Operations | Monitoring | ITSM | DevOps | Cloud

Google SecOps (formerly Chronicle) is widely used for large-scale security analytics, long-term telemetry retention, and detection across diverse environments. Its Unified Data Model (UDM) enables correlation across sources and supports analytics that operate over long time horizons. To take full advantage of these capabilities, security data must arrive in a consistent and well-structured UDM format. In practice, this is rarely the case.

A repository for this article can be found here.‍ When most developers think about request tracing, they picture instrumentation hooks inside familiar libraries. This allows us to track familiar metrics we see in application performance monitoring (APM) tools such as the duration of an HTTP call or how long a database query takes. But what if you could go deeper and instrument your own Ruby code automatically, without sprinkling timing calls everywhere?

High-performance computing (HPC) clusters rely on fast, reliable shared storage so that expensive CPU cores and GPUs aren’t left idle waiting for data. When bandwidth or I/O bottlenecks emerge, your workloads can slow down as your cluster spends more time on blocked reads, metadata lookups, and sync operations than actually computing.

A newly analyzed threat, Chrysalis, is a sophisticated backdoor attributed to the Chinese APT group Lotus Blossom. The malware employs advanced evasion techniques including heavy obfuscation, API hashing, dynamic DNS resolution, custom encryption and stealthy C2 communication disguised as legitimate traffic.

The secret to hyperscaler success isn’t magic. Kentik Co-founder and CEO Avi Freedman explains how organizations can adopt the same operating principles and empower network teams to drive results that far exceeds their headcount.

The 2025 Gartner Market Guide for Event Intelligence Solutions reflects a shift in how IT operations leaders evaluate AI-driven technologies. As AI hype gives way to more practical evaluation, we are seeing a natural departure from broad promises about AI capabilities toward clearly defined use cases and outcomes.

As Event Intelligence Solutions (EIS) move from early adoption to operational necessity, many enterprises are realizing that success depends on more than selecting the right technology. For Banking and Financial Services organizations, effective adoption requires a clear strategy, disciplined execution and a strong alignment to business priorities and regulatory demands and not least, customer expectations.

It takes just one misconfigured deployment script to silently dump TBs of debug logs into a production server's/var/log directory. By the time anyone notices, the disk will be at 98% capacity, and multiple microservices would have already crashed. Incidents like these usually take hours to remediate and cost the team an entire sprint's worth of goodwill with stakeholders. This should never happen.