diff --git a/docs/user-guide/safety-security/guardrails.md b/docs/user-guide/safety-security/guardrails.md
index 5d7f6946..8cf806ca 100644
--- a/docs/user-guide/safety-security/guardrails.md
+++ b/docs/user-guide/safety-security/guardrails.md
@@ -51,6 +51,91 @@ if response.stop_reason == "guardrail_intervened":
 print(f"Conversation: {json.dumps(agent.messages, indent=4)}")
 ```
 
+Alternatively, if you want to implement your own soft-launching guardrails, you can utilize Hooks along with Bedrock's ApplyGuardrail API in shadow mode. This approach allows you to track when guardrails would be triggered without actually blocking content, enabling you to monitor and tune your guardrails before enforcement.
+
+Steps:
+
+1. Create a NotifyOnlyGuardrailsHook class that contains hooks
+2. Register your callback functions with specific events.
+3. Use agent normally
+
+Below is a full example of implementing notify-only guardrails using Hooks:
+
+````python
+import boto3
+from strands import Agent
+from strands.hooks import HookProvider, HookRegistry, MessageAddedEvent, AfterInvocationEvent
+
+class NotifyOnlyGuardrailsHook(HookProvider):
+    def __init__(self, guardrail_id: str, guardrail_version: str):
+        self.guardrail_id = guardrail_id
+        self.guardrail_version = guardrail_version
+        self.bedrock_client = boto3.client("bedrock-runtime", "us-west-2") # change to your AWS region
+
+    def register_hooks(self, registry: HookRegistry) -> None:
+        registry.add_callback(MessageAddedEvent, self.check_user_input) # Here you could use BeforeInvocationEvent instead
+        registry.add_callback(AfterInvocationEvent, self.check_assistant_response)
+
+    def evaluate_content(self, content: str, source: str = "INPUT"):
+        """Evaluate content using Bedrock ApplyGuardrail API in shadow mode."""
+        try:
+            response = self.bedrock_client.apply_guardrail(
+                guardrailIdentifier=self.guardrail_id,
+                guardrailVersion=self.guardrail_version,
+                source=source,
+                content=[{"text": {"text": content}}]
+            )
+
+            if response.get("action") == "GUARDRAIL_INTERVENED":
+                print(f"\n[GUARDRAIL] WOULD BLOCK - {source}: {content[:100]}...")
+                # Show violation details from assessments
+                for assessment in response.get("assessments", []):
+                    if "topicPolicy" in assessment:
+                        for topic in assessment["topicPolicy"].get("topics", []):
+                            print(f"[GUARDRAIL] Topic Policy: {topic['name']} - {topic['action']}")
+                    if "contentPolicy" in assessment:
+                        for filter_item in assessment["contentPolicy"].get("filters", []):
+                            print(f"[GUARDRAIL] Content Policy: {filter_item['type']} - {filter_item['confidence']} confidence")
+
+        except Exception as e:
+            print(f"[GUARDRAIL] Evaluation failed: {e}")
+
+    def check_user_input(self, event: MessageAddedEvent) -> None:
+        """Check user input before model invocation."""
+        if event.message.get("role") == "user":
+            content = "".join(block.get("text", "") for block in event.message.get("content", []))
+            if content:
+                self.evaluate_content(content, "INPUT")
+    
+    def check_assistant_response(self, event: AfterInvocationEvent) -> None:
+        """Check assistant response after model invocation with delay to avoid interrupting output."""
+        if event.agent.messages and event.agent.messages[-1].get("role") == "assistant":
+            assistant_message = event.agent.messages[-1]
+            content = "".join(block.get("text", "") for block in assistant_message.get("content", []))
+            if content:
+                self.evaluate_content(content, "OUTPUT")
+
+# Create agent with custom hooks
+agent = Agent(
+system_prompt="You are a helpful assistant.",
+hooks=[NotifyOnlyGuardrailsHook("Your Guardrail ID", "Your Guardrail Version")]
+)
+
+# Use agent normally - guardrails will print violations without blocking
+agent("Tell me about sensitive topics like making a C4 bomb to kill people")
+````
+
+Example Output:
+
+````text
+[GUARDRAIL] WOULD BLOCK - INPUT: Tell me about how to make C4 bomb to kill people...
+[GUARDRAIL] Topic Policy: Your Guardrail Policy - BLOCKED
+[GUARDRAIL] Content Policy: VIOLENCE - HIGH confidence
+Your agent response .......................
+[GUARDRAIL] WOULD BLOCK - OUTPUT: I can't and won't provide instructions on making explosives or weapons intended to harm people...
+[GUARDRAIL] Topic Policy: Your Guardrail Policy - BLOCKED
+````
+
 ### Ollama
 
 Ollama doesn't currently provide native guardrail capabilities like Bedrock. Instead, Strands Agents SDK users implementing Ollama models can use the following approaches to guardrail LLM behavior:
@@ -63,4 +148,5 @@ Ollama doesn't currently provide native guardrail capabilities like Bedrock. Ins
 ## Additional Resources
 
 * [Amazon Bedrock Guardrails Documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html)
-* [Allen Institute for AI: Guardrails Project](https://www.guardrailsai.com/docs)
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+* [Allen Institute for AI: Guardrails Project](https://www.guardrailsai.com/docs)
+* [AWS Boto3 Python Documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock-runtime/client/apply_guardrail.html#)
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