NYEX AI Solving a Real DNS/TLS Incident
It started as the kind of production issue every team dreads: users of the client’s Health Certificate and Sanitary Permit system were reporting login failures, but only from certain networks. Internal teams tested the same login flow and it worked. Support escalated. Infra checked dashboards. App teams reviewed recent changes. Nothing looked obviously broken.
What made it worse was the error pattern. Some affected users saw certificate-name mismatch behaviour, which hinted at DNS/TLS path issues. But because the issue was intermittent and location-dependent, every team had partial evidence and no shared conclusion. This is where NYEX AI was brought in, not to guess a fix, but to run a structured diagnostic process end to end.
NYEX AI began by framing the scope clearly: this was not “the whole app is down,” but a path-specific failure in the login chain. It then assembled baseline truth across layers: expected API hostname, certificate bindings, DNS resolution behaviour, and cloud edge configuration.
That baseline became the anchor point for every next step.
From there, NYEX AI correlated evidence from multiple sources that were previously reviewed in isolation: browser error screenshots, `nslookup` outputs from affected networks, TLS certificate checks, endpoint configuration in the app, and cloud metadata. Instead of letting teams debate assumptions, NYEX AI turned the incident into ranked hypotheses with confidence levels.
The key value emerged here: NYEX AI separated two likely fault domains without forcing risky production changes. One branch focused on possible cloud-side hostname/certificate alignment issues. The other focused on network-local path conditions such as resolver differences, proxy behaviour, or SSL inspection. This narrowed the investigation from “everything is possible” to “these specific paths are most probable.”
Next, NYEX AI generated an operator-ready validation checklist for affected networks. It specified what to test, in what order, and what result would confirm or reject each hypothesis. That gave support and infra teams a common playbook, reduced escalation loops, and created evidence quality that leadership could trust.
With verified findings, NYEX AI produced a risk-rated mitigation plan: immediate low-risk containment actions, short-term stabilization, and longer-term hardening (including canonical API hostname strategy). This approach protected production while still moving quickly toward resolution.
By the end of the incident, the organization had more than a fix path. It had a repeatable diagnostic model. The business impact was clear: lower MTTR, fewer engineer-hours lost in cross-team back-and-forth, fewer escalation handoffs, and better control over repeat incidents.
NYEX AI turned an ambiguous “works for some, fails for others” outage into an evidence-driven, low-risk, executable response that both technical and non-technical stakeholders could follow from start to finish.
