.
AI Governance Officers
An approved-versus-unapproved AI register.
Security Teams
Shadow AI surfaced without manual hunting.
See what's connected to the AI you already run
Enable a connector for your environment and get a qualified inventory: approved and unapproved AI, in production and non-production, with shadow AI flagged in one pass.
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