Amazon CloudWatch is a great tool for DevOps engineers, developers, SREs, and other IT personnel who require basic Amazon Web Services (AWS) log processing and analytics for cloud services and applications deployed on AWS. However, most developer teams will ultimately need more logging functionality than a basic AWS log analyzer like Amazon Cloudwatch can provide. For example: That's why, although CloudWatch may be one tool in your log analytics strategy, it probably should not be the only one.

Cyber threats against AI systems are on the rise, and today's AI developers need a robust approach to securing AI applications that address the unique vulnerabilities and attack patterns associated with AI systems and ML models deployed in production environments. In this blog, we're taking a closer look at two specific tools that AI developers can use to help detect cyber threats against AI systems.

Enterprise data and analytics is a fast-evolving field in enterprise IT, where new technologies and solutions are creating revolutionary ways to extract insights from data. To keep pace with these changes and drive value creation through data analytics initiatives, organizations must be willing to adopt innovative solutions, embrace new and emerging best practices, and move beyond obsolete or outdated methods that are no longer effective. Our blog post this week is all about transforming your enterprise data architecture to elevate your data management and analytics capabilities.

Managed Detection and Response (MDR) services play a critical role in cybersecurity. These technologies remotely monitor, detect, and respond to threats, blending threat intelligence with human expertise to hunt down and neutralize potential risks. However, one of the biggest challenges MDRs face is managing the sheer volume and variety of threat intelligence data they receive. This data comes from internal resources and the numerous security technologies their customers use, making it difficult to create a cohesive picture of the threat landscape.