IntegraGuard — AI Usage Governance
An enterprise control plane for discovering AI usage, preventing unsafe data transfer, governing sanctioned LLM access and producing investigation-ready evidence.
What this capability solves
Public and embedded GenAI adoption creates shadow AI, sensitive-data leakage, unmanaged API keys, inconsistent controls and weak audit evidence. IntegraGuard provides a staged path from visibility to policy enforcement and governed AI access.
Technology is implemented as an operating capability: architecture, integration, governance, assurance, people, procedures and measurable outcomes are designed together.
Capability model
Modular building blocks allow the scope to start with a focused pilot and expand into an enterprise operating model.
AI Discovery
Identify sanctioned and unsanctioned AI services, users, usage patterns and vendor risk signals.
Browser Enforcement
Inspect prompt interactions and apply warn, mask, block or justification controls to sensitive data movement.
LLM Gateway
Centralize API access, model routing, redaction, policy checks, rate limits, budgets and request/response audit.
AI Posture
Inventory AI applications, models, keys, data flows and governance attributes for risk ownership.
Security Console
Policy management, alerts, RBAC, dashboards, exception workflows, case evidence and SIEM forwarding.
Control Analytics
Trend analysis, false-positive feedback, risky destination scoring and management reporting.
How the capability fits together
Final topology, control placement and deployment model are validated during discovery and detailed design.
Controls & governance
- Risk-based policy by data class and use purpose
- Exception and approval workflow
- Role-aware controls and privileged administration
- Tamper-evident activity logs
- Privacy-minimized monitoring design
- Continuous classifier tuning and red-team validation
Priority use cases
- Shadow AI discovery
- GenAI browser DLP
- Secure LLM API access
- AI asset inventory
- Sensitive prompt investigation
- AI acceptable-use evidence
Key deliverables
- AI usage baseline
- Policy matrix and enforcement rules
- Gateway deployment blueprint
- AI asset inventory
- Alert and investigation playbook
- Executive risk dashboard
Integration considerations
- Enterprise browser management
- Identity and group policy
- LLM/model APIs
- SIEM/SOAR and ITSM
- Existing DLP/SSE controls
- GRC/evidence repository
Phased delivery
Each phase ends with evidence, acceptance criteria and a decision gate before broader scale-out.