Identity and access
IAM/RBAC, role separation, admin controls, environment access, and least-privilege patterns for AI workflows.
Secure AI cloud platform
Build a reusable, governed AI foundation—not another isolated proof
DigiScience designs AI landing zones and platform patterns that support governed enterprise AI across Azure, AWS, and GCP with identity, networking, data controls, observability, resilience, audit, and cost governance.
What it enables
Secure internal assistants, RAG systems, document intelligence, governed agents, model services, AI observability, and controlled production rollout.
Platform building blocks
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 platform