Systems | Development | Analytics | API | Testing

We’re at it again, bringing more incremental improvements to Kong Mesh!

In March 2024, the European Union passed the AI Act. This sweeping regulation reshapes how organizations deploy and manage AI systems. The law addresses AI risks that could affect both individuals and businesses, from hiring biases to critical infrastructure failures. Similar rules have started taking shape across the world, including several state-based regulations in the US. But the regulation is only the floor. We all share the responsibility of creating a safe, responsible AI future.

The era of generative AI is upon us, and large language models (LLMs) like Claude are demonstrating incredible potential to revolutionize how businesses operate and interact with customers. However, to truly unlock this potential, AI needs secure and standardized access to the wealth of information and services locked within enterprise systems. This is where standards such as Model Context Protocol (MCP) come in, offering a powerful way to make enterprise resources AI-consumable.

APIs need strong security. They often handle sensitive data like financial records or health information. To protect this data while meeting privacy rules (like GDPR and HIPAA), fine-grained access control (FGAC) is key. FGAC uses OAuth scopes and claims to manage API access.

When Java operations hit their time limits, they throw java.util.concurrent.TimeoutException. This checked exception appears in scenarios ranging from thread synchronization barriers that never complete to Selenium tests waiting for elements that never appear. The challenge isn't just catching these exceptions—it's designing your code to handle timing constraints intelligently.

In case you missed it, we recently made a big announcement around beta GCP support for Kong’s Dedicated Cloud Gateways (DCGWs). There’s a lot of good stuff in there, but TL;DR DCGWs now support all three of the major cloud service providers (CSPs): AWS, Azure, and GCP at a 99.95% SLA with support for over 25 regions around the globe. Being the first API management vendor to support managed gateway deployments with all three CSPs has a lot of folks excited, for obvious reasons.

Enterprise adoption of large language models (LLMs) is surging. According to Gartner, more than 80% of enterprises will have deployed generative AI (GenAI) applications or used GenAI APIs by 2026, up from just 5% in 2023. That stark increase paints a telling picture: LLMs have evolved from a fringe technology to a cornerstone of business development and productivity. But as with any new technology, competition is fierce.

Let’s take a moment to think about how far we’ve come. Remember when opening a bank account meant sitting at a branch, filling out stacks of paperwork? Or when transferring money meant writing a cheque and hoping it cleared in a few days? Now, you can do all of that and so much more, with just a few taps on your phone. So, what changed? And more importantly, what’s driving this massive shift in how we bank today? The answer lies in one word: AI in Banking.