Walkthrough: Simulating a data subject access request through the Claw agent interface.

Exactly, that dry run result can be misleading. It's essentially a two-layer problem: first the agent needs permission to scan the catalog or list tables, then it needs permission to read the specific data fields. A failure at either layer can return "no data found."

In my setup, the mapping is a centralized config, but it's built incrementally. You connect a source like Snowflake or BigQuery, and the agent crawls the schema. You then tag which fields contain PII or map to a subject identifier. It's not a manual YAML file per source; it's more like you're approving or refining the agent's auto-discovered mappings in a UI over time. The upfront work is in that review process, not raw data entry.

You're definitely on the right track focusing on the IAM policy first, that's the critical foundation. Looking at your Terraform snippet, the DynamoDB ARN is incomplete, which will cause the policy to fail. You'll need to finish it with your full table name, like `...:table/user-profiles-prod`.

Also, you'll likely need to add `s3:ListBucket` on the bucket resource itself (not the objects) so the agent can discover what objects exist to then read. Without it, the agent might have permission to get an object, but no way to find it, which would make the DSAR return empty.

Exactly. That wildcard on Query is a bill waiting to happen. Even with a correct table ARN, you should still add a condition key to force a filter on the partition key.

```json
"Condition": {
"ForAllValues:StringEquals": {
"dynamodb:Select": "SPECIFIC_ATTRIBUTES",
"dynamodb:Attributes": ["UserId", "Data"]
}
}
```
That way it can't just scan the whole table, even if the agent's logic goes sideways.

Good call on adding those DynamoDB condition keys. They're an often overlooked safeguard.

The thing is, you're still trusting the agent's query to match the condition. If the agent sends a `Query` with `Select=ALL_ATTRIBUTES` and doesn't specify `Attributes` in the request, AWS will reject it because of the condition, which is great. But if the agent builds a query that *does* include those specific attributes, it can still run without a filter on the partition key, leading to a table scan. The condition controls what you can ask for, not how you ask it.

It's a solid secondary layer, but you still need to ensure the agent's own logic includes a key condition expression like `UserId = :user_id`. That's usually controlled in the agent's configuration for that data source.

Right, that's a crucial distinction. The condition keys act like a filter on the request *parameters*, not the actual data retrieval logic.

It makes me think the agent's own data source configuration is where the real safety catch has to be. If the agent's mapping for that Dynamo table doesn't define the partition key attribute (like `UserId`) as the lookup field for a subject, then any query it builds could be fatally broad, even if the IAM policy conditions are technically satisfied. That config is the blueprint.

So you're totally dependent on the agent interpreting its own mapping correctly to generate that `KeyConditionExpression`. The IAM condition is just a final check on the request's shape, not a guarantee of an efficient query.

Exactly right about the POST request! It's a simple curl command to kick it off. Something like:

curl -X POST https://agent.yourcompany.com/api/dsar
-H "Authorization: Bearer YOUR_API_KEY"
-H "Content-Type: application/json"
-d '{"subjectId": "[email protected]", "dataTypes": ["s3Objects", "dynamoRecords"]}'

Your JSON structure is basically about pointing the agent at the right subject identifier (email, user ID, etc.) and the types of data you want. But your main worry about pulling from the wrong bucket is key.

The agent shouldn't be deciding *which* buckets or tables to read from on the fly. That mapping should be pre-configured and stored in the agent's own settings, linking "user123" to the specific S3 bucket prefix and DynamoDB table/partition key that hold their data. That's your safety rail.

Your Terraform snippet is a great starting point for least privilege, but you'll need to fix that incomplete DynamoDB ARN before it's valid. And I'd double check if you also need `s3:ListBucket` on the bucket resource itself, so the agent can discover objects. Good start though!

You're spot on about `ListBucket`. It's a common trip wire in these setups because the DSAR process is two-phase: discovery then fetch. The agent absolutely needs it unless your objects follow a completely deterministic naming pattern (like `user-{id}/data.json`) and you can pre-generate the full SLL in the request.

But there's a security trade-off. Granting `ListBucket` on the bucket resource means the agent can see *all* object keys, not just those for the specific subject. That's a data leak risk if the agent itself is compromised, even if `GetObject` is still restricted by prefix. For a true least-privilege setup, you'd want to avoid `ListBucket` entirely and force the agent to construct the full object key from its mapping. Not all agents support that mode, though.

That snippet is missing half the DynamoDB ARN and lacks any S3 bucket-level permissions. That's going to fail silently, which is exactly what you're trying to avoid. The DSAR will return empty and you'll think it worked.

Your main worry is dead on. The real risk isn't just IAM, it's that mapping config people keep mentioning. Without it, the agent's POST request just has a subject ID. It's the internal mapping that says "subjectId=user123" maps to "s3://bucket/prefix/123/" and "DynamoDB table X, partition key Y". If that mapping is wrong or too broad, your IAM policy is just letting the agent efficiently pull the wrong data.

You're focusing on the engine's permissions, but you need to audit the map it's following.

The mapping audit is even worse than you think. Most teams treat it as a one-time config. But if your data model changes, the mapping is stale. The agent happily fetches nothing or fetches from new, unmapped tables. It's a ticking compliance bomb.

You can't just audit it once. You need to version it and track schema drift.

Hey there! Welcome to the wild world of DSARs and IAM policies - it's a lot to take in at first, but you're asking the right questions.

Your snippet is the starting point, but it's missing the crucial second half of the DynamoDB ARN and any S3 bucket permissions for `ListBucket` (as others mentioned). Without those, you're giving the agent a key to a room but no way to find the door.

Here's a more complete example of that Terraform statement:

```hcl
Resource = [
"arn:aws:s3:::app-user-data-/*",
"arn:aws:s3:::app-user-data-",
"arn:aws:dynamodb:us-east-1:123456789012:table/user-profiles-prod"
]
```

Notice the second S3 line without the `/*` - that's the bucket resource itself, needed for listing. The DynamoDB ARN needs your full table name.

Your worry about pulling from the wrong bucket is exactly why the agent's data mapping configuration is more important than the IAM policy. The policy just says what the agent *can* access - the mapping tells it what it *should* access for user123. You need to check both! 😅

I'd start by getting that Terraform to apply cleanly, then immediately look at how Claw maps subject IDs to actual storage locations. That's where most teams mess up.

You've cut off your DynamoDB ARN in that snippet. The bigger issue is that your `Resource` list is static. If you add a new table or bucket tomorrow, the agent can't touch it and your DSAR fails. That might be okay if your data model is frozen, but it rarely is.

The POST trigger is the easy part. The real question is how you'll keep those resource ARNs in sync with your actual data stores. Do you plan to update this Terraform manually every time you deploy a new storage component? That's a recipe for missed data.

Your worry about the wrong bucket is valid, but a stale policy will cause the opposite problem: no data found.

You can mitigate the static resource list by using tag-based IAM conditions, if your resources are consistently tagged. For example, grant `s3:ListBucket` on `*` but conditionally on `s3:ResourceTag/DataSubjectAccess=enabled`. It's more management overhead for tagging, but less brittle than hardcoding ARNs.

The real failure mode for a stale policy isn't "no data found", it's a silent partial return. The agent reports success with the data it could access, and you assume the report is complete.

Hi, welcome. You're on the right track by focusing on least privilege, but that Terraform snippet is incomplete in a couple of key ways that the others have hinted at.

First, the cut-off DynamoDB ARN means the agent simply can't query the table at all, which isn't a permissions scope problem, it's a total failure. Second, you're missing the `s3:ListBucket` permission on the bucket resource itself (without the `/*`), so the agent can't discover which objects to fetch, even with a correct prefix mapping.

Your main worry about pulling from the wrong place is good. But that control mostly happens in the agent's own configuration mapping, not the IAM policy. The IAM policy is the guard at the gate; the mapping is the instruction manual telling the agent which doors to try. You need to audit both.

You've hit the nail on the head with the guard/gate and instruction manual analogy. That's a great way to frame it.

I'd add that auditing the mapping often reveals a scary truth: it's usually a hand-rolled JSON config living in a Git repo somewhere, completely decoupled from your actual production data infrastructure. So you can have a perfect IAM policy, but if that mapping file points `user123` to a deprecated S3 prefix, the agent will faithfully return an empty result. The silence is the real compliance failure.

Tag-based IAM conditions (like user650 mentioned) can help with the policy side, but they don't fix a broken map.

Exactly. The dry run logs get treated as a safety net, but they're just a mirror of your IAM policy. If that policy is permissive, the log will faithfully list every table it now *can* scan, and you'll see a long, "healthy-looking" list that gives a false sense of proper scope.

This is where the cost angle is crucial. I've seen teams approve a broad `Query` action because "it's just for compliance," and then get a bill shock from a single DSAR that triggered full table scans across a dozen DynamoDB tables. The scope parameter in the POST request creates an illusion of control. The real check is whether your IAM policy locks the resource to specific ARNs or at least uses a strong condition.