When building data platforms in the cloud, it is common to share data with partners, clients, or internal teams outside your own. AWS provides several mechanisms to grant secure, granular access — far beyond the simple (and risky) “make the bucket public” approach.

In this post, we will explore the main strategies for sharing data securely in Amazon S3 and compare their trade-offs so you can make informed architectural decisions.

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1. IAM User with Bucket-Scoped Permissions

The most straightforward approach is to create an IAM User and attach a policy granting access to a specific bucket or prefix.

Example of a restrictive policy:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:ListBucket",
        "s3:GetObject"
      ],
      "Resource": [
        "arn:aws:s3:::my-secure-bucket",
        "arn:aws:s3:::my-secure-bucket/*"
      ]
    }
  ]
}

Pros:

• Simple to implement.
• Easy to manage for one or two individuals.

Cons:

• Requires manual credential rotation.
• Does not scale well if you have many consumers.

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2. Cross-Account Access with IAM Roles

If the consumer has their own AWS account, the best practice is to create an IAM Role in your account and allow the external account to assume it. This avoids sharing long-term access keys and centralizes permission management.

Why it is preferred:

• No static credentials are exchanged.
• Access can be revoked by simply updating the trust policy.
• Fully auditable via CloudTrail.

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3. Pre-Signed URLs for Temporary Access

For ad-hoc sharing of individual objects, S3 Pre-Signed URLs are a quick solution. They allow anyone with the link to download (or upload) an object for a limited time.

Example using AWS CLI:

aws s3 presign s3://my-secure-bucket/report.csv --expires-in 3600

Best for:

• Sharing a single file with an expiration time.
• Quick distribution without IAM complexity.

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4. Data Governance with Lake Formation

For mature data lakes, AWS Lake Formation provides fine-grained governance on top of S3. You can grant access at the table, column, or even row level, and users can query via Athena, Redshift Spectrum, or EMR without ever touching the raw bucket.

When to use:

• Enterprise-grade data sharing.
• Regulatory requirements (PII masking, auditing).

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5. Exposing Data via a Secure API

Another approach is to avoid exposing S3 directly. You can build a serverless API (API Gateway + Lambda + S3) that returns filtered or pre-processed data.

Advantages:

• Full control over what is shared.
• Business logic can be applied before data leaves your system.

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6. Private Network Access with VPC Endpoints

For highly sensitive environments, you can restrict S3 access to specific VPC Endpoints. Consumers must connect via your private network (VPN, Direct Connect, or VPC Peering).

Use case:

• Financial services, healthcare, or compliance-sensitive workloads.
• Environments where internet exposure is not acceptable.

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Decision Framework

Option	Security	Scalability	Ideal Scenario
IAM User	High	Low	One-off programmatic access
IAM Role (Cross-Account)	High	High	Partner account integration
Pre-Signed URL	Medium	Low	Share single files temporarily
Lake Formation	Very High	Very High	Enterprise data lake governance
API Gateway + Lambda	High	Medium	Controlled, filtered data exposure
VPC Endpoint / PrivateLink	Very High	Medium	Compliance-driven private access

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Conclusion

There is no “one size fits all” solution for sharing data securely in AWS. Your choice depends on:

• Who needs access (internal user, external partner, anonymous recipient).
• What needs to be shared (single object, dataset, full data lake).
• How controlled the access must be (read-only, temporary, network-restricted).

By combining IAM policies, cross-account roles, pre-signed URLs, and modern services like Lake Formation, you can design a secure and scalable data-sharing strategy tailored to your business needs.