News reaction: The 'AI-powered threat block' feature seems like fluff.

I've been conducting a detailed evaluation of SASE platforms for a prospective enterprise migration, and Perimeter 81 is naturally on the list. Their recent announcement of an "AI-powered threat block" feature immediately triggered my skepticism. In the observability and SRE space, we've seen a marked increase in vendors using "AI" as a nebulous differentiator, often with a concerning lack of substantive detail on the actual mechanics and measurable outcomes.

My primary concern stems from the complete absence of published data or methodology. In a proper threat detection system, I would expect transparency on several key points:

* **The training dataset:** What corpus of network traffic and attack vectors was used? Is it proprietary, or based on a known standard like CICIDS2017? Without this, we cannot assess potential bias or blind spots.
* **The model's operational parameters:** What is the false positive/negative rate? What is the inference latency introduced into the data path? A feature impacting security posture and performance must have these metrics quantified.
* **Observability integration:** Does the feature export granular logs, metrics, or traces regarding its decision-making process? Can I see, for example, a Prometheus metric like `p81_ai_threat_block_score` or detailed JSON logs of blocked connections with a threat confidence score? Or is it just a binary "blocked/not blocked" event?
* **Comparative baseline:** What is the performance compared to their existing, presumably signature-based or heuristic, threat detection? Is there a A/B test result showing a measurable improvement in mean time to detection (MTTD) or a reduction in false positives?

The announcement material uses phrases like "learns your network's normal behavior" and "proactively neutralizes threats," which are functionally meaningless from an engineering standpoint. In the infrastructure we manage, "normal behavior" is a dynamic and complex pattern; an AI model would require immense, continuous tuning to avoid flagging legitimate DevOps or data pipeline traffic as anomalous.

I am particularly interested in whether any community members have moved beyond the marketing and into a proof-of-concept or actual implementation of this specific feature. My questions for those who have would be:

* Have you performed any controlled testing, such as simulating exfiltration attempts or lateral movement, and measured the detection rate?
* What logging output have you observed, and can it be integrated into a central SIEM or Grafana dashboard for correlation with other security events?
* Is the "AI" component purely cloud-side, or does it involve an on-premise/agent-based model? The network latency and data privacy implications differ significantly.
* Have you observed any performance degradation or unexpected blocks since enabling it, and how did you troubleshoot without transparent logs?

Without concrete answers to these questions, the feature risks being a black box that complicates incident response and violates the core SRE principle of observability. It becomes an unknown variable during a post-mortem, which is an untenable position for any team responsible for both security and reliability.