AI prompt guard

Content safety and PII detection—block prompt injection and sensitive data on Kubernetes

Use content safety and PII detection: configure agentgateway to inspect and filter LLM requests and block sensitive data like PII before it reaches AI models.

What you’ll build

In this tutorial, you will:

Set up a local Kubernetes cluster with agentgateway and an LLM backend
Configure prompt guard policies to block sensitive data
Test that requests containing SSNs and emails are rejected
Learn about built-in and custom regex patterns

Before you begin

Make sure you have the following tools installed:

Docker
kubectl
kind
Helm
An OpenAI API key (get one at platform.openai.com)

For detailed installation instructions, see the LLM Gateway tutorial.

Step 1: Create a kind cluster

kind create cluster --name agentgateway

Step 2: Install agentgateway

# Gateway API CRDs
kubectl apply --server-side -f https://github.com/kubernetes-sigs/gateway-api/releases/download/v1.5.0/standard-install.yaml

# agentgateway CRDs
helm upgrade -i --create-namespace \
  --namespace agentgateway-system \
  --version v agentgateway-crds oci://oci://cr.agentgateway.dev/charts/agentgateway/charts/agentgateway-crds

# Control plane
helm upgrade -i -n agentgateway-system agentgateway oci://oci://cr.agentgateway.dev/charts/agentgateway/charts/agentgateway \
  --version v

Step 3: Create a Gateway

kubectl apply -f- <<EOF
apiVersion: gateway.networking.k8s.io/v1
kind: Gateway
metadata:
  name: agentgateway-proxy
  namespace: agentgateway-system
spec:
  gatewayClassName: agentgateway
  listeners:
  - protocol: HTTP
    port: 80
    name: http
    allowedRoutes:
      namespaces:
        from: All
EOF

Wait for the proxy:

kubectl get deployment agentgateway-proxy -n agentgateway-system

Step 4: Set up an LLM backend

export OPENAI_API_KEY=<insert your API key>

kubectl apply -f- <<EOF
apiVersion: v1
kind: Secret
metadata:
  name: openai-secret
  namespace: agentgateway-system
type: Opaque
stringData:
  Authorization: $OPENAI_API_KEY
---
apiVersion: agentgateway.dev/v1alpha1
kind: AgentgatewayBackend
metadata:
  name: openai
  namespace: agentgateway-system
spec:
  ai:
    provider:
      openai:
        model: gpt-4.1-nano
  policies:
    auth:
      secretRef:
        name: openai-secret
    ai:
      promptGuard:
        request:
        - regex:
            action: Reject
            matches:
            - "SSN"
            - "Social Security"
          response:
            message: "Request rejected: Contains sensitive information"
        - regex:
            action: Reject
            builtins:
            - Email
          response:
            message: "Request rejected: Contains email address"
EOF

This backend configures:

An OpenAI LLM provider
A prompt guard that rejects requests containing SSN references or email addresses

Step 5: Create the HTTPRoute

kubectl apply -f- <<EOF
apiVersion: gateway.networking.k8s.io/v1
kind: HTTPRoute
metadata:
  name: openai
  namespace: agentgateway-system
spec:
  parentRefs:
    - name: agentgateway-proxy
      namespace: agentgateway-system
  rules:
    - backendRefs:
      - name: openai
        namespace: agentgateway-system
        group: agentgateway.dev
        kind: AgentgatewayBackend
EOF

Step 6: Test the prompt guard

Set up port-forwarding:

kubectl port-forward deployment/agentgateway-proxy -n agentgateway-system 8080:80 &

Test a normal request (should succeed)

curl -s http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4.1-nano",
    "messages": [{"role": "user", "content": "Hello, how are you?"}]
  }' | jq

You should receive a normal response from OpenAI.

Test with SSN mention (should be blocked)

curl -s http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4.1-nano",
    "messages": [{"role": "user", "content": "My SSN is 123-45-6789"}]
  }'

Expected response: the request is rejected before reaching the LLM.

Test with an email address (should be blocked)

curl -s http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4.1-nano",
    "messages": [{"role": "user", "content": "Contact me at [email protected]"}]
  }'

Expected response: the request is rejected before reaching the LLM.

Built-in patterns

Agentgateway includes built-in patterns for common PII types:

Pattern	Description
`Email`	Email addresses
`Phone`	Phone numbers
`SSN`	Social Security Numbers
`CreditCard`	Credit card numbers
`IPAddress`	IP addresses

Response filtering

You can also mask sensitive data in LLM responses:

ai:
  promptGuard:
    response:
    - regex:
        action: Mask
        builtins:
        - CreditCard

Cleanup

kill %1 2>/dev/null
kind delete cluster --name agentgateway

Next steps

Prompt Guards ReferenceComplete prompt guard configuration TelemetryAdd observability to your deployment LLM OverviewAll LLM gateway features

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