Operations | Monitoring | ITSM | DevOps | Cloud

On March 25, 2026, the Internet Systems Consortium (ISC) released patches for three vulnerabilities in BIND 9, the most widely deployed DNS server software in the world. The headline flaw — CVE-2026-1519 — carries a CVSS score of 7.5 and is remotely exploitable with no authentication required. An attacker who controls a maliciously crafted DNS zone can trigger the vulnerability by forcing a BIND resolver to process excessive NSEC3 iterations during DNSSEC validation of an insecure delegation.

C-suite leaders are redefining how they measure digital performance. Reliability, customer experience, resilience, and cost efficiency still matter, yet these indicators only hold value when they reflect what is actually unfolding inside the environment. Digital ecosystems have reached a level of complexity where small deviations influence outcomes, and leaders increasingly recognize that traditional metrics cannot be trusted without contextual grounding.

Data centre security must meet strict compliance and risk standards, giving regulators, insurers, and clients confidence that critical data is protected. Without it, organisations risk audit failure, downtime, and reputational damage. For executives and auditors, data centre security is part of wider governance and risk management. Oversight means confirming that physical safeguards, environmental systems, and compliance frameworks are in place and can be trusted.

You spent months instrumenting your app with OpenTelemetry. The idea of ripping it out to adopt a new observability backend is not an option. Sentry's OTLP endpoint means you don't have to. In fact, two environment variables are all you need and your existing traces start showing up in Sentry's trace explorer. Sentry's OTLP support is currently in open beta. This means you can start using it today, but there are some known limitations we'll cover later.

AWS Direct Connect pricing looks simple until you’re staring at an unexpected bill. Understanding how AWS Direct Connect costs work, such as port hours, data transfer, and the charges that don’t appear on the AWS pricing page, is the first step to managing them. The model has no setup charges and no minimums, but it has enough moving parts that costs can compound quickly if you’re not watching closely.

There’s always been a bit of a communication breakdown between finance and engineering when it comes to cloud costs. Cloud costs are driven by technical factors expressed in esoteric terms, and so speaking the language of finance does not guarantee that you’ll speak the language of cloud cost. But AI is changing that. Fast. With the right AI tools, finance leaders can now ask natural-language questions about their cost data and get fast, accurate answers.

If your organization is running AI or machine learning workloads on Kubernetes, the bill is real. GPU instances are among the most expensive resources in cloud infrastructure, where a single high-end node can run $30 to $40 per hour, and a multi-day training job on a cluster can cost tens of thousands before anyone looks up from their terminal. What most engineering and FinOps teams haven’t been able to do (until now) is connect that spend to the workloads that caused it.

Whilst AI has compressed the visible stages of software delivery; requirements, validation, review and release discipline have not disappeared. They have been pushed into automation, runtime and governance. The real risk is not that the lifecycle is dead, but that organisations start acting as if accountability died with it.

"We see 10,000 critical vulnerabilities!" "We patched everything last week!" This conversation happens in enterprise IT departments every single day. Security teams present dashboards filled with red alerts. IT teams show deployment reports at 98% success. Both teams are looking at real data. Both are absolutely correct. And both are totally blind to what's actually happening across the endpoint environment. This isn't a people problem — your teams aren't incompetent.

Though AI tools have made individual developers dramatically more productive at writing code, most engineering organizations report moving only about 20% faster than before. As Honeycomb CTO Charity Majors recently wrote, "AI came for code generation first because it was the easiest problem to solve, but it was never the thing holding developers back.".