AI adoption is spreading faster than enterprise control. Runtime visibility, policy enforcement, and continuous evidence generation for AI agents, copilots, and internal AI applications.
Your organization is already using AI: LLM APIs, coding assistants, internal applications, SaaS-embedded AI, agentic workflows. But most enterprises lack a unified way to answer:
What AI is being used?
Shadow AI is the new shadow IT.
Who owns it?
Without clear ownership, governance fails.
What data does it touch?
Data lineage matters. Leakage risk is real.
Is it monitored after deployment?
Policy theater stops at the gate. Runtime matters.
Can we prove controls worked?
Auditors need defensible evidence, not checklists.
Which regulations apply?
EU AI Act, NIST AI RMF, ISO 42001, GDPR...
Goal: Scale AI across the enterprise without losing control of tools, vendors, data, and compliance exposure.
Goal: Understand and control how AI systems access sensitive data, tools, APIs, and enterprise systems.
Goal: Prove that AI systems are classified, controlled, monitored, documented, and aligned to applicable regulations.
Governance without evidence is policy theater. Governance without runtime insight is guesswork. The strongest enterprises integrate all five disciplines into a single operating model.
The system of record for all AI. Who owns it? What data does it touch? Which vendor powers it? What's the risk tier?
From intake through retirement. Risk questionnaire, approval gates, deployment controls, periodic review. Workflows, not spreadsheets.
What actually happened. Policy violations. Tool invocations. Sensitive-data detection. Agent behavior. Observed, not assumed.
Active controls, not passive dashboards. Allow, warn, block, or route for approval — at the point the AI acts.
Timestamped, attributable, audit-ready. Auto-generated from runtime events, approvals, and controls. Not a spreadsheet.
Three dashboards. Three different stakeholders. One control plane. Every asset classified, every action logged, every evidence artifact generated automatically.
| Asset | Owner | Type | Risk | Status | Last reviewed |
|---|---|---|---|---|---|
| Coding Assistant Engineering |
Marcus T. | agent | High | Monitored | 2 days ago |
| HR Screening Bot People & Talent |
Aisha P. | application | High | Under Review | Pending |
| Customer Support AI Customer Experience |
James K. | rag_app | Medium | Approved | 1 week ago |
| Contract Analysis Legal |
Priya N. | application | Medium | Approved | 3 weeks ago |
| Analytics Copilot Data Platform |
Wei L. | copilot | Low | Monitored | 5 days ago |
tool:git_push → main branch
PII detected in prompt payload
action:write_file → /etc/config.prod
hallucination_flag: confidence 0.34
model_invoked: gemini-1.5-pro
Coding Assistant approved by Security — 2 days ago
HR Bot risk assessment filed — 5 days ago
Support AI quarterly review due — 12 days
Contract Analysis missing model factsheet
Flowcraft translates frameworks into operational controls — no manual interpretation required.
Trustworthy AI + human rights baseline
Risk-based obligations + conformity assessment
Govern, map, measure, manage
AI management system. Continual improvement.
AI risk management integrated into your processes
Security controls for LLM applications
Watch the complete flow from shadow AI detection to runtime enforcement:
Detect: An unregistered AI agent session is detected across your environment.
Risk-tier: The system auto-classifies risk based on repo sensitivity, agent autonomy, and tool permissions.
Enforce: A policy violation is detected — the agent attempted a restricted tool action. The policy engine requires approval.
Evidence: Approval is captured. Runtime evidence is attached to the asset factsheet. The audit trail is immutable.
Report: Dashboard shows the risk, owner, session, policy decision, and evidence. Audit package ready to export.
"This is not another policy checklist. This shows what is actually happening."
Most governance tools start with questionnaires. We detect actual AI activity, then classify risk based on what we observe — not what teams self-report.
Agents require new controls: tool governance, action boundaries, autonomy scoring, loop detection. We built for the operating model enterprises are actually deploying.
Evidence is generated automatically from runtime events, workflows, approvals, and controls. Not maintained in a spreadsheet. Defensible at any point in time.
Zero-trust ready. Deploy on your cloud, your Kubernetes cluster, or self-managed data centers. No vendor lock-in. Your data never leaves your environment.
Start with a readiness assessment. We'll map your AI inventory, classify risk, and identify exactly where your governance gaps are.
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