Just got looped into a meeting about this new "Predictive Security" module. Had to drink some bad coffee to stay awake for it. The deck was full of the usual buzzwords: "AI-driven," "proactive," "risk surface." Felt like I was being sold a crystal ball.
So, anyone actually using it in production? I'm skeptical of anything that claims to predict the future in our chaotic k8s clusters. My specific questions:
* **What's it actually looking at?** Is it just correlating existing CB alerts with vuln scan data we already have, or is there a new data source?
* **Does it integrate with the existing workflow, or is it another siloed dashboard no one will check?** If the "prediction" doesn't create a high-fidelity alert in our normal SIEM/pager chain, it's dead to me.
* **The "risk score" inflation problem:** We've seen tools that just jack up scores for everything, causing alert fatigue. Does this actually *prioritize*, or just add more noise?
Ran a quick test in our dev environment. The API spits out a JSON blob of "potential threats" that looks suspiciously like a rehash of last week's container image scans flagged as "future non-compliance." Not impressed.
```json
{
"entity": "pod/my-app-7d8bb44b5-zk2pg",
"predicted_risk": "HIGH",
"factors": [
"base_image_cve_count > 10",
"deployed_in_namespace: default"
],
"recommended_action": "Review image sourcing."
}
```
I could write a 20-line Python script to do this by querying the registry and the k8s API. Are we just paying for a fancy wrapper?
Looking for real-world stories. Did it actually predict and help you block something novel, or is it just more dashboard dressing for the CISO? My pager is loud enough already.
NightOps
Your skepticism is warranted, especially regarding the data sources. I've been through a proof-of-concept. The core inputs are indeed your existing telemetry: cloud posture data, vuln scans, and runtime alerts. The novel data source, such as it is, is a proprietary "threat intelligence feed" focused on emerging attack patterns in similar stack configurations.
The real question is the correlation logic. In my analysis, it's primarily a rules engine mapping known attack patterns against your current drift state. If your cluster's web nodes are running an old nginx version and a new CVE for that version appears in their feed, you'll get a "predictive" alert about potential exploitation. It's extrapolation, not true prediction.
Regarding your workflow concern, the integration is clunky. Out-of-the-box, it only creates incidents in its own dashboard. You must use its webhook API to push to your SIEM, and the signal quality is low. We had to build a filter to only forward events where the module's confidence score was above 85% and the resource had an external exposure. Without that, the noise was overwhelming. The risk score inflation you mentioned was our biggest issue; everything became a "high" or "critical" potential threat, rendering the categories meaningless.
Migrate slow, validate fast.
It's just marketing fluff unless the alerts are actionable and integrated.
Your dev test shows the core problem: it's repackaging old data and calling it a prediction. The "risk score" will be noise unless it's tied directly to a business metric, like projected downtime cost or clean-up effort.
Check if their API can trigger a high-severity alert in your existing paging system. If not, it's another dashboard. Save the budget for reserved instances.
cost per transaction is the only metric