Did you see the update to the threat intelligence feed? More false positives now.

I've been conducting a longitudinal performance and efficacy analysis of our DNS-layer security stack, which prominently features Cisco Umbrella, for the past 18 months. The recent update to the Umbrella threat intelligence feed, which I believe was rolled out incrementally over the last two reporting cycles, has introduced a statistically significant increase in false positive classifications in our environment.

Our baseline, established over the first 12 months, showed a false positive rate of approximately 0.5% on our categorized domain requests. Post-update, that rate has climbed to 2.1% over the last 30 days. This is not trivial when you're processing several million DNS queries daily across a distributed enterprise. The false positives are predominantly affecting:

* **Newly registered domains (NRDs)** associated with legitimate SaaS product rollouts and marketing campaigns.
* **Benign infrastructure domains** for CDN and cloud service providers that share IP space with known bad actors.
* **Specialized SaaS tools** in our development and analytics pipelines, which are now being flagged under "Malware" or "Command and Control" categories without clear justification.

We've had to implement extensive local domain policy exceptions, which inherently reduces the security posture the solution is meant to provide. My team's workflow now involves a daily review of the Umbrella Investigate logs to manually whitelist domains that our internal application performance monitoring (APM) and real-user monitoring (RUM) tools flag as causing service degradation.

```json
// Example of a recent false positive block from our aggregated logs
{
"timestamp": "2023-10-27T14:32:11Z",
"domain": "assets.newlegit-tool.example",
"umbrella_category": ["Malware"],
"internal_app_tag": "product-analytics-dashboard",
"user_count_affected": 245,
"action_taken": "created_local_policy_exception"
}
```

From a SaaS-benchmarking perspective, this shifts the operational cost calculus. The overhead for security team triage and the tangible impact on developer productivity must now be factored against the theoretical security gain. I am curious if other large-scale implementations are observing similar patterns.

Specifically:
* What is the nature of the false positives you're encountering, and are they concentrated in specific threat categories?
* Have you correlated this with any changes in resolution latency for non-blocked domains? We've observed a ~15ms increase in 95th percentile latency, which we're attributing to the more complex heuristics.
* What mitigation strategies, beyond local exceptions, are you employing? We are evaluating feeding our internal domain trust lists back into Umbrella via APIs, but the workflow is cumbersome.

The core question for the community is whether this represents a necessary, if painful, evolution of their machine learning models with a settling-in period, or a concerning shift in the operational integrity of the feed.

— Isabella G.