PRD-STD-013: Multi-Tenant AI Governance
Standard ID: PRD-STD-013 Version: 1.0 Status: Active Compliance Level: Level 2 (Managed) Effective Date: 2026-02-22 Last Reviewed: 2026-02-22
This page is the normative source of requirements for this control area. Use it to define policy, evidence expectations, and audit/compliance criteria.
For implementation and rollout support:
- Execution plan: Apply-Ready Rollout Kit
- Adoption sequencing: Production Rollout Paths
- Hands-on tutorials: Production Tutorials & Starter Guides
- Runnable repos / apply paths: Reference Implementations
Use the Compliance Level metadata on this page to sequence adoption with other PRD-STDs.
1. Purpose
This standard defines mandatory governance controls for multi-tenant AI product architectures where multiple customers, organizations, or business units share AI infrastructure. Without explicit tenant governance, multi-tenant AI products risk cross-tenant data leakage, inconsistent safety behavior, unattributable costs, and SLA violations that affect individual customers.
Multi-tenant AI products amplify standard multi-tenancy risks because AI inference pipelines carry conversational context, prompt state, and behavioral configuration that can leak between tenants if isolation is not explicitly enforced.
2. Scope
This standard applies to:
- Any AI product where multiple tenants share inference infrastructure, model instances, data pipelines, or AI feature surfaces
- SaaS AI products, platform-as-a-service AI, embedded AI features in multi-tenant applications, and white-label AI solutions
- Both first-party multi-tenant deployments and third-party platform integrations serving multiple tenant organizations
This standard does not replace PRD-STD-010 through PRD-STD-012. It adds tenant-scoped controls required for multi-tenant AI product operation.
3. Definitions
| Term | Definition |
|---|---|
| Tenant | A distinct customer, organization, or business unit with logically separated data and configuration in a shared AI product |
| Tenant Isolation | Controls ensuring one tenant's data, configuration, and inference context cannot be accessed or influenced by another tenant |
| Tenant Safety Profile | A tenant-scoped configuration defining permitted and prohibited AI behaviors, content policies, and escalation thresholds |
| Tenant Cost Attribution | The ability to trace AI inference costs to a specific tenant for billing, budgeting, and anomaly detection |
| Tenant SLA | A service-level agreement defining availability, latency, throughput, and quality guarantees specific to a tenant or tenant tier |
| Cross-Tenant Leakage | An event where data, model context, prompt content, or inference state from one tenant is exposed to or influences another tenant |
4. Requirements
4.1 Tenant Data Isolation
REQ-013-01: Every multi-tenant AI product MUST implement logical tenant isolation ensuring tenant data, prompt context, and inference state cannot be accessed or influenced by other tenants.
REQ-013-02: Tenant context MUST be scoped per request. Shared inference sessions, context windows, or conversation state MUST NOT persist across tenant boundaries.
REQ-013-03: Tenant isolation MUST be validated through automated cross-tenant leakage tests executed on every release candidate.
REQ-013-04: Tier 2 and Tier 3 features processing Confidential or Restricted data MUST implement tenant-scoped encryption for data at rest and in transit.
4.2 Tenant-Scoped Model Configuration & Safety Policies
REQ-013-05: Every multi-tenant AI product MUST support tenant-scoped safety profiles that define permitted and prohibited AI behaviors per tenant.
REQ-013-06: Tenant safety profile changes MUST be version-controlled and auditable, with named approvers per change.
REQ-013-07: Default safety policies MUST apply to all tenants. Tenant-specific overrides MUST only weaken defaults when explicitly approved by the product governance owner.
REQ-013-08: Organizations SHOULD support tenant-scoped system instructions, prompt templates, and content filtering thresholds to enable tenant customization within governance boundaries.
4.3 Per-Tenant Audit Trails
REQ-013-09: Every AI inference request MUST be attributed to a specific tenant in audit logs.
REQ-013-10: Audit log retention periods MUST meet or exceed the contractual obligations for each tenant.
REQ-013-11: Cross-tenant data access by internal personnel MUST be logged as a security event and MUST require explicit authorization with business justification.
REQ-013-12: Organizations SHOULD provide tenant-accessible audit log exports or dashboards to support customer compliance requirements.
4.4 Tenant-Level Cost Allocation
REQ-013-13: AI inference costs MUST be attributable to individual tenants at the request, session, or billing-period level.
REQ-013-14: Per-tenant cost anomaly detection MUST be implemented with alerting thresholds that trigger investigation.
REQ-013-15: Organizations SHOULD define per-tenant cost budgets with automated enforcement or notification when budgets are approached.
REQ-013-16: Organizations SHOULD support granular cost reporting by tenant, feature, model, and time period to enable usage-based billing and capacity planning.
4.5 SLA Mapping Per Tenant Tier
REQ-013-17: Every multi-tenant AI product MUST define tenant tiers with documented SLO targets for availability, latency, and throughput.
REQ-013-18: Resource allocation and priority queuing MUST align with tenant tier definitions. Lower-tier tenants MUST NOT degrade higher-tier tenant SLOs.
REQ-013-19: SLA breach events MUST be detected, recorded, and reported to affected tenants within contractually defined notification windows.
REQ-013-20: Organizations SHOULD implement tenant-tier-aware graceful degradation so that under capacity pressure, lower-priority tenants degrade before higher-priority tenants according to documented degradation order.
5. Implementation Guidance
Minimum Tenant Governance Pack
Teams SHOULD establish:
- Tenant isolation architecture document with data flow diagrams
- Cross-tenant leakage test suite integrated in CI/CD
- Tenant safety profile template with default and override configuration
- Per-tenant audit log pipeline with configurable retention
- Cost attribution dashboard with per-tenant drill-down
- Tenant tier SLA matrix with monitoring and alerting
Example Tenant Safety Profile
tenant_id: "tenant-acme-corp"
tenant_tier: "enterprise"
safety_profile:
version: "1.3"
approved_by: "product-governance-owner"
approved_date: "2026-02-22"
content_policy:
prohibited_topics: ["competitor-analysis", "legal-advice"]
content_filtering_level: "strict"
pii_handling: "redact-before-inference"
behavior_limits:
max_response_length_tokens: 2048
allowed_languages: ["en", "ar"]
human_handoff_threshold: 0.7
escalation:
safety_violations: "security-team@acme-corp.com"
sla_breaches: "ops-team@acme-corp.com"
Minimum Operational Metrics
Track at least:
- cross-tenant leakage test pass rate
- tenant safety profile compliance rate
- per-tenant inference cost variance from budget
- per-tenant SLO attainment by tier
- cross-tenant access security events
6. Exceptions & Waiver Process
Waivers are limited to non-isolation procedural controls and MUST include:
- business justification
- compensating controls
- named approver
- expiration date (maximum 30 days)
No waivers are permitted for:
- missing tenant isolation testing
- cross-tenant leakage in production
- bypassing tenant safety profiles
- absent per-tenant audit logging
7. Related Standards
- PRD-STD-010: AI Product Safety & Trust Controls
- PRD-STD-011: Model & Data Governance
- PRD-STD-012: Inference Reliability & Cost Controls
- PRD-STD-014: AI Product Privacy & Data Rights
- Pillar 2: Governance & Risk
- AI Product Lifecycle
8. Revision History
| Version | Date | Author | Changes |
|---|---|---|---|
| 1.0 | 2026-02-22 | AEEF Standards Committee | Initial release |