What should be used instead of the AWS AmazonSageMakerReadOnly managed policy for least privilege?

What to use instead of the AWS managed read-only policy

Use a customer-managed policy that grants only the SageMaker read actions the job actually needs, and scope those permissions to the specific projects, notebooks, models, or other resources in use. The right replacement is narrower than a broad read-only attachment and should be tailored to the workflow, not the entire service surface.

That distinction matters because visibility is itself access. SageMaker metadata, notebooks, model artifacts, and configuration can reveal training data paths, deployment details, environment names, and other sensitive operational context that should not be broadly exposed.

How to scope SageMaker read access without overexposing the environment

Start by separating the work into distinct read needs. A support analyst may only need notebook and endpoint inspection, while an ML engineer may need visibility into model packages, hyperparameter jobs, or experiment metadata. A customer-managed policy lets you grant only the specific read actions that match the role, instead of inheriting a broad default meant for convenience.

Where possible, scope by resource ARN, project boundary, or tagging convention so the policy follows the actual operating unit. If the role must span multiple projects, prefer an explicit allow list for those projects rather than a service-wide read pattern that makes every asset discoverable.

For teams that are still defining their access model, the policy design should sit inside a broader IAM pattern. NHIMG’s AWS IAM Policies and Roles Guide is the best starting point for understanding policy evaluation, role trust, and how to express least privilege cleanly.

Why the default managed policy is usually too broad

Managed read-only access is attractive because it is fast to attach, but it often exceeds what the job needs. The practical problem is not just excess permissions in the abstract. In SageMaker, broad read access can expose model inventory, notebook contents, deployment configuration, and associated naming or metadata patterns that help an attacker map the environment.

That makes the policy a visibility control as much as an authorization control. Even when the role is read-only, the information it can enumerate may be enough to support lateral movement, social engineering, target selection, or future privilege escalation if other controls are weak.

Teams that already manage broader access governance should treat this as part of identity discipline, not a SageMaker-only tuning exercise. NHIMG’s IAM and IGA Basics helps place the decision in the context of entitlement design, review, and least privilege across people and machines.

When the job is operationally sensitive, a narrower custom policy is also the safer default for privileged workflows. NHIMG’s Privileged Access Management Guide is useful for translating the same least-privilege principle into just-in-time access, session control, and bounded privilege.

What practitioners should verify before they grant read access

You should be able to answer three questions before attaching any policy: what data the role must see, which SageMaker resources that role must touch, and whether a narrower path exists that avoids broader discovery altogether. If the answer to the first two questions is vague, the policy is probably too broad.

Confirm that the role does not need write, delete, or pass-through permissions hidden inside a convenience template. Also verify whether the role needs cross-project visibility at all, because many access patterns look operationally necessary until you test them against the actual task.

For teams designing least-privilege access at the platform layer, NHIMG’s Cloud PAM and CIEM Guide is a useful companion for right-sizing effective permissions and spotting unnecessary cloud access paths.

Risk and Threat Considerations

Broad read-only access can still create exposure because it reveals sensitive environment structure, asset naming, and operational metadata that attackers can use to plan follow-on actions. In ML platforms, the information exposed by “read” often matters as much as the data itself.

Failure mechanism: A role granted service-wide read access can enumerate more notebooks, models, jobs, or configuration than the task requires, exposing useful intelligence and weakening segregation between projects.

Impact: The result can be data leakage, easier targeting of high-value assets, and a larger blast radius if the role or credentials are later misused.

Practitioner Guidance

What to prioritise: Treat the replacement policy as a role design exercise, not a drop-in permissions swap. Start from the exact read workflow, then trim away any SageMaker action or resource scope that does not support that workflow.

What to verify: The role should be able to complete its job without exposing unrelated projects, dormant notebooks, or model metadata from other teams. If it can browse more than it can operationally use, the policy still needs reduction.

Practitioner takeaway: For SageMaker, least privilege means limiting discovery as well as execution, because broad read access can disclose enough context to create real security and operational risk even when no changes are allowed.

Related resources from NHI Mgmt Group

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• What should be used instead of the AWS AmazonBedrockReadOnly managed policy for least privilege?
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