After analyzing 25 customer meetings across various industries, Glenda O’Keefe identifies five recurring themes regarding the challenges of scaling AI and data products. While tech…
As non-human identities like AI agents and bots begin to vastly outnumber humans, identity has become the primary attack surface and the new security perimeter for the modern enter…
Data modeling is the essential foundation for successful AI implementation, providing the shared semantic definitions required for effective data and AI governance. Without this fo…
The article warns that "semantic chaos"—the lack of shared business definitions—poses a major risk to enterprise AI by enabling models to confidently scale errors and drive costly …
Many organizations waste significant time and resources by rebuilding existing data assets due to poor discoverability and a lack of trust in current datasets. To break this cycle,…
Resisting change in data management leads to lost competitive advantage because fragmented, ungoverned data prevents organizations from reacting quickly to market shifts. To remain…
As traditional security perimeters disappear, organizations must adopt an identity-first approach to govern access across people, devices, and AI agents through a strategy of preve…
Recent research reveals that even organizations with mature data programs are rebuilding assets instead of reusing them due to a fundamental lack of trust in existing data quality …
Quest Software has acquired Anetac to strengthen its identity security capabilities, specifically targeting the growing risks posed by non-human identities like AI agents, APIs, an…
The article argues that enterprise AI success depends less on acquiring more data or larger models and more on implementing a "navigation layer" of business context to ensure data …
As AI-driven ransomware attacks become more automated and difficult to prevent, identity systems like Active Directory and Microsoft Entra ID have become primary targets for attack…
Many organizations struggle to derive value from data because their existing datasets are not designed for practical use, leading to inefficient, manual preparation processes. To b…
