SageMaker vs Vertex AI Security Review

Most sagemaker vs vertex ai security reviews lead with the checkbox comparison: both do KMS encryption, both do IAM, both have compliance certs. That framing misses what actually breaks in production. This comparison goes deeper: the IAM misconfigurations that enable privilege escalation, the network controls that matter, and where compliance posture genuinely diverges.

Identity and Access: Where Most Incidents Start

SageMaker’s default IAM posture is the bigger footgun. The Quick Setup wizard grants AmazonSageMakerFullAccess to the execution role — a policy broad enough to create notebook instances, assume roles, and invoke Lambda, all from a single compromised credential. Palo Alto’s DSPM analysis of SageMaker documented several resulting attack chains:

• Domain hopping via notebook creation. An attacker with iam:ListRoles and sagemaker:CreateNotebookInstance can enumerate cross-domain roles, spin up a notebook bound to a more privileged role in a different SageMaker domain, and reach compute and data resources (S3, RDS, trained models) that were meant to be isolated.
• Lambda privilege escalation. The AmazonSageMakerPipelinesIntegrations policy allows creating Lambda functions with elevated IAM roles. In environments using Databricks-managed roles, this can cascade to full account takeover.
• Secrets exfiltration across domains. Unrestricted secretsmanager permissions let a compromised role retrieve any secret prefixed with AmazonSageMaker — including Databricks, Snowflake, and Salesforce connection credentials stored by Canvas.

The Cloud Security Alliance puts the root statistic starkly: 91% of organizations using SageMaker had at least one notebook instance with root access enabled.

Vertex AI’s IAM surface is narrower by default. Predefined roles (roles/aiplatform.user, roles/aiplatform.admin) are scoped to AI Platform operations, and the platform inherits Google Cloud’s organization-policy constraints. There is no equivalent of SageMakerFullAccess granted on setup. The tradeoff: Vertex AI’s IAM is tightly coupled to GCP’s project/folder/org hierarchy, so if your GCP IAM boundaries are already weak, Vertex AI inherits that weakness.

For both platforms, the remediation is the same: scope execution roles to the minimum required actions and to specific resources by ARN/resource name, enforced with SCPs (AWS) or organization-policy constraints (GCP).

# SageMaker: scope the execution role to a specific S3 prefix and KMS key
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": ["s3:GetObject", "s3:PutObject"],
            "Resource": "arn:aws:s3:::my-ml-bucket/training-data/*"
        },
        {
            "Effect": "Allow",
            "Action": ["kms:GenerateDataKey", "kms:Decrypt"],
            "Resource": "arn:aws:kms:us-east-1:123456789012:key/mrk-abc123"
        },
        {
            "Effect": "Deny",
            "Action": ["iam:CreateRole", "iam:PassRole"],
            "Resource": "*"
        }
    ]
}

AWS also provides the aws:SourceArn and aws:SourceAccount global condition keys to prevent confused-deputy attacks, where SageMaker’s service principal is abused to call other services on an attacker’s behalf. The official SageMaker documentation on confused-deputy prevention shows the exact resource-policy pattern — use it in any trust policy that lets SageMaker assume a role.

Network Isolation: VPC Controls Compared

Both platforms support running workloads inside a private VPC with no public internet egress. The implementation details differ.

SageMaker provides per-job network isolation. Setting NetworkIsolation: true on a training job or inference endpoint cuts off all outbound calls from the container, including to other AWS services like S3, enforced at the hypervisor level rather than via security-group rules. For notebook instances, the equivalent is disabling direct internet access and routing through a NAT gateway or VPC endpoints. AWS recommends enforcing this via the sagemaker:NetworkIsolation IAM condition key so it cannot be overridden at job submission time:

# SCP-style: deny training job creation without network isolation
Condition:
  StringNotEquals:
    sagemaker:NetworkIsolation: "true"
Effect: Deny
Action: sagemaker:CreateTrainingJob

VPC endpoints (PrivateLink) keep SageMaker API traffic, S3 data, and ECR image pulls on the AWS backbone — no internet gateway required.

Vertex AI uses VPC Service Controls to build a logical perimeter around GCP resources — Vertex AI, Cloud Storage, BigQuery, Artifact Registry — enforcing that API calls originate only from within it. The key property it adds over IAM alone is protection against data exfiltration by a compromised identity: even a valid credential cannot move data outside the perimeter to an attacker-controlled project. Google’s documentation notes that VPC Service Control perimeters and Firewall Rules work in addition to IAM, not as a replacement.

Private Service Connect and VPC peering handle training and serving traffic for workloads that need to stay off the public internet.

The practical difference: SageMaker’s network isolation is per-workload and opt-in at job submission. Vertex AI’s perimeter is account-wide and enforced at the organization policy layer. Neither is on by default; both require explicit configuration.

Encryption: At Rest and In Transit

Both platforms encrypt at rest and in transit by default. The operational difference is key management.

SageMaker uses AWS KMS customer-managed keys (CMKs) for notebook volumes, training storage, model artifacts in S3, and inter-container traffic. Each resource type takes a separate key ARN, so you can isolate keys by workload sensitivity — but you must supply the key at every resource creation call or tooling defaults to the AWS-managed key.

Vertex AI uses Cloud KMS CMEKs for models, datasets, and pipeline artifacts. CMEK is configured at the region level for a project, which makes it easier to enforce uniformly — one policy covers all resources in that project/region combination.

For regulated workloads, both approaches satisfy FIPS 140-2 requirements when the right key policies and algorithm selections are in place.

Compliance Posture

This is where the two platforms diverge most clearly for regulated industries.

AWS SageMaker inherits AWS’s compliance portfolio: SOC 1/2/3, ISO 27001/27017/27018, PCI DSS, HIPAA (via BAA), and FedRAMP Moderate/High (GovCloud). Workloads in GovCloud can run training and inference without data leaving the FedRAMP boundary.

Vertex AI carries Google Cloud’s certifications: SOC 1/2/3, ISO 27001/27017/27018/27701, ISO 42001 (the AI management system standard), HITRUST, PCI DSS v4.0, and FedRAMP High. The ISO 42001 certification is notable — it is specific to AI system management and has no direct AWS equivalent at the time of writing.

For healthcare workloads specifically, both require a signed BAA (AWS or Google). HIPAA compliance is not automatic on either platform.

If your workload must stay in a FedRAMP High boundary, Vertex AI’s FedRAMP High authorization (achieved in 2025) covers Vertex AI directly. AWS’s FedRAMP High coverage for SageMaker is GovCloud-scoped; standard commercial regions are Moderate.

For ML model supply-chain risks that affect both platforms equally, ML model supply chain attacks covers the threat patterns in detail. The platform-agnostic controls that sit on top of either cloud — supply-chain verification, endpoint input and output filtering, least-privilege agency and adversarial monitoring — are in secure ML model deployment best practices. For monitoring once workloads are deployed, sentryml.com covers drift detection and observability that complement the controls described here.

What Actually Gets Skipped

A few controls appear in every security checklist but rarely get implemented:

Inter-container traffic encryption. SageMaker can encrypt traffic between compute instances in a distributed training job. Most teams leave it off for the latency overhead, but on multi-tenant clusters or sensitive data the cost is worth paying.

CloudTrail / Cloud Audit Logs for ML API calls. Both platforms log control-plane operations (creating training jobs, modifying endpoints, updating artifacts) — the primary evidence after a credential compromise. Neither enables comprehensive audit logging by default; you opt in and route logs to a SIEM.

Runtime detection. Configuration scanning catches misconfigurations before deployment. It does not catch a compromised execution role launching unauthorized GPU instances (p4d, p5, g5 class) for cryptomining or model theft. Both platforms need a behavioral detection layer on top of IAM and network controls.

Caveats

The attack chains here (Palo Alto’s SageMaker DSPM analysis, CSA’s runtime findings) apply to default or quick-setup configurations. A hardened SageMaker deployment — scoped roles, network isolation enforced via SCP, VPC endpoints — is materially harder to pivot through. The point is that hardening requires deliberate action; the defaults work against you.

Vertex AI’s reduced privilege escalation surface reflects Google Cloud’s tighter coupling between service accounts and organization policies, not an inherently more secure architecture. Misconfigurations in GCP IAM bindings (overly broad roles/owner, workload identity federation misconfiguration) create equivalent risks.