Federal agencies face enormous pressure to integrate AI into their operations. But they also face immense challenges to do so securely – both that the AI is accurate and trustworthy, and that it does not introduce new risks. Much of the current risk arises from legacy infrastructure and systems. Agencies’ data pipelines have undergone massive growth, including structured, unstructured and streaming data, yet legacy infrastructure i...

The original version of Moore’s law declared that the capacity of an integrated circuit – the backbone of IT – doubles every two years. For decision-makers in the era of artificial intelligence, this could be revised to observe that new versions of AI proliferate at an accelerated rate. Because AI is so new, yet already has so many offshoots, agencies are wrestling with which type of AI to implement – agentic or generat...

As the federal government begins to implement plans to build AI data centers and generation plants to support its current and anticipated demands for AI capabilities, agencies continue to pursue infrastructure modernization to streamline delivery of services. These two large-scale efforts go hand-in-hand: improving infrastructure supports mission resilience, Zero Trust security, and energy optimization, while purpose-designed AI data centers c...

The federal government is moving quickly to embrace the use of AI tools across agencies. The purpose of such tools is to improve delivery of public services, enhance decision-making, and strengthen national security. To use these tools responsibly, agencies must understand their infrastructure, data governance, and workforce availability policies, and how they intersect with AI requirements and operations. Additionally, there are ongoing conve...

There have been plenty of articles warning of the cybersecurity risks posed by artificial intelligence (AI), whether it is bad actors – from hostile nation-states to cyber criminals – or new attack vectors caused by AI implementations. At the same time, numerous other reports point out that AI can strengthen cybersecurity. For agencies looking to incorporate AI into their systems, cybersecurity is a great place to focus. AI tools c...

Federal agencies are accelerating their adoption of artificial intelligence (AI) to meet growing demands to increase speed and efficiency to keep pace with AI requirements. Yet, AI is only as effective as the trusted data that powers it. While agencies possess vast data amounts of datasets and data products, much of it remains isolated, inconsistent, and untrusted, putting AI program at risk. To unlock the full potential of AI, data must be ac...

Federal agencies are rapidly adopting artificial intelligence to improve mission performance, enhance service delivery, and strengthen decision-making. As adoption accelerates, leaders face new challenges around preparing fragmented data, governing AI systems, and managing risks while delivering measurable results. Agencies are looking for practical strategies to overcome cultural and technical barriers, ensure responsible use of emerging tech...

Finding ways to incorporate artificial intelligence (AI) into federal agencies’ workflows are becoming increasingly important to meeting agencies’ missions and fulfilling Americans’ needs. Between budgetary and workforce constraints, agencies are eager to find ways to do more with less, and AI has the greatest potential to achieve success. Agencies wrestling with how to use AI to the greatest effect face common challenges and...

As generative AI platforms like ChatGPT, Claude, Copilot, and Gemini become more prevalent, federal agencies are exploring how to harness AI capabilities while maintaining control over sensitive data, infrastructure, and mission-critical operations. Public cloud-based AI models offer scale, but they often fall short on security, transparency, and compliance — especially for defense, intelligence, and civilian agencies operating in class...

The rapid adoption of artificial intelligence (AI) is transforming both the design and use of communication networks. Robust telecom infrastructure — such as private 5G, SD-WAN, and edge computing — is critical for transporting and processing the massive data flows underpinning AI workloads across industries. At the same time, AI is evolving communications itself, powering network automation, predictive analytics, and intelligent m...