Systems | Development | Analytics | API | Testing

Kuwaiti banks got two very different signals from their regulator within a few months of each other. In December 2025, the Central Bank of Kuwait (CBK) replaced its 2020 Cybersecurity Framework with something far more demanding – the Cyber and Operational Resilience Framework (CORF). Then in March 2026, CBK went the other way on capital and liquidity, temporarily easing several key ratios to give banks more breathing room. Read separately, these look like contradictory moves.

AI-powered automated test script generation is transforming how software teams approach quality assurance. By analyzing real user behavior, code changes, and system logs, these tools reduce the time and effort needed to create and maintain tests. This shift from manual scripting to AI-generated scripts helps teams keep test coverage in sync with rapid release cycles.

Every organization that adopts a cloud platform takes on a shared responsibility: the assurance that its vendors are secure, compliant, and resilient. This is where third-party risk assessments (TPRAs) come in with a systematic evaluation of a vendor's security posture, operational resilience, and compliance standards. TPRAs are a cornerstone of modern security and procurement programs because they provide structured, verifiable evidence of a vendor's security posture.

By now everyone is aware of the limitations inherent in generative AI and the AI agents that use it to complete their tasks, and the challenges involved in getting them enterprise quality. If you are planning to incorporate AI agents into your enterprise IT architecture, how are you planning to validate their quality and accuracy?

Analytics platforms are built to help organizations understand what their users are doing. Increasingly, they do much more than that: they connect behavioral data with customer context, power personalization, inform automated decisions, and provide intelligence to teams and AI systems. To do this well, analytics needs access to valuable data. That makes analytics more than a measurement layer. It makes analytics part of your security perimeter.

Analytics vendors claim their AI answers questions accurately, but almost none of them will show you how they checked. The standard move is a percentage with no denominator: "90%+ accuracy on internal benchmarks." No dataset you can download. No scoring method you can inspect. No competitor runs under the same conditions. You're asked to trust the grade without ever seeing the exam1 We ran the exam in public terms instead.

In Under the Hood with Go TLS and eBPF, I left socket tracking as an exercise for later. The example used bpf_get_current_pid_tgid() and explicitly excluded concurrent TLS operations. Capturing plaintext was enough for that post. With rustls, later arrived: I could read the HTTP payload perfectly and still attach it to the wrong TCP connection. That’s a frustratingly convincing failure. The request looks right. The response looks right. The application works.

Continuous validation is redefining performance assurance, moving beyond traditional, one-off testing. By embedding ongoing validation into CI/CD pipelines, teams can identify and address regressions as soon as they arise. This approach reduces production incidents and enables faster, more confident releases.

Apple's iOS 27 release marks another significant shift in the mobile landscape. Beyond new features for iPhone users, it signals a familiar pressure point for QA and development teams: the clock starts ticking the moment the update goes live, and your apps need to be ready. Here's why that matters: nearly 80% of iPhone users adopt the newest iOS version almost immediately. That high adoption rate leaves little room for error. If your app breaks on the iOS 27 release, your customers notice fast.

Every iOS team is about to hit a testing cycle they can't skip. And waiting until production is the expensive way to find out.