Every day, countless systems work in unison behind the scenes, powering our lives. From the electricity that lights our homes to the internet that provides global connectivity, our critical infrastructure is integral to our economy, our security, and our way of life.
For federal missions, the stakes are high. Sure, an inaccurate weather forecast can disrupt your day or maybe ruin your favorite pair of shoes. But for national security missions, an unreliable AI model can introduce false positives or negatives, leading to the misidentification of threats. Similarly, for federal health missions, an unreliable AI model could perpetuate bias, negatively impacting research and care.
The demand for solutions leveraging artificial intelligence (AI) and machine learning (ML) continues to surge. Core to every AI or ML solution is a robust security model that protects data privacy. These more advanced data solutions demand a data security and privacy paradigm that evolves beyond approaches such as data suppression and data masking that, frankly, miss the mark when it comes to robust model security.
One of the biggest challenges associated with AI algorithms is the idea that they require vast amounts of data. While data is essential for development, it presents several challenges, particularly in the areas of Data Privacy and Data Security.
Fall is synonymous with football. The roar of the crowd, the crunch of cleats on the turf, the whistle of the referee—it’s a cacophony of athleticism and excitement. Ever-focused on the sidelines—the head coach, meticulously analyzing player strengths and weaknesses, evaluating opponent strategies, and making decisions on the fly.
In the age of TikTok dance crazes and meme-able moments, marketers revel in viral sensations that sweep the globe. Over the decades, we've seen several marketing campaigns gain virality, from Wendy’s “Where’s the Beef,” to Nike’s “Just Do It,” and Staples’ “Easy” button.
Imagine a doctor who gives you a diagnosis but can't explain how they reached that conclusion. Doesn’t instill a lot of confidence, right? That's how some AI systems operate: their inner working are mysterious, and it’s hard to pinpoint why they make a specific prediction.
The world of Artificial Intelligence (AI) can feel like a complex maze of technical jargon and specialized terms. Concepts like "deep learning" and "neural networks" get tossed around in conversation. “AI,” “ML,” “LLM,” “GenAI” – it’s a veritable acronym soup that can leave many federal professionals wondering, what does it all mean?
Government agencies are increasingly adopting cloud technologies to enhance efficiency, scalability, and service delivery. However, managing cloud costs remains a challenge. Unchecked expenses can quickly spiral out of control, impacting budgets and project viability.
In today's rapidly evolving mission and technological landscape, federal agencies face a constant challenge: balancing the need for reliable, secure IT operations with the desire to innovate and deliver next-generation services. Often, the burden of maintaining legacy systems can overshadow the potential of advanced features and emerging technologies.
Summer is here – beach vacations, fun in the sun, kids running amok. Now, federal IT systems aren’t the first things to pop into my mind when I’m thinking about summer vacation, but humor me with this analogy.
The trust of the public in the Federal Government has remained consistently low for the past few years. To address this issue, President Biden has recently issued an Executive Order #14058, issued 13th December 2021) directing specific agencies to improve the services they provide to the public.
