Episode 77 — Prevent public bucket mistakes by validating policies, ACLs, and inherited permissions
Public bucket mistakes rarely happen because someone intends to expose sensitive data to the world. They happen because cloud storage access is controlled through multiple overlapping permission layers, and teams misjudge how those layers combine into the effective access decision. In this episode, we start by treating public exposure as a layering problem, not as a single setting that can be flipped safely. A bucket can look private in one place and still be public because another layer grants access, or because an inherited template introduces permissions nobody noticed. The goal is to prevent public mistakes by validating policies, ACLs, and inherited permissions as one system, so the final effective permission is understood and controlled. When you do that consistently, accidental exposure becomes harder to create, easier to detect, and faster to remediate. The focus is on making public access a deliberate, time-limited exception rather than an accidental default outcome.
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To make this practical, it helps to explain the major permission layers in simple terms, because confusion about terminology is often the root cause. A bucket policy is the central rule set attached to the bucket that states who can do what to objects in that bucket, and it is often the most powerful and visible control. ACLs are object or bucket level access control entries that can grant access to specific principals, and they can introduce permissions that bypass what people think the policy is doing, especially when ACLs are used casually. Inheritance is the concept that permissions can be granted from higher levels, such as account-level defaults, organization-level controls, or standardized templates applied across multiple buckets, and those higher-level grants can affect access even if the bucket policy looks restrictive. The effective permission is the result of how these layers combine, not the result of any single layer. When teams understand that the system is layered, they stop asking which layer wins and start asking what the final access decision actually is. That shift is crucial because attackers and accidents both exploit gaps between intended and effective permissions.
One of the clearest risk indicators in any storage permission review is the presence of anonymous principals or public read or write grants. Anonymous principals are identities that represent the public, meaning anyone on the internet without authentication, and any permission granted to them should be treated as high risk by default. Public read grants allow anyone to retrieve objects, which is the most common accidental exposure pattern. Public write grants are often even more dangerous because they can allow attackers to upload malicious content, overwrite objects, or create data integrity issues that ripple into applications and downstream consumers. Even if a bucket is intended to hold public content, write access should remain tightly controlled, because public write often becomes a pathway for abuse. A disciplined review treats anonymous access as a special case that requires explicit justification, explicit scoping, and additional monitoring and guardrails. When anonymous principals appear unintentionally, the correct response is to treat it as a defect, not as a harmless convenience.
Wildcard actions are another common way public exposure and over-broad access are introduced, often by well-meaning engineers trying to avoid operational breakage. Checking for wildcard actions that unintentionally allow broad access means looking for permissions that allow wide sets of operations rather than the few operations actually needed. A wildcard can grant broad read capabilities, broad write capabilities, or even administrative actions that affect policy and access controls themselves. The risk is that a policy intended to enable a small workflow ends up enabling many other workflows, including malicious ones, because it is expressed too broadly. Wildcards also make auditing harder because it is less clear what the policy is meant to allow, which invites misinterpretation and missed review. A tighter approach uses specific actions and scopes them to specific resources and prefixes, so access is bounded by design. When you remove unintended wildcards, you reduce the chance that a change elsewhere turns a narrow permission into a broad exposure.
Inheritance validation is where many organizations are surprised because inherited permissions are often not visible in the same place where engineers review bucket settings. Validating inherited permissions from accounts, organizational units, or templates means checking whether there are higher-level grants that affect the bucket, such as a template that allows broader access to a set of resources or an account-level practice that enables public access for certain workloads. Inheritance can also include shared roles or cross-account trust patterns that allow principals from other accounts to access storage unexpectedly. The important point is that a bucket policy review that ignores inheritance can produce false confidence, because it verifies only one layer and not the effective result. In practice, inheritance validation requires understanding where templates are applied, how organization-level controls are configured, and how identity policies interact with resource policies. This is why central guardrails and consistent policy-as-code approaches are so valuable, because they make inheritance visible and intentional rather than implicit and accidental. When inherited access is validated, you are checking the actual surface area, not just the visible front panel.
Explicit deny rules can be a reliable way to block public access, especially in environments where multiple layers can accidentally add permissions. Using explicit deny rules to block public access reliably means you state that certain dangerous access patterns are not allowed, regardless of other grants, which provides a safety net against mistakes. The most common use is denying anonymous access or denying public access patterns so that even if a permissive allow statement slips in, the deny prevents the effective exposure. Deny rules should be carefully scoped to avoid blocking legitimate internal access, but they should be strict about public exposure because public exposure is high impact and frequently accidental. Explicit deny also helps with governance because it creates a clear, enforceable policy stance rather than relying on perfect review of every allow statement. In layered systems, deny rules can serve as the last line of defense that prevents a misconfiguration from turning into a real exposure. When deny is used correctly, it makes public mistakes rarer and reduces the burden on human reviewers.
It is useful to practice reviewing one bucket policy statement for public exposure risk because policy errors often hide in a single overly broad statement rather than in the overall policy shape. The review begins with identifying the principal, because if the principal is anonymous or a broad group that includes unauthenticated entities, the statement is immediately suspicious. Next, you evaluate the actions, looking for broad read or write operations that could expose or modify data beyond the intended scope. Then you evaluate the resource scope, checking whether the statement applies to the entire bucket or only to a narrow prefix or object subset. You also evaluate conditions, because well-designed conditions can restrict access by source, by identity context, or by other attributes, and missing conditions often indicate over-broad design. Finally, you consider how this statement interacts with other statements and other layers, because a safe-looking statement can become dangerous when combined with inherited permissions. Practicing this review teaches teams to think in terms of principal, actions, resources, and conditions as a repeatable mental model.
A major pitfall is believing that one control layer overrides all others, which leads to blind spots and false assurance. If teams assume the bucket policy is the only thing that matters, they may ignore ACLs that grant access broadly. If they assume ACLs are controlled, they may ignore an inherited template that introduced a permissive policy at scale. If they assume a central guardrail exists, they may stop reviewing effective permissions and miss that the guardrail is incomplete or misapplied in certain accounts. The effective permission is what matters, and the effective permission is produced by the combination of layers. The pitfall is often reinforced by user interfaces that present one layer prominently, making it feel like the authoritative control, while other layers are buried. Preventing public bucket mistakes requires discipline to check all layers consistently, especially in environments with multiple teams and multiple automation pipelines. When you treat layers as additive rather than exclusive, you make fewer assumptions and catch more mistakes.
A quick win that increases prevention is implementing automated checks that fail builds when public settings are detected. Automated checks matter because public exposure mistakes often occur during fast changes, and human review is inconsistent under time pressure. Failing builds means you prevent deployment artifacts that would create public access from being applied, which shifts the burden from after-the-fact cleanup to upfront prevention. The checks should evaluate bucket policies, ACL settings, and any configuration flags that enable public access, and they should also verify whether explicit deny rules or guardrails are present. When an automated check blocks a change, it should provide clear feedback about what triggered the block so teams can correct the configuration rather than bypass the control. This approach also creates learning, because teams quickly see which patterns are disallowed and adjust their templates accordingly. Over time, build-time enforcement reduces the volume of misconfigurations that ever reach production environments.
To make the operational challenge real, consider the scenario where a developer enables public access for quick testing. The intent is often harmless, such as making a static file reachable during development, but the action can create a real exposure if it occurs in the wrong account or on the wrong bucket. The developer may also forget to revert the change, or the change may be copied into templates that later reach production. In a layered system, they might enable public access in one layer while another layer already grants broader access, resulting in unexpectedly wide exposure. Detection might come from monitoring that flags public access changes or from unusual access logs showing unknown users reading objects. The correct response is to remove public access quickly, assess whether sensitive objects were exposed, and then address the process gap that allowed the change to persist. The scenario highlights why public access should be treated as an exception with strong guardrails rather than as a casual toggle.
When public access is genuinely required, exceptions should be designed to be time-limited, approved, and automatically expired. Requiring time-limited exceptions with approvals and automatic expiration means you force the organization to treat public exposure as a managed risk rather than as a permanent convenience. Time limits reduce the chance that a temporary testing exposure becomes a long-term leak, and approvals ensure that someone with context and accountability assesses the need and the scope. Automatic expiration is the safeguard that enforces reversal even if people forget, which is critical because forgetting is common and not malicious. Exceptions should also be narrowly scoped, applying to specific objects or prefixes rather than entire buckets, and they should include additional monitoring because public access increases probing and abuse risk. The goal is to support legitimate public use cases without allowing them to become hidden or unmanaged risks. When exceptions are structured this way, teams can move fast without leaving the doors open indefinitely.
A memory anchor that fits this topic is three locks that must all be secured. Imagine a door that has three locks, and the door is only secure if all three locks are engaged. Bucket policies are one lock, ACLs are another lock, and inherited permissions are the third lock, and leaving any one of them open can make the door effectively unlocked. The anchor helps teams remember that checking only one lock is not enough, even if that lock looks strong. Explicit deny rules are like adding a deadbolt that prevents the door from being opened to the street even if another lock is misused. Automated build checks are like a routine inspection that ensures the locks are set before the building is opened each day. Time-limited exceptions are like issuing a temporary key that expires automatically rather than leaving a permanent spare under the mat. When you keep this anchor, public access prevention becomes a simple discipline of securing all locks, not a complex debate about which layer is most important.
Before closing, it helps to summarize the operational model in a way that supports consistent practice. Public bucket prevention requires understanding the layers, including bucket policy, ACLs, and inherited permissions, and evaluating how they combine into effective access. Reviews should check for anonymous principals, public read or write grants, and wildcard actions that unintentionally broaden access. Inherited permissions must be validated because templates and higher-level controls can introduce access that is not visible in the bucket policy alone. Explicit deny rules provide reliable blocking against accidental public exposure and serve as a safety net against layered mistakes. Automation that fails builds on public settings shifts the program toward prevention and reduces reliance on human memory. Exceptions should be time-limited, approved, and automatically expired so public access remains managed and temporary where possible. When these elements work together, public bucket mistakes become rare and quickly corrected rather than common and persistent.
To conclude, run a policy review on your top bucket today and treat the outcome as a measurable security check, not a vague reassurance. Identify how bucket policies, ACLs, and inherited permissions combine for that bucket, and verify there are no anonymous principals or unintended public grants. Look for wildcard actions and overly broad resource scopes that could turn a narrow intent into broad exposure. Confirm that explicit deny controls or centralized guardrails exist to prevent accidental public access even if someone makes a mistake later. If a legitimate public exception is required, ensure it is narrowly scoped, approved, and automatically expires so exposure does not linger. When you can confidently state that your most important bucket is protected across all three locks, you reduce one of the most common and costly cloud misconfiguration risks.
