Category: Artificial Intelligence

Organizations are increasingly embracing a shift-left approach when it comes to security, actively integrating security considerations into their software development lifecycle (SDLC). This shift aligns seamlessly with modern software development practices such as DevSecOps and continuous integration and continuous deployment (CI/CD), making it a vital strategy in today’s rapidly evolving software development landscape. At its […]

Customers face a number of challenges to quickly and effectively respond to a security event. To start, it can be difficult to standardize how to respond to a partic­ular security event, such as an Amazon GuardDuty finding. Additionally, silos can form with reliance on one security analyst who is designated to perform certain tasks, such […]

Generative artificial intelligence (generative AI) has captured the imagination of organizations and is transforming the customer experience in industries of every size across the globe. This leap in AI capability, fueled by multi-billion-parameter large language models (LLMs) and transformer neural networks, has opened the door to new productivity improvements, creative capabilities, and more. As organizations […]

Software development is a well-established process—developers write code, review it, build artifacts, and deploy the application. They then monitor the application using data to improve the code. This process is often repeated many times over. As Amazon Web Services (AWS) customers embrace modern software development practices, they sometimes face challenges with the use of third-party […]

In the telecommunications industry, sensitive authentication and user data are typically received through mobile voice and keypads, and companies are responsible for protecting the data obtained through these channels. The increasing use of voice-driven interactive voice response (IVR) has resulted in a need to provide solutions that can protect user data that is gathered from […]

In this post, you will learn how to use familiar security controls to build more secure machine learning (ML) workflows. The ideal audience for this post includes data scientists who want to learn basic ways to improve security of their ML workflows, as well as security engineers who want to address threats specific to an […]

Customers want to use AWS services to operate on their most sensitive data, but they want to make sure that only the right people have access to that data. Even when the right people are accessing data, customers want to account for what actions those users took while accessing the data. In this post, we […]

Amazon SageMaker, like other services in Amazon Web Services (AWS), includes security-related parameters and configurations that you can use to improve the security posture of resources as you deploy them. However, many of these security-related parameters are optional, allowing you to deploy resources without them. While this might be acceptable in the initial exploration stage, […]