Artificial Intelligence (AI) is here, and it has the potential to revolutionize industries, enhance customer experiences, and drive business efficiencies. But with great power comes great responsibility — ensuring that AI use is ethical is paramount to building and maintaining customer trust. At Tricentis, we’re committed to responsible AI practices. At the core of this commitment are data privacy, continuous improvement, and accessible design.

The modern data-driven approach comes with a host of benefits. A few major ones include better insights, more informed decision-making, and less reliance on guesswork. However, some undesirable scenarios can occur in the process of generating, accumulating, and analyzing data. One such scenario involves organizational data scattered across multiple storage locations. In such instances, each department’s data often ends up siloed and largely unusable by other teams.

As compute gets increasingly powerful, the fact of the matter is: most AI workloads do not require the entire capacity of a single GPU. Computing power required across the model development lifecycle looks like a normal bell curve – with some compute required for data processing and ingestion, maximum firepower for model training and fine-tuning, and stepped-down requirements for ongoing inference.

In today's fast-paced world of media publishing, keeping up with technological advancements and changing consumer preferences is no easy task. Tight budgets, fierce competition and evolving audience behaviors add to the pressure, creating what's often termed the "content crash" — a saturation of content that makes it hard for publishers to stand out. But amidst these challenges, there's a beacon of hope: generative AI.

With organizations prioritizing data-driven decision-making, the amount of collected and stored data is reaching historic highs. Meanwhile, organizations are democratizing access across all functions to convert this data into actionable insights. Since more users will work with sensitive data, ensuring secure access is more important than ever. Organizations must regulate and maintain the relationship between their data assets and users. Why?

In today’s fast-paced business environment, having control over your data can be the difference between success and stagnation. Leaning on Master Data Management (MDM), the creation of a single, reliable source of master data, ensures the uniformity, accuracy, stewardship, and accountability of shared data assets.

Today’s enterprise networks are complex. Potential attackers have a wide variety of access points, particularly in cloud-based or multi-cloud environments. Modern threat hunters have the challenge of wading through vast amounts of data in an effort to separate the signal from the noise. That’s where a security data lake can come into play.