If your security data could talk, what would it say? With generative AI, this question isn’t rhetorical. While AI is making waves across all industries, its transformative potential is especially clear in security.

Providing generative AI and traditional AI models with the data they need to be accurate and performant requires organizations to establish a single foundation of high-quality, standardized data. But getting there can be challenging for numerous reasons:

• Organizations often have multiple data lakes using different schemas, data formats, data governance practices, and more
• They may be storing massive volumes of data that require a significant investment to bring together
• They use disparate tools or solutions that don’t integrate with one another, creating data, product, and workflow siloes
• They may not have the resources needed to unify a data estate, such as talent or capital

What’s needed is a holistic approach to data and AI—an approach that accounts for everything from the data sources, to the AI models, to the process of surfacing insights to the security team while keep costs manageable. It’s time to move away from a siloed approach to data, solutions, and workflows.