Data (In)Security: The Imperative of Trust

As humanity continues to plunge into the age of artificial intelligence (AI), we are becoming increasingly beholden to and reliant upon the aggregate data collected and shared all around us. From autonomous vehicles to large language models and generative pretrained transformers (GPTs) to facial recognition doorbells, data-driven AI tools are rapidly becoming a common and central component of our lives. However, these tools, and the data on which they rely, are only as valuable as the authenticity and veracity of the data itself—that is, the data is only as valuable as the trust that can be placed in it. Accurate, quality data can provide profound insights into market conditions, develop unmatched AI models, and drive decision making in nearly every industry. Conversely, inaccurate data can result in flawed predictions, poisoned AI training data, and perilous decisions. When we can no longer trust the data being input into a system, we can no longer trust the system itself. Therefore, it is trust in data—trust in its authenticity and veracity—from which the value of any system is derived.