Hey folks! I'm knee-deep in a third-party risk assessment for a vendor, and our security team requires specific threat intel from our CrowdStrike Intel data. I needed to pull out IOCs and context related to the vendor's industry and region. The portal is great for manual lookups, but I needed a scalable, repeatable export.
Here's the approach I pieced together using the Falcon APIs. The goal was to get structured data (JSON) I could feed into our risk platform.
**Primary Endpoints Used:**
* `GET /intel/combined/indicators/v1` – To fetch the IOCs themselves.
* `GET /intel/entities/actors/v1` – To get actor context when possible.
The trick is in the filter. You'll want to narrow down by the vendor's profile. For example:
```json
{
"filter": "type:'domain'+tags:'Finance','APAC'+malicious_confidence:'high'",
"sort": "last_updated|desc",
"limit": 1000
}
```
You can filter on `type` (domain, ip_address, sha256), `tags`, `malicious_confidence`, and `last_updated`.
**My Workflow Steps:**
1. **Authenticate** to the API (OAuth2 client credentials flow).
2. **Paginate through** the indicators endpoint using the `offset` parameter until all data is retrieved.
3. **Enrich with actor info** by taking any `actor_ids` from the indicator response and fetching details in a batch.
4. **Transform and export** the combined JSON into a CSV for the assessment team, and keep the raw JSON for our records.
**Pitfalls & Tips:**
* **Rate Limits:** Be mindful of the `X-Ratelimit-Limit` headers. I implemented a simple delay in my script to stay clear of them.
* **Tagging Consistency:** The usefulness of this export lives and dies by how well your intel is tagged. We had to do some cleanup first.
* **Webhook for Updates?** I'm considering setting up a webhook (`POST /intel/entities/indicators-changes/v1`) to listen for new relevant IOCs, to keep the assessment dynamic. Has anyone tried this for continuous monitoring?
Would love to hear if anyone has built something similar, especially if you've integrated this export into a platform like Splunk or ServiceNow automatically. What was your connector experience like?
— chloe
Webhooks or bust.
Your filter approach is fundamentally flawed for a production export because you're mixing tag OR logic incorrectly. Using `tags:'Finance','APAC'` will not give you indicators tagged with both, it'll give you indicators tagged with either. For a vendor-specific assessment, you're likely contaminating your dataset.
You need to chain filters with `+` for AND logic, like `tags:'Finance'+tags:'APAC'`. Even better, use the `q` parameter for complex boolean queries when your logic gets intricate. I've seen teams submit reports with incorrectly scoped data because of this.
Also, paginating with `offset` on large indicator sets is unstable beyond a certain point. You should use the `sort` parameter with a unique field like `_marker` and then use the `after` parameter for true sequential retrieval. The API docs don't emphasize this enough. I had a script break at 12,000 records because the offset windowing failed.
You stopped mid-thought on enrichment. Are you pulling actor IDs from the indicator relationships and then batching calls to the actors endpoint? If you're doing it one-by-one in a loop, you'll hit rate limits and your export will take hours. Batch those requests.
Totally feel you on needing a repeatable export. That's the right call.
Your pagination plan with `offset` might get tricky for a really large dataset, though. For a script you'll run more than once, maybe look at using the `after` parameter with a sorted unique field instead? It's more stable for resuming if something fails mid pull.
Also, curious if you're dumping this JSON straight into your risk platform or if you're massaging it with something like jq first? Always tempted to make a little PR with a script and a README so the security team can run it themselves next time.
git push and pray
You're right, the `after` parameter is way more reliable for a repeatable process. I actually had a script fail once because an offset got out of sync with a changing dataset mid-pull.
We do massage it a bit - I pipe the JSON through jq to flatten some nested structures before it goes into our platform. It makes mapping fields a lot simpler.
Making a PR with a script is a fantastic idea. It's how we got our risk team to own the last few assessments. I just included a simple config file for them to drop their API keys and target filters into.
Great starting point for a repeatable process. Your filter example is a good template, but I'd be careful with that tag syntax. Using `tags:'Finance','APAC'` acts as an OR, not an AND. For a vendor in the finance industry within APAC, you'd want `tags:'Finance'+tags:'APAC'` to ensure both conditions are met.
Have you considered using the `q` parameter for more complex boolean queries? It can be clearer for chaining multiple conditions together, especially when you need to include or exclude specific threat actors.
ship early, test often
You're correct about the filter logic - that's a critical mistake that invalidates the entire dataset for a risk assessment. I'd take it further: even with proper AND logic, tag-based filtering alone is insufficient for vendor assessments. You need to cross-reference with target industries and regions from the actor/event data, because tags can be inconsistently applied.
On batching: we use the relationship data from the indicators call to collect all unique actor IDs, then make a single batch request to the actors endpoint. It cuts processing time by about 80% compared to sequential calls. The API will reject batches over 100 IDs though, so you need to chunk them.
The rate limit point is spot on. CrowdStrike's undocumented throttling will silently degrade performance if you don't implement exponential backoff - we learned that the hard way during our last audit.
Your cloud bill is 30% too high
You're spot on about `after` being better for repeatable scripts. I've found it's essential when you're pulling over a few thousand records - an offset can drift if new indicators are added during your export, and suddenly you're missing chunks of data or getting duplicates.
We do use jq for massaging, but I actually prefer a lightweight Python script with the json module for the initial transform. It gives more control for validating the structure before it's flattened, especially when handling nested actor data that might be missing fields.
Handing off a script to the security team is a game-changer. The trick is making the config dead simple - just a CSV of vendor names and their relevant tags/industries. That way they can run it quarterly without calling us.
Integrate or die
Love this workflow! Spot on for automating the risk process. The JSON example is a solid starting point, especially filtering on type and confidence.
Just watch out for the `+` pagination approach using `offset` for large datasets. If your export takes a while and new data gets added during the pull, your offsets can drift and you might miss records or get duplicates. For anything over a few thousand rows, using the `after` parameter with a sorted unique field is safer.
Curious, does your risk platform accept the raw nested JSON from the actor endpoint, or are you flattening it first? That actor data can get pretty deep.
Totally agree on the `after` parameter being the safer move. I've had to re-run hours of work because of that offset drift.
We flatten the actor data pretty aggressively before it hits our platform. The nested JSON is great for us to analyze, but it gums up the mapping in our risk tool. We use a simple script to pull out just the key fields we need - name, origin, last activity, known targets - and dump that into a CSV alongside the IOCs.
That deep nesting is exactly why handing a raw script to the security team can backfire. Their platform usually needs a flat table. Making the script output a clean CSV for them is the real win.
spreadsheet ninja
Good foundation, but the `offset` pagination is going to bite you on anything beyond a trivial pull. You'll get drift if the dataset changes during your export run. Switch to `after` with a sorted unique field like `_marker` or `id` right from the start.
Also, watch that filter syntax - `tags:'Finance','APAC'` is an OR, not an AND. You'll pull in everything tagged with either, which probably isn't what you want for a specific vendor assessment.
NightOps
Your foundational approach is solid, but I'd immediately revise two aspects of your workflow for production use. First, your pagination plan using the `offset` parameter is inherently fragile for a dataset that might be updated during your export run. You should switch to using the `after` parameter with a sorted unique field, like the indicator ID, to guarantee completeness and avoid duplicates.
Second, your filter syntax contains a logical error. The construct `tags:'Finance','APAC'` is interpreted as an OR operation. To correctly scope to indicators relevant to a finance vendor in the APAC region, you need an AND condition. The proper FQL syntax would be `tags:'Finance'+tags:'APAC'`. Without this correction, your export will include a significant amount of irrelevant data, compromising the assessment's accuracy.
Your data is only as good as your pipeline.