Enterprise AI Governance
AI Security & Privacy
Deploying AI responsibly means choosing the right platform for your data sensitivity, compliance requirements, and infrastructure constraints. Behaim ITS guides enterprises through three proven paths for running large language models securely.
Why Governance Matters in AI Adoption
Large language models process whatever you send them. In enterprise contexts that often means business logic, customer records, intellectual property, or regulated data. Where that data goes and who can access it is not a detail to leave to the model provider's default terms.
Three questions should drive every AI platform decision: Data residency — where is inference happening and where does conversation data land? Training opt-out — can the provider use your prompts and completions to improve their models? Access control — can you enforce your existing identity, network, and audit policies around the model endpoint?
The three deployment patterns below address these questions differently, each with a distinct risk profile and operational overhead. Behaim ITS has production experience with all three and can help you match the right pattern to your workload.
Choose the deployment model that fits your compliance posture and infrastructure.
Three Paths to Secure AI
Microsoft Azure AI Foundry
Cloud-hosted, enterprise-managed LLMs with Azure security controls. Supports OpenAI GPT, Codex, and Anthropic Claude inside your Azure tenant.
Details belowAWS Bedrock
Fully managed model API inside your AWS account. Run Anthropic Claude and OpenAI GPT with no data crossing to third-party infrastructure.
Details belowKubernetes: Cloud or On-Premise
Self-hosted open-source models such as DeepSeek and Kimi on your own infrastructure. Full data sovereignty with zero external API calls.
Details belowOption 1: Cloud Hosted
Microsoft Azure AI Foundry
Azure AI Foundry (formerly Azure OpenAI Service) deploys frontier models directly inside your Azure subscription. Inference runs on Microsoft-managed hardware in the region of your choice, but your data never leaves your tenant boundary and is never used to train shared models.
- OpenAI GPT and Codex — OpenAI's frontier models for reasoning and code, deployed as Azure-managed endpoints with private networking and Azure Entra ID authentication.
- Anthropic Claude Sonnet & Opus — Available through the Azure AI Foundry model catalog, giving you Anthropic's constitutional AI approach within the same governance perimeter.
- Private endpoints and VNet integration — Lock model access to your internal network. No public internet exposure required.
- Compliance coverage — ISO 27001, SOC 2, HIPAA BAA, and GDPR out of the box, backed by Microsoft's shared responsibility model.
Behaim ITS can provision AI Foundry deployments, integrate them into your existing Azure landing zones, and connect them to your integration middleware via MCP Servers or REST adapters.
Option 2: Cloud Hosted
AWS Bedrock
Amazon Bedrock provides a fully managed API for multiple foundation models running inside your AWS account. Requests and responses stay within the AWS network; Amazon does not store or use them to improve any model.
- Anthropic Claude Sonnet & Opus — Anthropic's frontier models are first-class citizens on Bedrock, with the same safety properties and constitutional AI principles, running entirely within your AWS account.
- OpenAI GPT and Codex — Access OpenAI's GPT models for general reasoning and Codex for code generation through Bedrock's unified API, alongside Amazon's own Nova models and Meta's Llama series.
- VPC endpoints and IAM — All calls can run over AWS PrivateLink. Access is governed by standard IAM policies, integrating with your existing AWS security posture.
- Guardrails — Bedrock Guardrails lets you define content filters, PII redaction, and topic restrictions that apply before and after every model call.
- Compliance coverage — AWS Bedrock inherits Amazon's compliance portfolio: SOC 1/2/3, ISO 27001, HIPAA BAA, FedRAMP (where applicable), and GDPR. AWS maintains shared responsibility documentation for each certification.
For teams already running workloads on AWS, Bedrock is the lowest-friction route to enterprise-grade LLM access. Behaim ITS can wire Bedrock endpoints into event-driven architectures using Amazon EventBridge, Kafka, or TIBCO integration layers.
Option 3: Cloud or On-Premise
Self-Hosted on Kubernetes
When data cannot leave your own infrastructure for regulatory, contractual, or sovereignty reasons, self-hosted open-source models running on Kubernetes are the answer. The same approach works whether your cluster runs in a cloud VPC or on bare metal in your own data center.
- DeepSeek — A high-performance open-weights model family from DeepSeek AI, competitive with frontier models on coding and reasoning tasks, with fully transparent weights available for self-hosting.
- Kimi (Moonshot AI) — A capable multimodal model with strong long-context performance, suitable for document-heavy integration and analysis workloads.
- Complete data sovereignty — No prompt or completion ever leaves your environment. Zero external API calls at inference time. Air-gapped deployments are supported.
- OpenAI-compatible API surface — Tools such as vLLM or Ollama expose a standard REST interface, so existing integrations built against OpenAI or Bedrock work without code changes.
- GPU and CPU options — Quantized model variants run on CPU-only clusters for lower-throughput workloads; GPU node pools unlock full performance for production inference.
Behaim ITS has deployed self-hosted LLM stacks on cloud Kubernetes (AKS & EKS), Red Hat OpenShift, and on-premise clusters. We handle model selection, quantization, serving infrastructure, and integration with your existing security tooling.
Choosing the Right Pattern
The three patterns are not mutually exclusive. Many enterprises run cloud-hosted models for general-purpose tasks and self-hosted models for workloads involving sensitive data. A hybrid approach can also be a stepping stone: start on Azure AI Foundry or Bedrock to validate use cases quickly, then move sensitive workloads to self-hosted infrastructure as they mature.
Key factors in the decision: Data classification — does the workload touch PII, trade secrets, or regulated records? Latency and throughput — cloud APIs scale elastically; self-hosted clusters need capacity planning. Model capability — frontier models (OpenAI GPT, Anthropic Sonnet & Opus) still lead on complex reasoning; the gap with open-weight models like DeepSeek is closing fast. Cost at scale — API pricing scales with tokens; self-hosted costs are mostly fixed hardware plus operational overhead.
Behaim ITS provides architecture advisory, deployment, and ongoing support for all three patterns. We can also help you define an AI governance framework that covers data handling policies, model risk assessments, and audit logging requirements.
Discuss your AI security requirements
Whether you are evaluating your first LLM deployment or hardening an existing one, our team can help you define the right architecture for your risk profile.