Add Confidential Computing Support for Document Intelligence SDK

Industry and healthcare clients processing sensitive documents (IDs, medical records, contracts) require end-to-end encryption including during AI analysis. Currently, the SDK lacks native integration with Azure Confidential Computing (DCsv3 VMs/Intel SGX). This forces developers to implement manual workarounds like:

Path: /sdk/documentintelligence/azure-ai-documentintelligence

Building custom proxy middleware on DCsv3 VMs

Handling encryption/decryption before/after Document Intelligence calls

Managing complex key rotation workflows

In enterprise deployments processing 50K+ docs/month ([AI Document Explorer](https://github.com/bibinprathap/ai-doc-explorer)), this adds 35% latency and increases infrastructure costs by 40%. More critically, it blocks adoption in GCC High/IL5+ environments where in-use data protection is mandatory.

Add a confidential_compute parameter to the DocumentIntelligenceClient initialization:

python
client = DocumentIntelligenceClient(
endpoint=ENDPOINT,
credential=CREDENTIAL,
confidential_compute=True # NEW PARAMETER
)
This should:

Auto-route processing to Intel SGX enclaves (DCsv3 VMs)

Integrate with Azure Key Vault Managed HSM for key management

Provide GCC High compliance documentation in SDK reference

Maintain backward compatibility (default=False)

Describe alternatives you've considered

Current workaround:

python
# Manual proxy implementation
from custom_confidential_proxy import SecureProxyClient

proxy = SecureProxyClient(DocumentIntelligenceClient) # Adds 35% latency

Meets compliance requirements

High maintenance (key rotation, VM scaling)

35% latency penalty

Client-side encryption:

python
encrypted_doc = encrypt(doc, KEK) # Before sending
result = client.analyze(encrypted_doc)
decrypted_result = decrypt(result) # After response
No service changes needed
Breaks document structure analysis
Incurs 2x bandwidth costs

Wait for service-side solution:
Forces insecure cleartext processing today