Identity and access
IAM/RBAC, role separation, admin controls, environment access, and least-privilege patterns for AI workflows.
Secure AI cloud platform
Build the secure foundation enterprises need before AI scales
DigiScience designs AI landing zones and cloud platform patterns that support governed enterprise AI across Azure, AWS, and GCP with identity, networking, observability, audit, and cost controls.
What it enables
Secure internal assistants, RAG systems, document intelligence, governed agents, 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
Recommended package: Governed AI Platform
Use this when the buyer wants a secure internal AI foundation rather than a one-off chatbot or prototype.
Plan secure AI platform