Systems | Development | Analytics | API | Testing

If you've ever updated a dependency, scanned your project for vulnerabilities, or opened a GitHub Security Advisory, you've probably seen something like CVE-2025-13465. For many developers, it's just another identifier attached to a security warning. Easy to ignore, especially when your priority is simply getting your application shipped. But that small reference represents something much bigger: it's the common language the software industry uses to identify and communicate security vulnerabilities.

Learn how to manage column-level access controls in BigQuery using IAM data governance tags, ensuring secure, scalable data classification and protection.

As enterprises race to adopt AI, weak data foundations are preventing more than half (58%) of organizations in the United States and Canada from realizing value and contributing to an estimated $108 billion in wasted global AI investment each year, according to a report from Hitachi Vantara. The reason is rarely bad models or lack of ambition.

As APIs grow across teams, keeping them consistent becomes difficult. Some APIs follow naming conventions and include clear descriptions, while others don’t. Over time, these differences make APIs harder to understand, review, and maintain. That is where API linting helps. That made it possible to apply custom Spectral rules as part of local development, Git workflows, or CI checks.. Teams can now upload and manage custom Spectral rulesets directly from the Insomnia UI.

Production AI no longer generates one kind of traffic. It generates four patterns, and most teams govern only one. **AI traffic management** starts with a single decision: **proxy AI traffic** through one control point instead of letting it flow straight from application code to model providers. Skip that step and security teams have no policy chokepoint, token spend climbs with no meter, and every new provider adds an integration nobody owns.

The world of banking is shifting. For a long time, cool consumer banking apps made all the headlines. But today, the biggest change is happening in the business world. Recent financial reports from firms like McKinsey and Boston Consulting Group show that small and medium enterprises (SMEs) spend over $850 billion a year on banking fees and loans. Because of this, specialized neobanking for SMEs has quietly grown into a massive $100 billion market opportunity in 2026.

Seamlessly chain Python and SQL in Jupyter notebooks using BigQuery DataFrames and the %%bqsql cell magic. Learn how in this step-by-step guide.

Learn how to use BigQuery to analyze Gemini Enterprise usage logs, audit compliance, and build Looker dashboards at scale for your organization.

Last April FloQast, an American maker of accounting software, published something unusual: a detailed engineering post on Amazon Web Services’ machine-learning blog, co-authored with AWS personnel, explaining precisely how its AI-powered transaction-matching feature works under the hood. The post described cloud infrastructure, model selection, and the specific technique (generating matching rules from user-supplied examples) that powers its AutoRec product.

We're thrilled to announce a strategic technology partnership between **Kong** and **ModelOp**. As enterprises rapidly transition into the agentic era, they face a critical challenge: how to deploy AI fast enough to stay competitive without taking on unacceptable regulatory or security risks. Together, **ModelOp** and **Kong** are solving the "last mile" problem of enterprise AI delivery.