EnaGuard
AI governance is only defensible when it is tied to evidence
Policies, committees, and dashboards matter, but they do not prove that enterprise AI is governed. EnaGuard maps AI governance claims to evidence across inventory, risk, architecture, human oversight, monitoring, incidents, and third-party AI risk.
Where AI governance needs proof
EnaGuard treats governance as an operating discipline, not a policy archive. Each domain should produce evidence a board, audit team, or risk committee can review.
AI system inventory
Which AI systems exist, which are in production, who owns them, and how shadow AI is detected.
Risk classification
How use cases are classified by impact, data sensitivity, autonomy, regulation, and business criticality.
Control design
How identity, access, model gateways, data lineage, guardrails, logging, and fallback paths are designed.
Human oversight
Where approvals, thresholds, stop mechanisms, and escalation routes sit inside the real operating flow.
Monitoring and incidents
How quality, drift, security events, cost, outage, and business impact are monitored and escalated.
Third-party AI risk
How provider dependency, contractual commitments, data processing, lock-in, and outage exposure are evidenced.
Governance language should resolve into reviewable artifacts
| Governance claim | Evidence to ask for | EnaGuard question |
|---|---|---|
| We know our AI estate. | Current inventory, ownership map, production status, and exception list. | Can the inventory be reconciled against actual usage signals? |
| Our AI risks are managed. | Risk classification, mitigation owners, target dates, and residual risk decisions. | Are open risks visible to management, or buried in project detail? |
| Human oversight is in place. | Approval thresholds, transaction limits, audit trail, and override procedures. | Does oversight exist in the workflow, or only in policy? |
| Controls are operating. | Configuration evidence, test results, monitoring data, and incident records. | What proves the control worked under load, failure, or attack? |
| Vendors are under control. | Provider architecture, contracts, data flows, resilience plans, and exit paths. | What happens if the primary AI provider changes terms or fails? |
Built for governance, risk management, and verification conversations
EnaGuard can be mapped to recognized AI governance and risk references, but the site should not imply certification, legal advice, or endorsement by a standards body.
NIST AI RMF language
Governance, mapping, measurement, and management become practical when each function is connected to evidence.
ISO/IEC and EU AI Act expectations
Management systems, risk processes, documentation, oversight, and traceability are translated into assessment questions.
Security and resilience references
OWASP, MITRE ATLAS, CSA, and industry expectations inform the control language without exposing EnaGuard scoring logic.
The right AI governance question is not “do we have a policy?” It is “what evidence shows the policy operates in production?”
Use the Red Flag Check to identify where governance claims may still rely on declaration, then use an EnaGuard discovery call to decide whether Signal, Focus, or Verify is the right next step.