The Illusion of 'ReadOnlyAccess'
Connecting AI and machine learning tools to your Amazon Web Services (AWS) account often requires granting them permissions. The standard request is for an Identity and Access Management (IAM) role, frequently described as "read-only." While this sounds benign, the term "read-only" is used with significant variation across the industry, and the default AWS-managed ReadOnlyAccess policy is far from truly restrictive. For tools that analyze AWS costs, security, or configurations, this distinction is critical. Relying solely on the "read-only" label without understanding the specific permissions granted can expose your cloud environment to unnecessary risks.
When a vendor like CloudWise initiates an AWS connection, they typically deploy a CloudFormation stack. This stack contains the necessary resources and policies for the tool to function. Crucially, the CloudWise onboarding process, for instance, uses a bespoke allow-list policy defined in their cloudwise-cur-setup-template.yaml file. This custom policy is intentionally designed to be more restrictive than the broad ReadOnlyAccess policy. The difference is significant: ReadOnlyAccess grants read permissions to nearly all AWS services, which can include sensitive data and configurations that a cost analysis tool doesn't actually need to see. A bespoke policy, conversely, grants access only to the specific services and actions required for its intended function, such as reading Cost and Usage Reports (CUR) or examining EC2 instance details.
Understanding the Risks of Broad Permissions
The AWS-managed ReadOnlyAccess policy, often presented as the gold standard for read-only access, is a broad set of permissions. It allows users or roles to view virtually all resources and configurations across your AWS account. While it prevents direct modification or deletion of resources, it still provides a comprehensive view of your entire cloud infrastructure. This includes sensitive information like:
- Detailed billing and cost data.
- Security group configurations and network access lists.
- IAM user and role information, including their attached policies.
- Details of all running instances, databases, storage volumes, and their configurations.
- Serverless function code and configurations.
- Secrets stored in services like AWS Secrets Manager.
For an AI tool designed for cost optimization, for example, access to the full IAM policy of every user and role is unnecessary. The tool only needs to read cost allocation tags, CUR data, and resource pricing information. Granting it the ability to see IAM policies, network configurations, or serverless function code introduces a larger attack surface. If the AI tool itself is compromised, or if its vendor has lax security practices, an attacker could leverage these broad read permissions to gather intelligence for a more targeted attack on your AWS environment. The principle of least privilege dictates that permissions should be granted only for the specific actions and resources required for a task, and ReadOnlyAccess often violates this principle.
The 'Read-Only' Spectrum: Beyond AWS Defaults
The problem is compounded by the fact that different vendors interpret "read-only" differently. Some may indeed implement a highly specific, least-privilege policy. Others might default to the ReadOnlyAccess managed policy, believing it sufficiently safe. Still others might use a slightly modified version of ReadOnlyAccess, removing only a few obviously destructive actions. This inconsistency means that "read-only" is not a universal guarantee of safety. It is essential to scrutinize what specific permissions are being requested.
Consider a scenario where an AI tool needs to assess your AWS security posture. A truly secure implementation would grant access only to security-related services like GuardDuty findings, Security Hub compliance checks, and IAM Access Analyzer reports. It would not require read access to S3 bucket contents, RDS instance data, or the configurations of every EC2 instance. If a vendor claims "read-only" but their required IAM role includes permissions like s3:ListAllMyBuckets, ec2:DescribeInstances, and iam:ListUsers, it’s a sign that their definition of read-only is much broader than necessary for its stated purpose.
The most secure approach involves vendors creating and maintaining custom IAM policies that are explicitly tailored to the tool's functionality. This is what CloudWise advocates for with its bespoke allow-list policy. Such policies act as a surgical instrument, granting only the precise permissions needed. For instance, a cost analysis tool might only need permissions to:
- Read Cost and Usage Reports (CUR) from an S3 bucket.
- Describe EC2 instances and RDS instances to associate costs with resources.
- List and describe resources tagged for cost allocation.
- Read pricing information from AWS services.
These specific permissions are far less risky than the blanket access provided by ReadOnlyAccess. They ensure that even if the AI tool's credentials are compromised, the potential damage is significantly limited.
What You Should Do
When evaluating any AI tool that requests access to your AWS account, follow these steps:
- Ask for the specific IAM policy: Do not accept "read-only" at face value. Request the actual JSON policy document or a detailed explanation of the permissions granted.
- Verify the policy against the tool’s function: Does the tool genuinely need all the permissions it's requesting? If it's a cost tool, does it need to read Lambda function code? If it's a security tool, does it need to list every S3 bucket name?
- Prioritize custom, least-privilege policies: Favor tools that use bespoke, allow-list IAM policies over those that rely on broad AWS-managed policies like
ReadOnlyAccess. - Consider temporary credentials or scoped roles: If possible, explore options for granting short-lived credentials or roles with even more granular permissions for specific tasks.
- Regularly audit permissions: Periodically review the IAM roles and policies granted to third-party tools to ensure they are still necessary and appropriately scoped.
The convenience of AI-powered cloud management tools comes with inherent security considerations. By understanding the nuances of IAM permissions and demanding transparency from vendors, you can leverage these tools safely, ensuring that "read-only" truly means "only the necessary reads." This diligence is not just good practice; it's essential for protecting your sensitive AWS environment from potential breaches.