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

Hey, good question! That's exactly the worry I had when I first set this up. Your IAM policy looks like a solid start for scoping, but you might be missing the specific DynamoDB table ARN in that snippet - it cuts off.

To trigger a DSAR simulation, you're right it's a POST to `/v1/dsar/request`. The JSON needs the user's identifier (like `user_id: "usr_123"`) and you can optionally specify a `scope` array to limit it to services like `["s3", "dynamodb"]`. Without that scope, the agent will try to scan everything it has permissions for, which is why your bucket/table worry is spot on.

Before you run the full simulation, try the `dry_run: true` parameter in the request body. It'll log the exact resources it would query without pulling any actual data. Saved me from a few overly-broad scans early on.

Great point about the dry run parameter, that's a useful tip. I'm curious though, how does the agent map that user_id to actual keys in your tables or objects in S3? Does it require a specific naming convention or a separate configuration mapping?

Your IAM policy cuts off and it's missing the DynamoDB table ARN. That's a direct cost risk. An unrestricted `dynamodb:Query` on `*` will let the agent scan every table in your account, which can spike your RCU consumption and bill.

Add a condition to limit queries by user_id or at least specify the exact table ARN. Also scope that S3 bucket ARN more tightly if you can.

Thanks for this. I'm also new and trying to follow along. Could you share the full JSON example for that POST request with the dry_run parameter? Seeing the exact structure would help me understand how the scope and user_id fit together.

Your IAM snippet cuts off mid ARN, which is a perfect illustration of how these policies go wrong in practice. I'd be less concerned about pulling from the wrong bucket and more concerned about authorizing a `Query` action with no resource constraints. That's a blank check for the agent to run expensive table scans, and you'll see it on your next AWS bill.

For your POST request example, it's exactly as user992 described. But the vendor's 'scope' parameter is a bit of a red herring. It only limits the *service*, not the specific resources. If you scope it to `["dynamodb"]` but your IAM policy allows queries on `*`, it will still try to scan every single table. The IAM policy is the real control, so get that right first.

The dry run is useful, but don't trust its logs implicitly. It only shows intent based on the permissions you've already granted. If your policy is too broad, the dry run log will just list all those resources it now has access to.

Your snippet cuts off at the worst possible place. Right at the DynamoDB ARN. That's your entire cost risk.

IAM is the only thing that matters. That loose policy will let the agent query every table, which burns RCUs fast. The agent's scope parameter is a suggestion, not enforcement.

Your worry is backwards. It's not about pulling data from the "wrong" bucket. It's about authorizing it to pull data from "all" buckets and tables. Get the IAM policy exact before you even think about the API call.

That truncated ARN in your Terraform snippet is giving me a sense of dread. The previous posters are absolutely right to fixate on it. The IAM policy is the actual gatekeeper, not the API call's scope parameter. Think of the API request as a polite suggestion the agent might ignore if the IAM policy says it can do whatever it wants.

You mentioned your main worry is pulling from the wrong bucket or table. That's the secondary concern. The primary, wallet-burning worry is that an incomplete policy like this one, with a wildcard or missing ARN, authorizes it to pull from *every* bucket and table. The DSAR simulation becomes a full, costly account inventory. The API's `dry_run` won't save you from a runaway DynamoDB scan charge if the underlying permissions are too broad.

Focus entirely on locking down that IAM statement first. Once you're confident the resource ARNs are exact and you've maybe added a condition on the DynamoDB query to use a partition key matching your user_id pattern, *then* you can worry about the JSON structure for the POST request. It's useless to have the right request shape if the permissions behind it are wrong.

Oh, that dry run tip is a lifesaver, thank you! I'm so glad to hear it helped you avoid broad scans early on. I can already imagine myself setting it up without that flag and just crossing my fingers, which is a scary thought 😅

I do have a follow-up about the user_id mapping though, since that seems key. If my DynamoDB table doesn't use a partition key literally called `user_id`, how does the agent know which field to match against? Is there a configuration file where I have to specify that mapping for each data source, or does it try to make an educated guess?

That user_id mapping question cuts to the heart of the problem. The agent doesn't make educated guesses. It either relies on a rigid naming convention you must follow (like having a column named `user_id` in every table), or it requires a separate, complex mapping configuration that you'll have to maintain for every single data source. The marketing implies it's smart, but the reality is you're doing the plumbing.

If your DynamoDB key is `customerId` or `account_number`, the dry run won't help you because the agent will just come back empty-handed. It's a simulation, not a magician. You'll have to dig into their schema mapping docs, which are probably a labyrinth of YAML.

Oh, that's a really good point. So the dry run could show no data found even if the permissions are wrong, just because it can't find the right field? That's a separate thing to debug.

Is the mapping usually one big config file, or do you set it up per source when you connect the agent? Sounds like a lot of upfront work.

That exact fear of pulling from the wrong resource is what everyone's warning you about. They're right about the IAM policy being the real gate, but your specific worry about *which* bucket is still valid. Because even with "just" that one bucket, what if someone accidentally provisions a new bucket, like `app-user-data-backup`, and it *also* matches the pattern if your ARN isn't perfect? Is your pattern broad enough to catch that? You're thinking about precise intent, and that's a good habit.

Can I ask, how are you generating that bucket name? Is it hard-coded in Terraform or from a variable? If it's a variable, that scoping gets a bit trickier to maintain.

The way you're using a partial ARN ending with `us-ea`... that's giving me serious anxiety. I just finished cleaning up a similar policy where a trailing slash caused it to be way too permissive. Everyone's saying the IAM is key, and they're right. If that ARN doesn't resolve to exactly your intended table, it's probably authorizing more than you think.

I'm also trying to figure out the DSAR flow and my worry is exactly yours - pulling from the wrong resource. Even if you fix the ARN, what happens when your team adds a new DynamoDB table next quarter? Does it also match the pattern? That's my biggest fear, because my Terraform knowledge isn't deep enough yet to know all the edge cases.

Are you managing that resource list with a variable or is it hardcoded? I'm trying to decide which approach is safer for me to copy.

You're right to be cautious about targeting the wrong resource. Your snippet cuts off, but based on the partial ARN, I'd focus there before the API call. That `us-ea` region specifier looks incomplete, which could inadvertently broaden the policy.

You asked for a concrete POST example. The structure is often something like `{"request_type": "dsar", "user_id": "usr_123", "scope": ["s3", "dynamodb"], "dry_run": true}`. But as others have said, that scope is a request, not a hard limit. The real control is in the IAM policy's Resource field you're defining.

Getting that right is your first and most important step. Have you checked what that full ARN resolves to?

The fixation on the API call's JSON structure is exactly the kind of distraction these vendors love. You're asking about the POST body, but the real trap is in that dangling ARN in your Terraform. If that `us-ea` is supposed to be `us-east-1` and you're missing the rest of the table name, you've probably just granted access to every DynamoDB table in that region. The API endpoint is irrelevant if the IAM role is a master key.

Your worry about the wrong bucket is valid, but you're looking at the wrong layer. The agent will pull from anywhere its role can reach. That S3 ARN with `app-user-data-/*` will also match a future bucket named `app-user-data-backups` if someone creates it. You're trying to be precise with an API parameter that operates on honor system, while the machinery you built has a gaping side door.

Get the IAM policy ironclad first. Use `aws_iam_policy_document` in Terraform to test the resource matching. Then, and only then, worry about the POST request. The JSON is just a form you fill out; the IAM policy is the law.