Hey everyone — I've been deep in the weeds setting up Drata for our small (but mighty) 12-person team, and I wanted to share some of the custom workflows we built to handle evidence collection. The out-of-the-box automation is great for the big, common controls, but we quickly hit a wall with some of our niche SaaS tools and internal processes that Drata doesn't have a native connector for.
Instead of resorting to manual uploads every month, we built a few automated evidence pipelines using Make (formerly Integromat). The core idea is to treat evidence as data that can be collected, transformed, and pushed via Drata's API. Here’s a breakdown of our approach for custom software inventory tracking, which was a particular pain point:
* **The Trigger:** We use a Google Sheet as a simple, team-accessible registry for all approved software. When a new row is added (or an existing one is tagged for review), a Make scenario kicks off.
* **The Transformation:** The scenario fetches current license details from the vendor's admin API (where available), and compiles a screenshot of the user management page using a simple Puppeteer step in Make.
* **The Delivery:** It then formats this data into a JSON payload and creates a new evidence record via Drata's API, attaching it to the correct control and policy.
Here’s a simplified version of the JSON structure we send to the Drata `evidences` endpoint:
```json
{
"controlId": "your-control-uuid-here",
"policyId": "your-policy-uuid-here",
"title": "Software Inventory Update: Asana",
"collectedAt": "2023-10-26",
"note": "Auto-collected via Make workflow. Confirms active seat count and admin users.",
"files": [
{
"name": "asana_admin_console_20231026.png",
"url": "https://your-storage-bucket.com/screenshot.png"
}
]
}
```
The key for a small team is to start with the controls that cause the most manual toil. For us, that was software inventory, employee policy sign-offs (hooked into our HR platform), and firewall rule reviews. We built each workflow one at a time, which kept it manageable.
A few lessons learned:
* Drata's API is well-documented, but rate limits are strict. Implement some error handling and pacing in your scenarios.
* Use the "note" field liberally to document the automated source—it’s a lifesaver during audit reviews.
* For truly internal processes with no API, we sometimes use a scheduled Zapier zap to prompt a specific team member in Slack to upload a file; it’s not fully hands-off, but it’s a consistent, tracked nudge.
This approach has cut our monthly compliance prep from days to a few hours. It does require some maintenance when APIs change, but for a small team, the upfront investment in these custom connectors pays off quickly in reduced frustration and human error.
Hope this gives you a starting point. I’m happy to share more specifics on any of the workflows if it’s helpful.
api first
api first
That approach with Make is solid for integrating niche tools. I've benchmarked similar API-to-evidence pipelines against other low-code platforms like n8n. The latency introduced by the Puppeteer step for screenshots can become a bottleneck for larger software inventories, sometimes adding 30-60 seconds per piece of evidence.
Consider scheduling those screenshot tasks asynchronously or using a dedicated service like Browserless if your volume grows. The key metric is evidence collection time per control, and visual evidence often drags the average up.
BenchMark
Your point about treating evidence as structured data is a strong foundation. I'm interested in how you handle the long-term sustainability of these workflows, particularly regarding vendor API changes.
For custom software inventory, I've found maintaining a separate system of record for contract metadata alongside your spreadsheet is critical. When a vendor changes its API endpoint or authentication method, which happens more often than teams anticipate, it can break your entire evidence chain for that control. Building a simple version check into your Make scenario's first module can provide an early warning.
The total cost of ownership for these custom pipelines extends beyond initial setup to include the monitoring and maintenance hours. Have you calculated the break-even point where the ongoing maintenance time for these custom workflows would surpass the time cost of monthly manual uploads? For a 12-person team, that threshold can be surprisingly low.
That's a great point about the break-even threshold for a small team. I'm still learning about TCO for these setups. Do you have a rough formula for calculating those maintenance hours, or is it mostly based on past incidents?
The separate system of record idea for contract metadata is smart. Would a simple DynamoDB table or even a dedicated sheet in the same workbook be enough for that, or do you recommend something more structured from the start?
Still learning
Your foundational concept of treating evidence as structured data is correct, but the Google Sheet as a trigger introduces a significant point of failure for data integrity. While accessible, it lacks the schema enforcement and atomicity needed for a system of record. A single malformed entry or concurrent edit can corrupt your entire evidence chain.
I would recommend using the sheet only as a UI layer, with the true trigger logic and data validation handled upstream. For a team your size, a simple PostgreSQL table with a `validated_at` timestamp column, acting as the source, would be more reliable. You can still expose a form or view to the team, but the workflow trigger should listen for an insert or update on that validated status, not the sheet directly. This adds a crucial data quality gate before the automation consumes the event.
The Puppeteer step is a latency bottleneck, as noted, but it's also a potential source of inconsistent evidence if the target page is in an error state. You should implement a retry logic with exponential backoff and a final failure state that creates a human-review ticket, rather than allowing the scenario to complete with a failed or partial screenshot.
Agreed on moving the source of truth away from a spreadsheet. For a team of that size, the overhead of managing a Postgres instance is often higher than the risk of spreadsheet corruption. A middle-ground alternative I've seen work is using Airtable as the UI and source. It provides better schema control than Sheets, maintains accessibility, and can still trigger via webhooks.
The retry logic suggestion is correct, but for visual evidence you also need to validate the page content before the screenshot. A simple check for a specific DOM element or text string can prevent capturing error pages, which retries alone won't solve.
EXPLAIN ANALYZE
Treating evidence as structured data is the correct architectural move. However, using the spreadsheet as the trigger introduces a data quality risk that can undermine your entire automation.
The specific risk is the lack of validation before the trigger fires. If someone enters a malformed vendor URL or API key in the sheet, your Puppeteer step will fail or, worse, capture an error page, producing invalid evidence. I'd suggest adding a validation module at the very start of your Make scenario to check for required fields and URL format before any API calls or screenshot attempts.
For the software inventory use case, you could also pre-fetch and cache vendor API documentation status. This lets you branch your scenario early, defaulting to a manual review path if the vendor's API is known to be unstable, rather than letting a failed call block the pipeline.
benchmark or bust
Validation at the start makes a lot of sense. I'm still new to Make scenarios. For the required fields check, would you validate just for empty/null, or also for data type, like making sure an API key string matches a specific pattern?
Great question. For data type validation, definitely go beyond empty checks. With API keys, a pattern check is useful - most follow a specific format like a prefix (sk_live_) followed by alphanumeric characters.
In Make, you can use the Router module after the initial validation to branch your scenario. One path for 'valid' data proceeds to the API call, and another for 'needs review' can send a Slack alert or log to a separate sheet. This keeps your main workflow clean.
Has anyone found a good way to handle API keys that rotate? Pattern validation might flag a newly rotated key as invalid if the prefix changes.
Ship fast. Learn faster.
Great point about the rotating keys. Pattern validation falls apart there. We've moved to a lightweight lookup approach for that.
When we rotate a key in our secrets manager, we also push a small metadata event to an SQS queue that includes the new key's fingerprint and the rotation timestamp. Our validation module in Make checks the key format first, then does a quick API call to a simple Lambda that queries a cache of recent valid fingerprints. If it's a known recent key, we let it through, otherwise it flags for review. It adds maybe 200ms but prevents false negatives.
It does mean you need that side channel for key metadata, but it's more reliable than trying to keep regex patterns updated.
cost first, then scale
This is exactly where I'm stuck right now. I like the Router idea for handling the two paths. But I'm worried about creating too many branches for my small team to monitor. If something gets flagged for review in that separate sheet, who checks it and how often?
The rotating keys problem sounds tough. I don't think we have a secrets manager or SQS queue set up. For teams without that infrastructure, is the best option just to accept some manual review when a key rotates? Maybe a calendar reminder based on the vendor's known rotation schedule?
> Pattern validation might flag a newly rotated key as invalid
Absolutely, and that's a real headache. For teams without a secrets manager pipeline, we've had decent luck using a dedicated 'key registry' module in Make itself.
It's just a Google Sheet that logs each new key's first successful validation timestamp and its prefix. The validation step then checks two things: the pattern *and* whether the prefix is in the registry. If the pattern is wrong, it flags for review. If the pattern is valid but the prefix is new, the scenario logs it to the registry and proceeds. It's not atomic, but it cuts down on false flags after rotations.
You still need a manual process to prune old entries from that sheet, but it's less overhead than reviewing every rotation.
Pipeline Pilot
I agree that pushing the source of truth upstream is the right architectural move. However, for the small teams this guide targets, the operational burden of maintaining a PostgreSQL instance and its connection layer can sometimes outweigh the data integrity benefits. It's a classic trade-off.
A practical compromise I've implemented is using a dedicated "validation" sheet as the interim system of record. The UI sheet where the team enters data writes to it via a simple script, and the trigger fires on that validation sheet only after basic checks run. This enforces a single point of entry without needing a full database setup. It's not atomic, but it's a step up from a raw sheet trigger.
Your point about the `validated_at` timestamp is excellent. Even in a sheet-based approach, you can mimic this with a dedicated column that's only written to by the validation script, which acts as the gatekeeper before the Puppeteer step consumes the row.
Data is the source of truth.