Low visibility into real AI usage
AI now lands in repositories, copilots, integrations, model wrappers, and agentic workflows before most organizations can even name what they operate.
AI Agile Governance Control Plane
Critical infrastructure for governing enterprise AI systems.
Governance AI gives organizations the control layer they need to discover AI, govern usage, apply runtime guardrails, detect Shadow AI, and operate AI systems with security, risk, and compliance in one unified control plane.
- Discover AI across repositories and CI/CD
- Build a live inventory of AI assets
- Apply runtime guardrails to prompts, outputs, and tool calls
- Detect Shadow AI before it becomes unmanaged infrastructure
Control-plane model
RepositoriesPipelinesAI Apps
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Governance AI Control Plane
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The problem
AI is being deployed faster than enterprises can govern it.
AI now appears in code repositories, CI/CD pipelines, internal copilots, customer-facing applications, and agentic workflows. Most organizations still lack a structural control layer to see where AI is used, govern how it behaves, and prove compliance with confidence.
Shadow AI across teams and pipelines
Undeclared projects, AI dependencies, and experimental runtimes become business infrastructure long before governance programs can see them.
Policies disconnected from execution
Most governance work stops at documents, spreadsheets, and review gates instead of reaching prompts, outputs, tool calls, and runtime decisions.
Fragmented security, risk, and compliance
Security sees exposure, compliance sees frameworks, engineering sees delivery pressure. Without a control layer, nobody sees the same system.
Category
A new infrastructure category for enterprise AI
Governance AI introduces the AI Agile Governance Control Plane: a new software layer that continuously discovers AI systems, builds live inventory, orchestrates governance policies, and applies controls at runtime.
They summarize what already happened.
They discover, decide, and enforce where systems actually run.
Three planes
Three planes. One control layer.
Discovery Plane
Discover AI across repositories, dependencies, pipelines, undeclared projects, and model-connected applications.
Governance Plane
Define policy intent, map controls, align frameworks, score risk, and materialize governance workflows with evidence.
Runtime Control Plane
Evaluate prompts, outputs, and tool calls in execution time with allow, modify, and block decisions plus observable telemetry.
How it works
How Governance AI works
Distributed scanners and SDKs feed the control plane with evidence and execution context. The platform correlates AI assets, evaluates policies, records telemetry, and supports allow/block decisions where it matters most.
RepositoriesPipelinesAI Applications
Governance AI Control Plane
InventoryPoliciesRuntime DecisionsTelemetry
Product capabilities
Operational governance, not paper governance
Platform proof
A real operational platform, not a conceptual promise
Product surfaces prove how discovery, policy, runtime decisions, and evidence come together inside one enterprise control plane.
Inventory and graph materialization
Graph-native visibility across AI assets, dependencies, and attack paths.
Shadow AI detection
Discovery of AI-related exposure in repositories, toolchains, and undeclared projects.
Policy orchestration
Policies, frameworks, detections, and exceptions linked in one operational control layer.
Runtime guardrails
Guardrail design and runtime evaluation over prompts, outputs, and tool calls.
Issue detail and evidence
Technical context, governance implications, and next actions in the same incident surface.
Compliance mapping
Framework-aligned governance evidence and control coverage in a real operational view.
Use cases
Built for teams operating AI as business-critical infrastructure
Security and CISO teams
See enterprise AI exposure as infrastructure, not scattered tickets, and connect findings to runtime controls.
AI platform teams
Operate AI services with structural policy, asset inventory, and telemetry instead of one-off integration logic.
Engineering organizations
Expose undeclared AI usage across repos and CI/CD while keeping delivery and governance in the same operating model.
Risk and compliance teams
Map operational evidence to frameworks and policy obligations without depending on paper-only attestations.
Enterprises building copilots and agents
Control prompts, outputs, tool execution, and evolving runtime behavior across internal and customer-facing AI applications.
Trust and research
Grounded in operational practice and AI governance research
Combine platform execution with research, policy analysis, and applied architecture for real-world enterprise AI governance.
Category thinking
Governance AI frames enterprise AI governance as a missing infrastructure layer, not a compliance afterthought.
Applied architecture
The platform is grounded in control-plane thinking, runtime enforcement, and evidence-driven governance workflows.
Governance research
Public writing, preprints, and regulatory analysis connect product execution to broader AI governance practice.
Next step
Operate AI with structural control
Governance AI helps organizations move from fragmented AI adoption to governed AI operations.