SageMaker Canvas InterContainerTrafficEncryption SCP Restriction - Complete Solution Guide

#aws #sagemaker #security #compliance

Aaron Rose

Software Engineer & Technology Writer

Problem

Organizations using AWS Service Control Policies (SCPs) that require sagemaker:InterContainerTrafficEncryption to be enabled encounter failures when using SageMaker Canvas for processing jobs. Canvas does not provide a UI option to enable this parameter, causing CreateProcessingJob API calls to be blocked by the SCP, effectively preventing users from running Canvas workflows in security-compliant AWS accounts.

Clarifying the Issue

This is specifically about inter-container traffic encryption within SageMaker processing jobs, not network encryption between AWS services or data in transit to external systems. SageMaker Canvas automatically creates processing jobs behind the scenes for data preparation, feature engineering, and model training tasks, but it doesn't expose the encryption controls that developers need for compliance. The core problem is that:

• SageMaker Canvas sends CreateProcessingJob API calls without the EnableInterContainerTrafficEncryption parameter set to true
• Organizations with strict security policies enforce this parameter through SCPs to ensure compliance
• Canvas provides no user interface or configuration option to enable this encryption setting
• The restriction blocks Canvas workflows entirely, forcing users to find alternative solutions

Why It Matters

Inter-container traffic encryption is a critical security requirement for organizations operating under strict compliance frameworks such as HIPAA, PCI-DSS, SOX, or government security standards. Without the ability to enforce this encryption in Canvas, organizations face a choice between compromising their security posture or abandoning Canvas entirely. This limitation can block entire machine learning initiatives in regulated industries where data security is paramount, potentially costing organizations significant time and resources while they search for compliant alternatives.

Key Terms

• Service Control Policy (SCP) – AWS Organizations policy that sets permission boundaries across multiple AWS accounts in an organization
• InterContainerTrafficEncryption – SageMaker parameter that encrypts all communication between containers within a processing job
• SageMaker Canvas – AWS no-code machine learning service that allows business analysts to build ML models without programming
• CreateProcessingJob – SageMaker API operation that launches containerized data processing workloads
• Organizational Unit (OU) – AWS Organizations grouping mechanism used to apply policies to specific sets of accounts

Steps at a Glance

Verify the SCP restriction is causing Canvas failures
Assess organizational flexibility for policy modifications
Implement account-level workarounds if policy changes aren't possible
Configure SageMaker alternatives with encryption enabled
Establish long-term governance for Canvas usage in your organization

Detailed Steps

Step 1: Verify the SCP restriction is causing Canvas failures

First, confirm that your Canvas failures are specifically due to the encryption requirement. Access AWS CloudTrail logs to identify the exact error when Canvas attempts to create processing jobs.

Look for error patterns like this in CloudTrail:

{
  "eventName": "CreateProcessingJob",
  "errorCode": "AccessDenied", 
  "errorMessage": "Request denied by organization service control policy",
  "requestParameters": {
    "processingJobName": "canvas-processing-job-xyz"
  }
}

Locate your organization's SCP in AWS Organizations console under Policies → Service Control Policies. Search for policies containing this restriction pattern:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Deny",
      "Action": "sagemaker:CreateProcessingJob",
      "Resource": "*",
      "Condition": {
        "StringNotEquals": {
          "sagemaker:InterContainerTrafficEncryption": "true"
        }
      }
    }
  ]
}

Troubleshooting: If Canvas fails but you don't see SCP denials in CloudTrail, check for other potential issues like IAM permissions, resource limits, or network connectivity problems.

Step 2: Assess organizational flexibility for policy modifications

Work with your AWS Organization administrators to understand options for policy modification. There are several approaches depending on your organization's security requirements:

Option A: Exclude Canvas from the restriction entirely

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Deny",
      "Action": "sagemaker:CreateProcessingJob", 
      "Resource": "*",
      "Condition": {
        "StringNotEquals": {
          "sagemaker:InterContainerTrafficEncryption": "true"
        },
        "StringNotLike": {
          "aws:RequestedRegion": "canvas-*"
        }
      }
    }
  ]
}

Option B: Create Canvas-specific exemption by resource naming

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Deny",
      "Action": "sagemaker:CreateProcessingJob",
      "Resource": "*", 
      "Condition": {
        "StringNotEquals": {
          "sagemaker:InterContainerTrafficEncryption": "true"
        },
        "StringNotLike": {
          "sagemaker:ProcessingJobName": "canvas-*"
        }
      }
    }
  ]
}

Security Consideration: Discuss with your compliance team whether Canvas workloads can be exempted or if the encryption requirement is absolute for your regulatory environment.

Important Note: Service Control Policies do not affect AWS service-linked roles, which are used by some AWS services for internal operations. However, this doesn't apply to Canvas processing jobs since they use standard execution roles rather than service-linked roles.

Step 3: Implement account-level workarounds if policy changes aren't possible

If modifying the SCP isn't feasible, create organizational structure to support Canvas usage:

Create a dedicated Canvas account:

Establish a new AWS account specifically for Canvas workloads
Place this account in an Organizational Unit (OU) without the restrictive SCP
Implement alternative security controls like VPC isolation, enhanced monitoring, and data classification

Account setup commands:

# Create new OU for Canvas workloads
aws organizations create-organizational-unit \
  --parent-id r-example1234 \
  --name "Canvas-Workloads"

# Move Canvas account to unrestricted OU  
aws organizations move-account \
  --account-id 123456789012 \
  --source-parent-id ou-example-restrictive \
  --destination-parent-id ou-example-canvas

Alternative: Use cross-account data access to maintain data in compliant accounts while processing in Canvas-enabled accounts. Configure this through S3 bucket policies and cross-account IAM roles:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow", 
      "Principal": {
        "AWS": "arn:aws:iam::CANVAS-ACCOUNT:role/CanvasExecutionRole"
      },
      "Action": [
        "s3:GetObject",
        "s3:PutObject"
      ],
      "Resource": "arn:aws:s3:::compliant-data-bucket/*"
    }
  ]
}

Cross-account role in the Canvas account:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "sts:AssumeRole", 
      "Resource": "arn:aws:iam::DATA-ACCOUNT:role/DataAccessRole"
    }
  ]
}

Step 4: Configure SageMaker alternatives with encryption enabled

If Canvas must be replaced entirely, implement SageMaker Studio or Notebook instances that can explicitly set the encryption parameter:

SageMaker Studio processing job with encryption:

import boto3
from sagemaker.processing import Processor

sagemaker_session = boto3.Session().client('sagemaker')

# Configure processor with encryption enabled
processor = Processor(
    image_uri='123456789012.dkr.ecr.us-east-1.amazonaws.com/my-processor:latest',
    role='arn:aws:iam::123456789012:role/SageMakerExecutionRole',
    instance_count=1,
    instance_type='ml.m5.xlarge',
    enable_network_isolation=False,
    network_config={
        'EnableInterContainerTrafficEncryption': True
    }
)

# Run processing job with encryption
processor.run(
    inputs=[],
    outputs=[],
    job_name='encrypted-processing-job'
)

Direct API call approach:

import boto3

client = boto3.client('sagemaker')

response = client.create_processing_job(
    ProcessingJobName='compliant-processing-job',
    ProcessingResources={
        'ClusterConfig': {
            'InstanceCount': 1,
            'InstanceType': 'ml.m5.xlarge',
            'VolumeSizeInGB': 30
        }
    },
    AppSpecification={
        'ImageUri': '123456789012.dkr.ecr.us-east-1.amazonaws.com/processor:latest'
    },
    RoleArn='arn:aws:iam::123456789012:role/SageMakerRole',
    NetworkConfig={
        'EnableInterContainerTrafficEncryption': True,
        'EnableNetworkIsolation': False
    }
)

Migration Strategy: Document Canvas workflows and recreate them using SageMaker Studio's visual interface combined with code notebooks for processing components.

Step 5: Establish long-term governance for Canvas usage in your organization

Implement organizational policies and monitoring to prevent future Canvas-related compliance issues:

Governance Framework:

Create approval processes for Canvas project initiation
Establish data classification requirements for Canvas workloads
Implement monitoring for Canvas usage across accounts
Document approved Canvas use cases and restrictions

Monitoring Setup:

# CloudWatch alarm for unauthorized Canvas usage
import boto3

cloudwatch = boto3.client('cloudwatch')

cloudwatch.put_metric_alarm(
    AlarmName='UnauthorizedCanvasUsage',
    ComparisonOperator='GreaterThanThreshold',
    EvaluationPeriods=1,
    MetricName='ErrorCount',
    Namespace='AWS/SageMaker',
    Period=300,
    Statistic='Sum',
    Threshold=0.0,
    ActionsEnabled=True,
    AlarmActions=[
        'arn:aws:sns:us-east-1:123456789012:canvas-violations'
    ],
    AlarmDescription='Alert on Canvas processing job failures'
)

Future Planning: Engage with AWS support to understand Canvas roadmap for encryption parameter support, and plan migration strategies if this feature becomes available.

Conclusion

The SageMaker Canvas inter-container traffic encryption limitation requires a strategic organizational response rather than a simple technical fix. Success depends on balancing security compliance requirements with business needs for no-code machine learning capabilities.

The most effective long-term solutions typically involve either organizational policy adjustments to accommodate Canvas limitations or migration to SageMaker Studio environments that provide full control over security parameters. Organizations should evaluate their specific compliance requirements, technical capabilities, and business priorities when choosing between account segregation, policy modification, or Canvas alternatives.

Immediate Action Items:

Verify SCP restrictions through CloudTrail analysis
Assess organizational flexibility for policy changes
Implement interim workarounds based on business priorities
Plan long-term governance framework for ML platform usage

This limitation highlights the importance of evaluating managed service capabilities against organizational security requirements before committing to specific AWS ML tools in regulated environments.

Aaron Rose is a software engineer and technology writer at tech-reader.blog.

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