Systems | Development | Analytics | API | Testing

Artificial Intelligence (AI) is reshaping Quality Assurance (QA) by accelerating testing, improving accuracy, and uncovering insights that once required hours of manual analysis. Yet, with great capability comes great responsibility. As AI begins to influence how tests are designed, executed, and interpreted, ensuring that it’s used ethically has never been more important. Responsible AI in QA isn’t only about compliance — it’s about trust.

The stakes for AI and data privacy are high. Risks in non-production environments are increasing. Regulatory bodies enact new privacy regulations each year. And concerns are rising as the AI/ML boom continues. Businesses like yours must be aware of key AI data privacy challenges. Read on to learn what these challenges are and how to address them.

The most innovative companies know that artificial intelligence (AI) problems are fundamentally data problems. To produce the right results, AI applications, ranging from real-time fraud detection to autonomous agents, must share a common foundation: trustworthy data in motion.

We’re excited to announce our Real-Time Context Engine, now available in Early Access. It’s a key part of Confluent Intelligence, our vision to bring real-time data directly to production AI systems through the power of Apache Kafka and Apache Flink.

As 2026 planning kicks into high gear, your CFO may be putting every technology investment under the microscope, but the focus and pressure to deliver on AI Innovation has never been higher. For IT Leaders, the challenge is clear and straightforward. How do you strike a balance continuing to move your AI initiatives forward and maintaining a balanced budget in 2026?

Atlassian Rovo is the latest evolution of AI-powered knowledge discovery and workflow assistance, built by Atlassian to help teams turn scattered information into meaningful actions. Rovo works on three core dimensions: Rovo is available in all Atlassian products by default. Users have multiple methods to engage with the AI, each offering different levels of complexity and interaction.

‘API governance’ can sound like an intimidating concept. When I started as a solutions engineer at SmartBear in 2018, it took me a while to wrap my head around what exactly API governance entails and even longer to be able to explain it clearly and simply to our customers.

Everyone's telling you to innovate faster with AI. Move quicker. Ship more features. Deploy more agents. But before we sprint headlong into the AI revolution, we need to have a proper dollars-and-cents conversation that most companies are avoiding. Disclaimer: Statistics cited in this post were reported in the 2025 State of AI Cost Governance Report by Mavvrik. The data is stark: 84% of companies report more than a 6% hit to gross margin from AI costs.