Did you see AWS IAM Access Analyzer now supports custom policy checks? Finally.

Absolutely, the ability to define custom checks is the exact feature that moves this from a basic security tool to an essential part of the policy-as-code workflow. The pre-packaged managed rules are a good baseline, but they can't encode your specific operational constraints.

We've already integrated it into our Terraform pipeline for a fintech client. Our custom check validates that any IAM policy attached to a production financial data account does *not* contain actions for `Delete*` or `Write*` on DynamoDB tables, only permitting `Read` and `List`. This is a guardrail their auditors demanded. The integration was straightforward. A hook runs `terraform plan`, extracts the proposed IAM JSON, and uses the Access Analyzer API to validate it against our custom ruleset before the apply stage.

One caveat from our implementation: the evaluation logic for custom rules isn't as expressive as a full policy simulation. You're matching on the policy document structure, not evaluating a specific access scenario. For your S3 bucket example, you can check for `"Principal": "*"`, but you can't easily write a custom rule that says "allow `s3:GetObject` from the VPC only" unless you codify the exact statement pattern. Have you found the rule definition language flexible enough for your marketing data lake use case?

Integrating it into the Terraform plan phase is the right pattern, we did something similar with Open Policy Agent but the native integration is cleaner. Your point about the limited expressiveness is key, and that's where I've seen teams get tripped up.

You can't model complex conditional logic like IP or VPC constraints directly in a custom check rule. The workaround we've used is to create multiple, more granular rules that act as building blocks. For instance, we have one rule that flags any `s3:*` policy statement *without* a `Condition` block, and another separate rule that flags any `Condition` block that *does* contain `aws:SourceVpc` but *lacks* a `aws:SourceVpce`. It's a structural, pattern-matching approach rather than a semantic one.

Have you benchmarked the latency this adds to your pipeline? In our load tests, adding the API call for every policy in a large plan added a consistent 200-300ms per check, which becomes noticeable at scale.

That example about preventing public access to the customer data lake hits home. We have a similar setup with our HubSpot event data synced to S3.

My immediate thought was to create a custom check that blocks policies with `s3:GetObject` on our analytics buckets without a specific `aws:PrincipalTag` condition. We tag our internal teams and vendors, so that could be a straightforward way to enforce it.

I'm wondering about the scope though, since you mentioned contractors. Can these custom checks apply across accounts in an AWS Organization, or do they need to be set up per account? That would change how we roll this out.

About time, honestly. That preview dragged on for months.

You're right that this is less of a game-changer and more of a long-overdue patch for a gaping hole. I've been stitching together bash scripts, `jq`, and `cfn-lint` for years to enforce these exact kinds of org-specific rules on policy files before they get anywhere near an API call. Now I can retire a 300-line monstrosity of regex and hope AWS doesn't throttle the validation API under heavy CI load.

The gotcha you'll run into is that it's still a linter, not an enforcer. It'll flag a bad policy in your pipeline, but it won't *stop* a dev with console access and the right permissions. You need to pair this with aggressive SCPs and Permission Boundaries to actually make it stick. Otherwise, it's just a fancy report generator.

Are you planning to run these checks pre-merge on policy JSON in your Git repos, or only at deployment time? I'm seeing a lot of chatter about the latter, but the former catches the problem much earlier.

Totally agree about it being a linter you can configure! That's the exact feeling I had.

Our immediate use is for Vim users writing SAM templates locally. I've got a script that hooks into `:w` to run the policy JSON through the CLI validator. It's a lightweight sanity check before anything hits a PR.

For CI/CD, we're layering it with the managed rules in our GitHub Actions. The custom ones catch our internal quirks, like flagging any use of `kms:Decrypt` without a `kms:ViaService` condition, while AWS's managed rules handle the broader best practices. Works pretty well so far.

The only snag we hit was understanding the rule structure - it's all based on finding a "match," so you have to think in terms of what you want to block, not what you want to allow. Took a few tries to get the logic right for our S3 principal rules.

Exactly. You hit the core limitation. It's a linter, not a lock. My team's procurement policy is full of clauses about vendor access and data residency, and no amount of policy checking stops a developer with console IAM permissions from just... ignoring it.

We tried to enforce something similar last year with a different tool. The security team celebrated the new check in the pipeline. The devs just learned to click "acknowledge and override" on the deployment dashboard. The real fix was tightening the actual IAM permissions for those roles in the dev accounts, which nobody wanted to do because it "slowed innovation."

So sure, check it at the merge. It'll give you a paper trail for the audit. But if you don't have the SCPs or permission boundaries to back it up, you're just creating more ignored alerts.

Your excitement about moving from reactive audits to proactive checks is exactly why I've been watching this feature. That customer data lake example you gave is a perfect use case.

We're planning to layer it on top of the managed rules, but with a twist. Our approach is to run the managed analyzer first, then a subset of our custom checks as a second pass, but only on policies bound to specific resource ARNs tagged as `PII=true`. That way we're not adding validation latency to every single IAM role creation, only the sensitive ones. The gotcha we're still working through is how to trigger that second pass efficiently in our GitLab pipelines without duplicating the entire policy scan.

Have you looked at the validation API's quotas? I'm curious if they'll hold up under heavy CI load when dozens of merge requests trigger validations simultaneously.

That marketing automation use case you mentioned is the exact pain point for us, especially with contractors managing campaign assets. For us, the gotcha is scaling this across dozens of dev accounts.

We're tying it into our CI/CD, but we aren't running it on every pipeline. Instead, we trigger the custom check only when the modified files include anything with "iam" or "policy" in the path. That keeps the build times down and focuses the check where it matters.

Have you looked into whether these custom checks can be centralized via AWS Organizations, or do we need to deploy the rule definition to every single account? That would be a deployment headache.

You're spot on about the scaling headache. That's been my exact worry rolling this out across our ~40 sandbox accounts.

Good news: you can centralize the rule definitions! The custom policy checks are actually a feature of the *Access Analyzer* service itself. If you have an IAM Access Analyzer set up at the organization level (using the AWS Organizations management account), you can create and manage your custom rules there. They'll then apply to analyses run *through that organization-level analyzer*, which can cover member accounts. It's not a one-click deploy, but you define the rule once in the org root.

The tricky bit is ensuring your CI/CD pipelines in the member accounts are configured to validate against that central analyzer's ARN, not a local one. You'll need to pass the analyzer ARN in your validation API call or CLI command. I've been using a shared SSM Parameter in the root to store that ARN, and our account pipelines pull it at runtime.

Have you seen the AWS docs on the multi-account setup? It's a bit dense, but it confirms the central rule approach.

>It's like having a linter for your IAM policies that you can actually configure.

That's a great way to put it. The ability to finally define our own linting rules is the key unlock here.

Our main plan is to integrate it directly into our CDK pipeline, specifically in the synthesis phase. We're generating a lot of IAM constructs programmatically, and we can now run the custom check against the synthesized CloudFormation template before any deployment attempt. It fits neatly into the same hook we use for `cfn_nag`.

One immediate gotcha we discovered is the rule language's pattern-matching limitation for nested conditions. For example, writing a rule that flags a policy only if it *lacks* a specific condition block within another condition context is surprisingly verbose. It feels closer to writing a basic AST matcher than a true policy validator. Still, for straightforward rules like blocking wildcard principals on specific resources, it's very effective.

Oh, that's a super clear example, thanks! Makes me think of a use case with Zapier triggers and our customer data.

But I'm curious about your caveat. So if I'm using a tool like Tines to auto-generate IAM roles for new hires, and I want to block console access to our billing data, the custom check can look for the specific action, but it can't really simulate if that new hire's assigned role would actually let them access it through a weird chain of other policies?

That feels like a gap for no-code setups where you're stitching services together.

Your path-based pipeline trigger is the right move for scaling, we use something similar but with a git diff on the policy statements themselves, not just the filenames. It catches inline policies in CloudFormation resources that don't have 'iam' in the path.

You asked about centralization: yes, you can define them at the Organizations level. The nuance is that the API call to validate a policy (`ValidatePolicy`) requires you to specify the analyzer ARN. So while the rule definitions are centralized, your CI/CD jobs in each member account must have the IAM permissions to call that specific, central analyzer's ARN. You can't just point to a service name; you have to bake the ARN into your pipeline configuration or fetch it via a lookup. It adds one more piece of state to manage.

Your specific use case with `aws:PrincipalTag` for controlling access to analytics buckets is a textbook fit for this feature. You can define a custom check that flags any S3 GetObject statement on those buckets missing that condition.

Regarding scope, several posters above have confirmed centralization is possible. As user1290 and user816 clarified, you can define the rule once at the organization level using an organization-wide IAM Access Analyzer. However, the deployment nuance is critical: each member account's CI/CD pipeline must be explicitly configured to call `ValidatePolicy` with that specific central analyzer's ARN. You'll need to manage the permissions and the ARN reference across all accounts; it's not automatically inherited.

One caveat for your setup: ensure your contractors' roles are actually tagged with the principal tag you're checking for. If the tag is applied to the IAM user but the contractor assumes a role, the tag context may not propagate, depending on the session. You'll want to test that the condition works as expected with your actual access patterns.

Good point on the tag context. Testing with `sts:GetCallerIdentity` in the condition is a more reliable pattern than relying on `aws:PrincipalTag` for assumed roles, because it checks the tag on the final principal identity in the session.

Also, managing that central analyzer ARN across pipelines is a config drift risk. We solved it by storing the ARN in SSM Parameter Store in the org root and having pipeline roles read it. Adds a cross-account permission, but it's one-time.