Define the access boundary
Identify the intended users, document sources, existing permissions, permitted model inputs and outputs, and the owner of each decision. Record gaps instead of assuming that all indexed content is available to everyone.
Secure AI cloud consulting
Plan secure AI access to your company’s data
Decide how an internal assistant should access approved documents while respecting each user’s permissions. DigiScience provides architecture and assessment services for AI landing zones, retrieval and cloud controls across Azure, AWS and Google Cloud.
Remote consulting for India and international teams. This page describes a consulting service; DigiScience is not selling a hosted AI platform subscription.
What it enables
Secure internal assistants, RAG systems, document intelligence, governed agents, model services, AI observability, and controlled production rollout.
Resolve one access or architecture question before a build
Use a bounded written assessment when you need to connect AI to internal knowledge but cannot yet explain which documents it may retrieve, where data will travel, or what happens when access changes.
Compare workable options
Consider an existing enterprise search or assistant product, a managed retrieval application, or a separately scoped private deployment. Compare source-permission support, data location, operational responsibility and cost before selecting services.
Receive a decision package
The written output includes a proposed data flow, an access-control responsibility map, open assumptions, options and a recommendation. The implementation brief specifies validation needed before any production build.
What we need for the first discussion
Describe one workflow, the kinds of users and documents involved, your existing cloud and identity setup, and the question you need answered. A redacted sketch is enough to start; do not send credentials or private documents through the public enquiry form.
The Solution Assessment covers one defined problem. Full platform implementation, cloud charges, penetration testing, certification and ongoing operations need separate scope and evidence. A written recommendation is not proof that an environment is secure.
Private connectivity and document permission solve different problems
A private network path controls connectivity. It does not by itself establish which employee may read a particular document. A retrieval workflow must derive permissions from a trusted identity, apply the applicable policy before restricted material reaches the model, and handle revoked or missing permissions explicitly.
For example, Microsoft’s security-filter guidance distinguishes string filtering from authentication. Its document-level access overview describes different approaches and marks preview capabilities. The choice depends on the actual source, identity integration and production requirements.
Our landing-zone blueprint shows a reference flow and a reproducible synthetic example. It illustrates decisions to test; it is not customer work or a deployed cloud security validation.
Platform building blocks
Identity and access
IAM/RBAC, role separation, admin controls, environment access, and least-privilege patterns for AI workflows.
Private networking and data paths
Secure connectivity, private endpoints where required, data classification, approved retrieval paths, and environment isolation.
Observability and cost governance
Logging, usage tracking, model behavior review, cost visibility, alerts, audit trail, and operational reporting.
Cloud services we design around
The target platform is selected based on buyer environment, data location, AI services, compliance, and operational maturity.
Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure AI Document Intelligence, Azure Monitor, Sentinel, Defender for Cloud
AWS Bedrock, SageMaker, Amazon Q, Lambda, EKS, CloudWatch, GuardDuty, Security Hub
Google Vertex AI, Google Kubernetes Engine, BigQuery, Looker, Cloud Monitoring
MLOps, LLMOps, policy-as-code, release governance, and operating documentation
Reference platform layers
Experience and agent layer
Applications, copilots, assistants, agents, APIs, user channels, tool access, and human approval workflows.
Models, orchestration and evaluation
Model gateway, prompt and agent orchestration, guardrails, evaluation sets, model selection, versioning, and policy enforcement.
Enterprise data and retrieval
Approved sources, ingestion, document processing, vector and structured retrieval, metadata, lineage, classification, and retention.
Identity, network and security
SSO, IAM/RBAC, workload identity, private endpoints, segmentation, encryption, secrets, threat protection, and environment isolation.
Operations and governance
LLMOps or MLOps, release approvals, logging, quality and safety monitoring, incidents, audit, resilience, capacity, and FinOps.
Establish the shared foundation for secure AI scale
The platform scope is tailored to the enterprise cloud, data location, regulatory needs, AI services, operating maturity, availability, and adoption roadmap.
Plan secure AI platformBuild a cost picture you can explain
Review workload ownership, billing boundaries and quality-aware cost controls.
Read the practical guide →Private AI data access: practical answers
What should a company decide before connecting AI to internal documents?
Start with one workflow and four separate decisions: who is allowed to ask, which documents each person may retrieve, where the data and model calls travel, and how access changes are revoked. A private endpoint can protect a network route, but it cannot decide whether an employee may read a particular document.
How do permissions reach retrieval?
Use a trusted identity and current policy to filter tenant, group and document access before text enters model context. Do not treat a document ID, browser filter or model-generated filter as permission.
What does DigiScience deliver first?
A bounded written assessment for one workflow: data-flow sketch, access-control responsibility map, assumptions, options, recommendation and validation plan. Implementation and ongoing operations are separately scoped.
What is still unproven?
A reference design or local fixture cannot prove your identity provider, revocation timing, index synchronization, cache isolation, network controls or production security. Those are tested against the selected environment before release.
For India and international teams: bring one redacted workflow, the user and document groups involved, the existing cloud and identity boundary, and the decision you need to make. Review the synthetic permission example or discuss the access question.