IAM Security

Overview

Identity and access management for ML workloads.

SageMaker Execution Roles

Role that SageMaker assumes to access AWS resources.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "sagemaker.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

Common Permissions

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": ["s3:GetObject", "s3:PutObject", "s3:ListBucket"],
      "Resource": ["arn:aws:s3:::my-bucket", "arn:aws:s3:::my-bucket/*"]
    },
    {
      "Effect": "Allow",
      "Action": [
        "logs:CreateLogGroup",
        "logs:CreateLogStream",
        "logs:PutLogEvents"
      ],
      "Resource": "arn:aws:logs:*:*:*"
    },
    {
      "Effect": "Allow",
      "Action": [
        "ecr:GetAuthorizationToken",
        "ecr:BatchGetImage",
        "ecr:GetDownloadUrlForLayer"
      ],
      "Resource": "*"
    }
  ]
}

AWS KMS

Encryption at rest for ML data and models.

Creating a KMS Key

import boto3

kms = boto3.client("kms")

response = kms.create_key(
    Description="Key for ML workloads",
    KeyUsage="ENCRYPT_DECRYPT",
    Tags=[{"TagKey": "Project", "TagValue": "ML"}]
)

key_id = response["KeyMetadata"]["KeyId"]

Using KMS with SageMaker

from sagemaker.estimator import Estimator

estimator = Estimator(
    ...
    output_kms_key=kms_key_arn,
    volume_kms_key=kms_key_arn
)

AWS Secrets Manager

Store sensitive credentials securely.

import boto3
import json

secrets_client = boto3.client("secretsmanager")

# Create secret
secrets_client.create_secret(
    Name="ml/database-credentials",
    SecretString=json.dumps({
        "username": "admin",
        "password": "secret123"
    })
)

# Retrieve secret
response = secrets_client.get_secret_value(SecretId="ml/database-credentials")
credentials = json.loads(response["SecretString"])

VPC Configuration

Isolate SageMaker resources in VPC.

from sagemaker import Estimator

estimator = Estimator(
    ...
    subnets=["subnet-abc123", "subnet-def456"],
    security_group_ids=["sg-12345678"]
)

VPC Endpoints

Endpoint	Purpose
sagemaker.api	SageMaker API calls
sagemaker.runtime	Inference calls
s3	S3 access without internet

Best Practices

!!! tip "Security Best Practices" 1. Use least privilege IAM policies 2. Enable encryption at rest with KMS 3. Use VPC for network isolation 4. Store secrets in Secrets Manager 5. Enable CloudTrail for auditing 6. Use S3 bucket policies for data access control

Exam Tips

!!! warning "Key Points" - Execution role is assumed by SageMaker - KMS for encryption at rest - Secrets Manager for credentials - VPC for network isolation - VPC endpoints for private access