Could someone explain why my Architect Data Action consistently returns a 403 Forbidden error when invoking an AWS Lambda function via the AWS SDK? I am using the Python boto3 library within a custom integration endpoint. The IAM role attached to the execution context has lambda:InvokeFunction permissions, and the Lambda’s trust policy explicitly allows the role ARN. Here is the invocation snippet: client.invoke(FunctionName='my-func', InvocationType='RequestResponse', Payload=json.dumps(payload)). The error response is {'Error': {'Code': 'AccessDeniedException', 'Message': 'User: arn:aws:iam::123456789012:role/Architect-Exec-Role is not authorized to perform: lambda:InvokeFunction on resource: arn:aws:lambda:us-east-1:123456789012:function:my-func'}}. I have verified the region matches us-east-1 and the function exists. Is there a specific resource policy requirement on the Lambda side that I am missing, or does the Architect runtime assume a different identity than the configured IAM role? I have checked the CloudTrail logs and the source IP is from the Genesys Cloud infrastructure, not my local machine.
This looks like a common IAM assumption issue where the execution context isn’t correctly passing the role credentials to the boto3 client. In Genesys Cloud custom integrations, the platform doesn’t automatically inject AWS credentials. You need to explicitly handle the STS AssumeRole flow or use the platform’s native AWS integration if available. If using a custom Python script, ensure the boto3 client initializes with temporary credentials from an STS call.
Check your IAM trust policy. It must allow the specific external ID if you are using cross-account access. Here is the corrected Python snippet for assuming the role:
import boto3
sts = boto3.client('sts')
response = sts.assume_role(
RoleArn='arn:aws:iam::123456789:role/my-role',
RoleSessionName='GCInvocation',
ExternalId='your-external-id'
)
creds = response['Credentials']
client = boto3.client('lambda', aws_access_key_id=creds['AccessKeyId'], aws_secret_access_key=creds['SecretAccessKey'], aws_session_token=creds['SessionToken'])
client.invoke(FunctionName='my-func', InvocationType='RequestResponse', Payload=b'{}')
Verify the ExternalId matches exactly.
This is typically caused by the lack of explicit credential injection in the execution environment. The suggestion above correctly identifies the missing STS AssumeRole flow, but the execution often fails due to how the temporary credentials are consumed by boto3 within the Genesys Cloud Data Action runtime.
Could someone explain why my Architect Data Action consistently returns a 403 Forbidden error when invoking an AWS Lambda function via the AWS SDK?
In my distributed tracing setup for Data Actions, I encountered the same issue. The platform does not automatically mount AWS credentials. You must explicitly assume the role and pass the resulting Credentials object to the client. Here is the working pattern using Python boto3 and botocore:
import boto3
from botocore.exceptions import ClientError
def handler(event, context):
sts = boto3.client('sts')
# Assume the target role
assumed_role = sts.assume_role(
RoleArn='arn:aws:iam::123456789012:role/my-lambda-role',
RoleSessionName='GCDataActionSession'
)
creds = assumed_role['Credentials']
# Initialize Lambda client with temporary credentials
lambda_client = boto3.client(
'lambda',
aws_access_key_id=creds['AccessKeyId'],
aws_secret_access_key=creds['SecretAccessKey'],
aws_session_token=creds['SessionToken'],
region_name='us-east-1'
)
# Invoke Lambda
response = lambda_client.invoke(
FunctionName='my-func',
InvocationType='RequestResponse',
Payload=b'{"key": "value"}'
)
return {
'statusCode': 200,
'body': response['Payload'].read().decode('utf-8')
}
Ensure your IAM role has sts:AssumeRole permissions. I also inject the aws_session_token into the OpenTelemetry span attributes for auditability. If you omit the session token, the 403 persists despite valid keys.
This looks like a credential propagation issue where the temporary security tokens from the STS AssumeRole response are not being correctly bound to the boto3 client session, causing the Lambda service to reject the request due to missing or expired identity context.
import boto3
from botocore.config import Config
def invoke_lambda_with_assumed_role(role_arn, function_name):
# Initialize STS client using default credential chain (if available) or injected env vars
sts_client = boto3.client('sts', region_name='us-east-1')
# Assume the role to get temporary credentials
assumed_role = sts_client.assume_role(
RoleArn=role_arn,
RoleSessionName='GenesysCloudDataActionSession'
)
credentials = assumed_role['Credentials']
# Create a new session with the temporary credentials
session = boto3.Session(
aws_access_key_id=credentials['AccessKeyId'],
aws_secret_access_key=credentials['SecretAccessKey'],
aws_session_token=credentials['SessionToken'],
region_name='us-east-1'
)
# Initialize Lambda client with the new session
lambda_client = session.client('lambda')
try:
response = lambda_client.invoke(
FunctionName=function_name,
InvocationType='RequestResponse',
Payload=b'{}'
)
return response['Payload'].read()
except Exception as e:
raise Exception(f"Lambda invocation failed: {str(e)}")
Ensure the RoleSessionName is static or predictable to avoid IAM role explosion, and verify that the execution environment has network connectivity to the AWS STS endpoint, as Genesys Cloud Data Actions run in a restricted sandbox.
The simplest way to resolve this is to stop assuming the GC runtime handles AWS sigv4 signing, because it doesn’t, so just pass the temporary STS credentials explicitly to the boto3 client constructor like this:
client = boto3.client('lambda', aws_access_key_id=temp_creds['AccessKeyId'], aws_secret_access_key=temp_creds['SecretAccessKey'], aws_session_token=temp_creds['SessionToken'])