Enterprise Azure AI Gateway & Governance Platform

Executive summary

Give enterprise AI consumers a common access contract, with identity, model permissions, safety and cost policy evaluated before inference.

The proposed platform places Azure API Management between callers and approved Microsoft Foundry / Azure OpenAI deployments. A logical consumer links users, applications and agents to policy ownership and usage attribution. Azure AI Search supports permission-aware enterprise RAG.

Here, “secure” describes intended controls—validated identity, explicit authorization and protected data handling—not an assurance of production security. No production adoption, measured performance or financial savings are claimed.

Business problem

Direct model access across enterprise applications, copilots and agents distributes policy enforcement across teams. The resulting problems include:

• Inconsistent authentication and authorization, with no unified logical-consumer identity.
• Uncontrolled model access and weak requests-per-minute (RPM), tokens-per-minute (TPM) and monthly quota governance.
• Limited cost attribution and weak budget, chargeback and showback controls.
• Inconsistent content safety and PII protection, alongside fragmented telemetry.
• Difficult model routing, deployment migration and controlled failover.

Role and responsibilities

This independent portfolio design presents the architecture responsibilities: define trust boundaries and request contracts; specify consumer resolution and policies; document ADRs and failure behavior; and establish evidence requirements for production readiness.

Samson’s current professional designation is Technical Architect. Senior Azure AI Solution Architect is the specialization and target role, not a claimed employment title for this reference project. No client engagement or implementation delivery is asserted here.

Requirements and constraints

• Authorize each caller and resolve it to an enabled consumer before provider access.
• Apply per-consumer model, RPM, TPM, monthly token and budget policies.
• Preserve product attribution across workload and delegated identities.
• Keep prompts, retrieved documents and sensitive identifiers out of routine logs.
• Support streaming, partial failures and accounting reconciliation without unlimited retries.
• Respect deployment-region and data-residency constraints; validate service tier and region support before deployment.

Latency objectives, cache lifetimes, availability objectives and recovery targets remain to be agreed with workload owners. No numerical production SLO has been validated.

High-level architecture

Trust boundaries separate external callers, the gateway and policy services, and model/retrieval backends. The gateway validates access; workloads remain responsible for tool authorization and document-level retrieval permissions.

Request lifecycle

1. Obtain an Entra access token for the gateway audience using the appropriate workload or delegated flow.
2. Validate token signature, issuer, audience, expiry and tenant; establish a correlation ID.
3. Resolve verified claims to a logical consumer using cached or authoritative configuration.
4. Authorize the caller, consumer and model; check rate limits, token reservation, quota and budget.
5. Apply required PII and content-safety inspection; constrain retrieval and tools to the caller’s permissions.
6. Route through managed identity to an approved deployment; enforce timeouts and bounded retries.
7. Inspect the response where required. Reconcile provider usage, release unused reservations and emit redacted telemetry.

Streaming requires separate design validation: inspect before release where required, account for cancellation, and do not claim whole-response safety if tokens have already reached the caller.

Identity and logical-consumer resolution

Do not infer a workload solely from the presence of azp: delegated tokens can also identify the client application. Distinguish token context, validate the permission model, handle group-overage claims through an authorized lookup, and deny ambiguous mappings. Never trust a caller-supplied consumer header as authorization.

Both paths converge on an enabled consumer. Unknown identities, unresolved membership, missing claims and unauthorized mappings deny access by default.

Governance-policy design

A versioned consumer record defines model allowlists, authorization and cost policy. Evaluate cheap access checks before expensive inspection or inference. Use an atomic reservation ledger for concurrent usage admission; Redis configuration caching alone does not provide reliable monthly accounting.

Synthetic consumer configuration

Synthetic demonstration data. Every value below is illustrative, including the limits and dollar budget. These are not real client settings, observed lab results or approved production targets.

{
  "consumerId": "finance-copilot",
  "enabled": true,
  "allowedModels": [
    "sample-chat-model",
    "sample-embedding-model"
  ],
  "rpmLimit": 120,
  "tpmLimit": 250000,
  "monthlyTokenQuota": 25000000,
  "monthlyBudgetUsd": 1000,
  "allowedGroups": [
    "finance-ai-users"
  ],
  "contentSafetyLevel": "medium",
  "piiProtection": true
}

Group names are readable placeholders; deployed policy should resolve immutable identifiers. Define month boundaries, currency, policy versioning, invalidation and revocation behavior before implementation.

Security and Responsible AI

• Identity: Entra ID with OAuth 2.0 access tokens; OpenID Connect for interactive sign-in where needed. Validate JWTs at the gateway and apply least-privilege RBAC.
• Backend access: managed identities; Key Vault for remaining secrets and certificates; consumer and model allowlists.
• Data protection: PII redaction or anonymization before inference and in logs, with workload-specific rules and retention controls.
• Responsible AI: Azure AI Content Safety, prompt-injection inspection, untrusted-document boundaries and output validation. Safety classifiers do not replace authorization.
• Agent controls: scoped tools, outbound destination allowlists and human approval for high-risk actions to reduce data exfiltration and unintended changes.
• Auditability: record policy version, outcome and correlation identifiers without storing raw prompts by default.
• Production targets: private endpoints, VNet integration, verified regional data flows, residency review and least-privilege operational access.

Residual risks include missed PII, jailbreaks, poisoned retrieval content, excessive permissions, stale authorization and sensitive generated output. Threat modeling and adversarial evaluation are required; this design makes no compliance certification claim.

Observability and LLMOps

Correlate gateway requests, retrieval, safety checks and provider calls without exposing prompt content. Track request success/failure; P50/P95 latency; input/output tokens; estimated cost; throttling and quota rejections; budget consumption; content-safety blocks; model distribution; and configuration-cache hit ratio.

For RAG, use a curated, permission-aware evaluation dataset to assess groundedness and relevance. Version prompts, retrieval settings, models and evaluation datasets; compare releases against accepted baselines before promotion. Track cached-token usage separately where providers expose it.

FinOps and cost attribution

Capture provider-reported input, output and cached-token usage where available. Join it to consumer, product, deployment, model version and effective pricing configuration. Avoid counting cached input twice: calculate uncached input separately from cached input and output.

Reserve estimated cost and token use before admission; reconcile with completed usage and retain an explicit “estimated” state when usage is missing. Apply budget thresholds and monthly quotas, alert owners, and support consumer/product showback and reviewed chargeback reports.

Cost per successful request includes attributed costs of unsuccessful attempts and retries divided by successful requests; show “not available” when no request succeeds. Report estimated inference cost separately from invoiced Azure cost, which also includes infrastructure, discounts and billing adjustments.

Near-real-time enforcement can overshoot under concurrency. Hard ceilings need transactional reservations and conservative admission. Review cheaper models, retrieval size and caching against quality and latency evaluations; no savings or production volumes are claimed.

Reliability and failure handling

All behavior below is designed, not validated through fault injection. Production acceptance requires explicit tests for each failure path.

Architecture decisions and trade-offs

These ADR summaries are design choices for the reference architecture. Revisit them when workload constraints change.

Implementation evidence

Available now: this architecture narrative, responsive logical diagram, synthetic configuration and decision records. These are design artifacts, not gateway implementation evidence.

Evidence status convention

• Implemented and tested: use only with reproducible tests and linked results. No gateway capability currently carries this status.
• Implemented, evidence pending: use when code exists but supporting evidence is incomplete. The existing meeting summarizer is listed this way; no gateway code was supplied.
• Designed: the gateway, identity, RAG, governance, safety, LLMOps, FinOps and failure contracts in this case study.
• Planned for production hardening: deployment and operational assurance work listed below.

Test results

No gateway lab results were supplied or found in this portfolio repository. Website build checks do not validate an Azure platform.

Limitations

• No gateway implementation, Azure deployment, load test or production adoption is evidenced here.
• The diagram omits physical network topology and service-tier-specific configuration.
• Distributed counters, streaming safety and budget reservations need implementation validation.
• Retrieval permission freshness and group membership changes can create authorization gaps.
• Content filters and injection defenses are probabilistic; human review and scoped tools remain necessary.
• Public credential verification and a downloadable resume have not been supplied.

Production-hardening roadmap

1. Build the lab: publish sanitized infrastructure, policy code and synthetic fixtures; automate positive and negative tests.
2. Validate identity and data boundaries: threat model, private endpoints, VNet integration, RBAC, residency and retention review.
3. Prove accounting: transactional reservations, concurrency tests, effective-dated pricing and Azure invoice reconciliation.
4. Operationalize: agreed SLOs, load/fault tests, disaster recovery, audit retention and incident runbooks.
5. Gate releases: reproducible RAG and safety evaluations, adversarial tests and human approval for high-risk changes.

Repository, documents and demos

A public gateway repository, downloadable architecture document and gateway demo are not available yet. This page is the current design document.

• Microsoft Learn: AI gateway capabilities in Azure API Management — platform reference; custom consumer and accounting logic still require implementation.
• Selected work and existing application demos
• Discuss the architecture with Samson on LinkedIn