While the promise of AI continues to generate momentum, many organizations face a familiar challenge: getting AI projects beyond the prototype phase. According to Gartner, only 30% of AI initiatives make it into production, and RAND reports that up to 80% fail to deliver expected outcomes.  

The problem isn’t model quality — it’s platform readiness. To deliver AI successfully, you need more than cloud infrastructure. You need a repeatable, secure, and governed platform built for modern, data-intensive workloads. 

The Shift: From Running AI in the Cloud to Building the Cloud for AI  

Deploying AI workloads in the cloud introduces operational complexity:  

• Infrastructure must be provisioned across teams and environments.  

• Models and pipelines need secure access to sensitive data.  

• Agent-based systems require controlled permissions and execution boundaries.  

• Environments must meet enterprise security, compliance, and audit requirements. 

AI workloads aren’t traditional apps — they span services, APIs, users, and machine-to-machine communication. And the attack surface grows with each agent or integration introduced.  

Rather than solving these problems after the fact, platform teams are adopting an infrastructure-as-code approach to AI environments, treating security and scalability as part of the delivery pipeline. 

HashiCorp + Azure: Automate the AI Infrastructure Lifecycle  

Using HashiCorp and Azure, platform teams can build an automated foundation for secure and scalable AI deployments.  

1. Provision repeatable environments with Terraform and Azure Verified Modules  

Define infrastructure as code using Terraform and deploy Azure resources — including compute, networking, and storage — using Azure Verified Modules that follow Microsoft’s standards. This enables repeatable, production-grade environments with built-in compliance and best practices.  

2. Secure access with Vault  

Use Vault to centrally manage access to credentials, secrets, and sensitive data. Vault supports dynamic secrets, identity-based access, and control groups for human-in-the-loop approval — critical for managing access to LLMs, data pipelines, and prompt injection risks. 

3. Enable self-service with HCP Terraform  

Deploy with confidence using HCP Terraform to manage remote state, apply policy as code (Sentinel), and integrate infrastructure changes into CI/CD workflows. Platform teams can expose secure, reusable environments to internal AI or ML teams — without losing control. 

Scaling AI Safely: Guardrails for Agentic Workloads  

Modern AI architectures (e.g. RAG, orchestration agents, tool-using LLMs) present new operational risks: 

• Unbounded API access  

• Prompt injection and data exfiltration  

• Escalated permissions across chained systems 

With HashiCorp, platform teams can implement security guardrails early:  

• Enforce least privilege and short-lived credentials with Vault  

• Apply infrastructure policy at plan time with Sentinel  

• Create secure patterns for AI deployment via modules and registries 

Build a Platform AI Can Trust  

AI outcomes are only as reliable as the infrastructure they run on. By automating provisioning, securing access, and enforcing policy through code, platform teams can give data science and AI teams what they need — without compromising on security, scalability, or compliance.  

Resources 

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