Secure AI Landing Zone Blueprint
A reference design for enterprise AI access boundaries, with a readable data flow and a reproducible fictional retrieval example. Validate it against your actual cloud and identity environment.
What the asset is
Download the architecture planning PDF · Read the text planning worksheet. These planning references are separate from the executed local example below.
A blueprint covering IAM/RBAC, network isolation, data classification, AI service access, secrets handling, logging, monitoring, Sentinel/GuardDuty/Security Command Center patterns, audit trails, and cost controls.
Who it is for
Cloud leaders, CISOs, platform teams, data teams, and CTOs who need a secure foundation before AI pilots scale.
Business problem supported
AI pilots often start outside governance. This blueprint shows how to create a controlled landing zone before risk expands.
What the buyer learns
How Azure OpenAI, AWS Bedrock, Vertex AI, Kubernetes, monitoring, and security controls fit into a governed AI platform.
How DigiScience uses it
It supports platform discovery, proposal architecture, security review, and the technical baseline for AI pilots.
Cloud cost governance for AI workloads
Cloud cost optimisation should start with evidence, not a generic savings target. This planning checklist applies to Azure, AWS, and GCP when a team is preparing AI, data, or platform workloads.
Allocate and explain spend
Tag environments, products, teams, and experiments so finance and delivery leaders can trace material cost to a workload and owner.
Set budgets and guardrails
Define budget thresholds, alerts, usage limits, and approval points before deploying GPU, model API, data transfer, or always-on services.
Measure unit economics
Track cost per workflow, document, transaction, user action, or model evaluation—not only total cloud spend.
Choose the right placement
Compare managed services, private deployment, hybrid options, schedules, capacity commitments, and model choices against security, latency, resilience, and cost requirements.
This is not a claimed saving or a financial guarantee. It identifies what must be measured before a cost optimisation decision is credible.
Reference architecture and executed local example
Keep restricted documents out of an AI assistant's context
Signing in to an internal assistant does not grant access to every indexed document. A private endpoint controls a network path; it does not decide which employee may read which source. Establish the caller's identity, enforce the relevant document permissions before constructing model context, and preserve those permissions when documents become searchable chunks.
This is a reference design for one read-only knowledge workflow. The example below runs on fictional local records. It is not a deployed cloud architecture, security assessment, certification or customer result.
A readable architecture and data flow
- Verify identityThe application validates the sign-in session. Client-supplied group names are not authority.
- Resolve authorizationThe server obtains current tenant and permitted groups from a trusted policy source. Missing identity or policy fails closed.
- Retrieve within permissionsApply mandatory tenant and document access constraints. Relevant but restricted chunks must not enter the model request.
- Build bounded contextPass only authorized source text and identifiers to the model integration. Treat source instructions as untrusted.
- Return evidence for reviewUse permitted citations or state insufficient authorized evidence. Recheck access when opening source links.
Ingestion is a second path: source documents and access rules → approved ingestion → chunks with source identity, tenant and permission metadata → search index. Permission changes and deletions need synchronization, verification and a defined delay budget. A diagram alone cannot establish that this works.
Network and operations surround the flow: validate private routing, public-access settings, workload identity, secret handling, restricted logs and retention separately. Shared caches and conversation history must not let earlier access survive a later policy change.
Executed example: a document ID is not permission
The downloadable Node.js example reads a fictional policy and four invented documents. An engineering user may receive engineering-guide; requesting finance-plan by its exact ID still returns no context. A different tenant's identically named group cannot cross the tenant boundary.
| Scenario | Expected | Observed in this fixture |
|---|---|---|
| Permitted engineering / finance user | Only that group's document enters context and citations. | Matched in both allow cases. |
| Exact restricted source / different tenant | No unauthorized source text or citation. | Restricted lookup empty; tenant B received only its own source. |
| Missing identity or permissions | Deny the request or omit the inaccessible document. | Missing/unknown user denied; missing or malformed ACL granted nothing. |
| Updated policy removes access | The next call using that updated snapshot has no old access. | Group removal, deactivation and document ACL change all withheld prior context. |
| Client claims broader access | Untrusted group, tenant and filter fields cannot change policy. | Only the engineering document remained eligible. |
| Deliberately omit ACL checks in a test | The test oracle must detect unauthorized context. | Negative control detected; unsafe expression is confined to the test. |
16 fixture cases passed, plus a detected negative control. Download the expected/actual record to inspect every case. These checks exercise deterministic local context selection, not identity verification, vector search, a generative model, a live private endpoint or production security.
With Node.js 22 or later, extract the archive and run node test-access-boundary.mjs --check-recorded. It requires no credentials, packages, cloud account or network calls. All fixture content is public and fictional.
What remains to prove in a real environment
The sample assumes a verified identity and trusted, current policy. Updating a local snapshot does not prove timely cloud revocation. Validate the actual sign-in integration, chunk-level permission propagation, deleted sources, cross-user cache isolation, conversation reuse and access to citation destinations. The permitted text contains an adversarial instruction in one test, but no model runs: this does not demonstrate prompt-injection resistance. Do not deploy this sample as a security boundary.
Map the pattern to the selected cloud
Azure: AI Search security filters compare strings supplied by the application; they do not authenticate those strings. Native document permission approaches differ by source and API, with several integrations currently in preview. Verify support and synchronization before choosing. A private endpoint and disabling public network access are separate configuration decisions.
AWS: Bedrock Knowledge Bases supports explicit metadata filters, with operator support varying by store. Build mandatory access constraints from server-side authorization, as illustrated by AWS's access-control pattern; model-generated relevance filters are not an identity authority. PrivateLink and endpoint policies address network paths and service access, not this application's document rules.
Provider references checked 28 September 2026. This original example is not provider-endorsed. For one defined access-boundary question, the Solution Assessment produces written options, a recommendation and an implementation brief. Building a proof of concept, deploying controls and operating the platform need separately agreed scope. Discuss the platform boundary.