Key facts
| Policy foundation | Use-case register, approval gates and named accountable owners |
| Platform controls | SSO/SCIM, RBAC, audit export, residency selection, BYOK |
| Model oversight | Registry of models and versions with fallback chains and deprecation plans |
| Deployment models | Cloud, VPC, on-prem, air-gapped — matching control level to risk tier |
| Audit | Audit log export to SIEM; evidence retained per policy |
| Compliance posture | SOC 2 Type II and ISO 27001 readiness in progress (not yet certified); GDPR and PDPL alignment |
| SLA | 99.9% uptime on paid plans; Enterprise 4-hour support SLA — see /legal/sla |
| Status | Live API with 30+ models and automatic failover |
TL;DR
- Governance is an operating model: owners, registers, gates and evidence — not a policy PDF.
- Classify use cases by risk and map each tier to concrete controls and review requirements.
- Keep a model registry with versions, evaluations and deprecation fallbacks.
- Require human oversight for decisions that affect people's rights, money or safety.
- Make evidence routine: audit logs, approvals and drill results collected continuously.
How it works, step by step
- Write a one-page AI policy covering scope, principles, prohibited uses and accountability.
- Create a use-case register with owner, data classification, model, deployment tier and risk score.
- Define risk tiers and the controls each requires: review, evaluation, oversight, monitoring.
- Stand up a model registry with approved versions, evaluation results and fallback chains.
- Configure platform controls: SSO/SCIM, RBAC, audit export, residency, BYOK where required.
- Establish an AI review board with security, legal, data and business representation.
- Run quarterly reviews and an annual framework audit; keep evidence current.
Original data
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The six components
- Policy and accountability — who owns AI risk, what is allowed, how exceptions are handled.
- Register — every use case, model, data category and deployment tier in one place.
- Risk classification — tiers that determine required controls and approvals.
- Evaluation and approval gates — quality, safety and security checks before launch.
- Human oversight — review paths for consequential outputs.
- Audit and improvement — evidence, incidents and lessons feeding back into policy.
Use the register as a living document: if a use case is not listed, it is not approved.
Mapping risk tiers to deployment
Risk tier should drive architecture. Low-risk internal drafting can run on shared in-region cloud. Customer-facing or personal-data workloads benefit from a private endpoint in your VPC with SSO, RBAC and audit export. Regulated or classified workloads move to on-prem or air-gapped tiers with BYOK or HSM key custody. Plugsky supports this progression on one API contract, so raising the control level does not require rebuilding the application.
Evidence and audit readiness
Auditors ask three questions: can you list your AI systems, can you show who approved them, and can you prove what they processed? Answering requires a register, approval records, and audit logs exportable to your SIEM with attributable actors. Build evidence collection into workflow — a launch checklist that emits artifacts — rather than reconstructing it before an audit.
Common pitfalls
- A policy with no register, so nobody can enumerate AI use.
- Treating model evaluation as a one-time launch task instead of a lifecycle gate.
- Ignoring shadow AI usage in teams that bypassed procurement.
- No deprecation plan, leaving applications exposed when a model version retires.
- Audit logs enabled but never reviewed.
Honest comparison
| Capability | Plugsky | Hyperscaler AI platform | Building in-house |
|---|---|---|---|
| Identity controls | SSO/SCIM and RBAC on Enterprise | Mature IAM and policy tooling | You build and operate |
| Audit evidence | SIEM export with attributable events | Native cloud audit | Custom pipelines |
| Deployment progression | Cloud → VPC → on-prem → air-gapped on one API | Multiple product families | Rebuild per tier |
| Residency controls | Region-locked planes selectable per workspace | Region configuration per service | Your design |
| Model lifecycle | 30+ models with fallback chains | Catalog governed by vendor roadmap | You manage versions |
| Certifications | SOC 2 / ISO 27001 readiness in progress | Completed audits in many regions | Your own programme |
Frequently asked questions
Who should own AI governance?
A named accountable executive, supported by a cross-functional review board covering security, legal, data and business. Ownership must be explicit, not distributed by default.
Do we need a model registry?
Yes. A registry of approved models, versions, owners and evaluation results is the backbone of governance and the first artifact auditors request.
How often should evaluations run?
Before launch, after material model or prompt changes, and on a scheduled cadence for high-risk use cases. Automate regression suites where possible.
How does residency fit into governance?
Residency is a control selected by risk tier. The register should record the data plane and deployment model for each use case, with evidence of configuration.
What platform controls should we require from a vendor?
SSO/SCIM, granular RBAC, audit export, region selection, encryption with BYOK options, and a documented SLA. Plugsky provides these capabilities across its tiers.
How do we handle shadow AI?
Make the approved path fast and observable, publish clear prohibited uses, monitor key and usage anomalies, and review exceptions through the governance board.
Can governance work without certifications?
Yes, if controls are documented and evidenced. Treat in-progress certifications such as Plugsky's SOC 2 and ISO 27001 readiness as an item on the risk register with a timeline, not as a blocker to all AI adoption.