Prerequisites
The following requirements are met before installing Protegrity Agent v1.2.0 with PPC v1.2.0.
Infrastructure
The jumpbox is registered and prepared.For more information, refer to Configuring Authentication for Protegrity AI Team Edition.
Access to the OCI registry is available.
Protegrity Provisioned Cluster
A PPC cluster is installed and accessible.For more information, refer to Installing PPC.
Protegrity Traverse and Policy
Ensure that the Protegrity Traverse and Policy Features must be installed.
To verify the Protegrity Traverse is installed, run the following command:
kubectl get pods -n discoverx
To verify the Policy is installed, run the following command:
kubectl get pods -n pty-policy
Tools
Ensure that the following tools are available.
Configuring kubectl
Ensure that the kubectl is configured against the target cluster.
To configure kubectl on AWS, run the following command:
aws eks update-kubeconfig --name <cluster-name> --region us-east-1 --alias <cluster-name>
To configure kubectl on Microsoft Azure, run the following command:
az aks get-credentials --resource-group <resource-group> --name <cluster-name>
Verifying cluster name
Verify that the correct cluster is used before proceeding.
kubectl config current-context
Note (Azure): The PPC AKS clusters are private (
*.privatelink.<region>.azmk8s.io). After fetching credentials, confirm the API is reachable usingkubectl get nscommand.If the system hangs, then the cluster needs a private network path, such as VPN, jumpbox, or private DNS.
Cloud Vendor ConfigMap
The Helm chart uses the CLOUD_VENDOR key to determine which cloud provider-specific resources are to be deployed.
Ensure that the CLOUD_VENDOR key is present and set to the appropriate value for the target environment, such as aws or azure. If the key is missing or incorrectly configured, the chart may render resources that are not supported by the target platform, causing the installation to fail.
To verify this before installing, run the following command:
kubectl get configmap nfa-config -n default -o jsonpath='{.data.CLOUD_VENDOR}{"\n"}'
- For Azure, the
AKS:value must be"azure". - For AWS, the
AWS/EKS:value must be"aws"orempty.
The ARCHITECTURE key (amd64 | arm64, default amd64) for'
arm64 architecture uses Azure Ampere nodes in Azure and AWS Graviton nodes in AWS.
Supported Models
The Protegrity Agent requires an administrator to configure the underlying LLM models before any usage. The model configuration is provided by the organization’s administrator. Both an LLM chat model and an embeddings models must be configured.
When configuring a model, the BYOM CLI requires several mandatory configuration fields. These values must be obtained from the selected model provider and supplied during the configuration process.
The following are the models supported by Agent v1.2.0.
Supported LLM providers
| Provider | Baseline Model | Required Parameters |
|---|---|---|
openai | gpt-5.2 | provider, api_key, model |
azure-openai | gpt-5.2-chat | provider, api_key, api_endpoint, api_version, model¹ |
anthropic | claude-sonnet-4-6 | provider, api_key, model |
google-gemini | gemini-3.1-flash-lite | provider, sa_json², project_id, location, model |
¹ For azure-openai, model is the Azure deployment name.
² For google-gemini, sa_json is the full contents of the Vertex AI service-account JSON key file.
Supported Embedding providers
Embedding models are a fixed allowlist — only the exact model shown below is accepted per provider.
| Provider | Supported Model | Required Parameters |
|---|---|---|
openai | text-embedding-3-large | provider, api_key, model |
azure-openai | text-embedding-3-large | provider, api_key, api_endpoint, api_version, model¹ |
google-gemini | gemini-embedding-001 | provider, sa_json², project_id, location, model |
voyage | voyage-3 | provider, api_key, model |
¹ For azure-openai, model is the Azure deployment name.
² For google-gemini, sa_json is the full contents of the Vertex AI service-account JSON key file.
Note: Anthropic does not offer an embedding API. Use
voyage,openai, orazure-openaifor embeddings when your LLM is Anthropic.
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