Increased expectations for enhanced digital services and capabilities from citizens and government employees are forcing agencies to accelerate their transformation efforts. One way that the federal government can make a difference is by modernizing its software development practices to provide engaging digital services to citizens and enable its workforce to drive mission effectiveness—all while keeping applications and data safe. Simplifying the application modernization journey is the way to start. This Public Sector Tech Talk episode will discuss the process to make application modernization more efficient in government environments as well as key considerations for legacy applications, new applications and applications in transition. It will look at the impact of multi-cloud deployments and why utilizing containers is an important step in the process.

In the past, only a chosen few public sector agencies and enterprises – those with deep pockets and the most advanced data and analytics requirements – had access to high-performance computing (HPC) solutions. Today, data lives everywhere. HPC has become integral to doing business – from predicting hurricanes and delivering clean water to manufacturing safe, efficient vehicles and developing and executing COVID vaccines and clinical trials. How do public sector entities integrate HPC into legacy infrastructures in a timely, cost-efficient manner without creating silos? What role do Artificial Intelligence (AI), the Internet of Things (IoT), and the edge play in deploying the infrastructure needed to solve today’s and tomorrow’s most complex problems? This episode offers insight into these and other questions about HPC in the public sector.

Businesses moving forward with an AI strategy need to ensure the security of those AI models is part of that strategy. In this TEC37 encore, we revisit issues in AI model security for the organization and suggest approaches for identifying and closing gaps in defending AI models. We will also touch on the importance of building a robust, mature, and repeatable program for assessing AI model vulnerabilities.