What does a good DevSecOps pipeline should look like from a code security perspective? We hear this question often, and even though there are multiple answers, we’ve put together a blueprint that everybody could easily start with.

From ChatGPT to DALL-E to Grammarly, there are countless ways to leverage generative AI (GenAI) to simplify everyday life. Whether you’re looking to cut down on busywork, create stunning visual content, or compose impeccable emails, GenAI’s got you covered—however, it’s vital to keep a close eye on your sensitive data at all times.

In today’s age of rapidly increasing data collection, data privacy laws are becoming more prevalent than ever. The EU’s General Data Protection Regulation (GDPR) is considered the worldwide benchmark of data privacy law. While many countries have followed similar regulations, the United States does not have a GDPR equivalent. Instead of national standards and regulations, individual states pass their own privacy laws.

Often security engineers find it difficult to scale secure code review processes either due to lack of funding, adoption to smaller sprint cycles or even security engineers failing to integrate security to agile philosophy. This post talks about various ways to address such challenges.

The data protection landscape is changing. The rapid adoption of cloud and software as a service (SaaS) greatly affects the financial industry, where many institutions rely on SaaS vendors for data protection previously implemented in-house. Moody’s Analytics relies on cloud service providers (CSPs) to adequately protect its data, showcasing the need for scalability, industry cost controls, and flexibility.