Just caught the latest Gartner Critical Capabilities report, and I have to say I'm a bit disappointed but not entirely surprised. For a platform that handles such critical threat intelligence streams, seeing them take hits on scalability and performance for large enterprises is a recurring theme. It feels like every review cycle, this is the point that comes up.
I've been building integrations between Anomali and our internal CRM/SIEM for a couple of years now, and I've hit my own walls that echo this. The API rate limits are quite conservative, and when you're trying to sync massive IOC lists or automate threat enrichment across tens of thousands of assets, you can really feel the strain. The webhook implementation is solid for basic alerts, but trying to get a high-volume, fault-tolerant data pipeline set up requires a lot of custom middleware.
Here are a couple of specific pain points I've had to build workarounds for:
* **Bulk IOC ingestion:** The standard API endpoints really aren't built for syncing a full, large-scale threat feed in one go. I ended up building a Make scenario that does the following:
1. Fetches the raw feed from our sources.
2. Chunks it into batches of 100 (anything more and you'd hit timeouts).
3. Makes parallel API calls with deliberate pacing to avoid throttling.
4. Logs each batch success/failure to a separate database for replay capability.
* **Event-driven automation scaling:** When you set up a workflow to enrich every internal alert with Anomali intelligence, the latency can stack up if you're processing a high alert volume. I had to implement a queue system (using Redis) to decouple our alerting system from the API calls, then use a separate worker process to handle the lookups.
The core intelligence is there, but the "plumbing" feels like it's designed for more modest deployments. For those of us in larger orgs or with heavy automation needs, it adds a significant layer of custom integration work. Has anyone else run into similar scaling issues, and what have your workarounds looked like? I'm particularly curious about how you're handling real-time matching at scale.
api first
api first