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    Home»WiFi / Internet & Networking»How AI is transforming network incident response (and where it still falls short)
    WiFi / Internet & Networking

    How AI is transforming network incident response (and where it still falls short)

    adminBy adminMay 19, 2026No Comments2 Mins Read
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    Data Center Male Administrator Using Laptop Computer. Maintenance Specialis working in Cloud Computing Facility on Cyber Security and Network Protection. Server Farm Analytics. Medium Wide
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    The biggest reason: we still can’t see most of what’s happening on our own networks.

    The visibility problem comes first

    Network operators love to talk about observability. The actual state of observability in 2026 is much less impressive than the marketing suggests. According to Broadcom’s 2026 State of Network Operations report, 95% of IT professionals report lacking visibility into network segments, especially in public cloud. Only 49% believe their network can support the bandwidth and latency that AI workloads need.

    That tracks with what I see in practice. At the scale I’ve worked, you typically have SNMP polling every few minutes, syslog arriving with variable delay and streaming telemetry on a fraction of your interfaces. Even with all of that running, the blind spots are enormous. Anything traversing a third-party network is invisible. Specific paths through ECMP fabrics are often uninstrumented. The state of a BGP decision process on a router three hops away usually require SSH and a manual look.

    Traceroute, still one of the most-used diagnostic tools, has well-known limits. It can’t see through firewalls that block ICMP. It doesn’t handle asymmetric routing well. It gives you a snapshot of a path that may have already changed by the time you read the output. Load balancing distorts the result. Muted interfaces look identical to real packet loss. None of this is news to anyone who has run a traceroute and squinted at the output.

    falls Incident Network response short transforming
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