Systems | Development | Analytics | API | Testing

The Rise of the Open Security Lake: Why CISOs Are Betting on Open Table Formats

As we head into the RSA Conference this year, the conversation on the show floor is going to be different. Yes, artificial intelligence (AI) will be everywhere. But if you listen closely to the C-suite discussions happening behind closed doors, the real buzz isn't just about the newest detection algorithm. It’s about data gravity and the unprecedented data explosion driven by AI-fueled bad actors.

Complete Guide to Testing LLM-Powered Applications

Your AI chatbot might give a customer the wrong price. A RAG-based support agent might cite a document that doesn’t exist. An AI coding assistant might suggest code with a security problem. These issues are common for teams releasing LLM features without proper testing. The reality is that many teams using GPT, Claude, or Gemini don’t have a strong testing strategy. They usually do a few manual checks or simple prompt tests and assume it’s enough.

AI Portfolio Management: Governing AI Investments at Scale

Are you still evaluating when and how to implement AI across your asset and wealth management operations? While many organizations remain in the planning stage, others have already started integrating AI into their decision-making frameworks because AI adoption in the FinTech space has matured enough. According to the PwC Asset & Wealth Management Report, firms adopting AI-led transformation could see up to a 12% revenue increase by 2028.

Beyond the Hype: Is Your Organization Ready for AI at Scale?

According to Perforce's 2026 State of DevOps report, there is a direct correlation between DevOps maturity and AI success. In a highly mature DevOps environment, AI accelerates innovation, optimizes workflows, and enhances security. In an immature environment, it scales chaos, multiplies risk, and inflates costs. So, before we ask ourselves how to make the most of our AI solutions, we must assess if our foundational processes are prepared for the challenge ahead.

Unlocking Intelligence: How AI-Assisted Insights Transform Embedded Analytics

The data visualization landscape is experiencing a seismic shift. No longer is it enough to simply present dashboards filled with colorful charts and metrics. Today's decision-makers need something more powerful: the ability to understand what their data actually means, why trends are occurring, and what actions to take next.

The State of Real Estate Data: Perspectives From Industry Leaders

For years, real estate has been described as a data-rich industry. But in practice, most organizations still struggle to collect, trust, and use their data at scale. Across multiple episodes of the Innovation Blueprint podcast, founders, CEOs, and operators repeatedly came back to the same conclusion: the real challenge in real estate isn’t analytics or AI — it’s data foundations.

Build an Interactive Dashboard in 5 Minutes with Kai

Data Apps are interactive web applications that run directly in your Keboola project. They let you visualize, explore, and interact with your data without needing external BI tools. Think of Data Apps as your custom dashboards, built exactly how you need them. Now, let's see how Kai makes building Data Apps effortless.

Best PAM Solutions for Mid-Size Teams in 2026

Privileged access management has a reputation problem. Nearly one in two IT leaders describes PAM implementation complexity as a top challenge. For enterprises with dedicated security engineering teams and six-figure budgets, that complexity is manageable. For everyone else, it is the reason PAM projects stall, get deprioritized, or never start at all. If you are part of a security team of two to ten people, or an IT leader at a mid-size company that needs to protect privileged credentials without running a multi-month deployment, this guide is for you.

Prompt, Deploy, Pray Is Dead: Validating AI Code with Proxymock

Recent outages tied to AI-assisted code changes have pushed companies into a corner. After several incidents with massive “blast radius” impacts, organizations like Amazon introduced stricter controls—mandating that senior engineers manually review all AI-generated code before it hits production. That response makes sense on paper, but it exposes a fatal flaw in the modern development pipeline.