Trust by design: the new standard for AI-ready data
Instead of merely accelerating flawed processes with AI, organizations must prioritize building a robust data foundation through unified governance, modeling, and quality control. …
Instead of merely accelerating flawed processes with AI, organizations must prioritize building a robust data foundation through unified governance, modeling, and quality control. …
Rapidly deploying autonomous AI agents without prepared data environments creates a significant risk of cascading failures, where minor data errors trigger a chain reaction of inco…
AI security is fundamentally an identity security challenge because AI agents do not create their own trust, but instead inherit the permissions and vulnerabilities of existing ide…
High AI costs are often driven by fragmented data that forces models to expend excessive tokens trying to interpret missing business context. Implementing a semantic layer provides…
Using the recent OpenAI breach of Hugging Face as a warning, this article argues that autonomous AI agents can rapidly exploit existing identity vulnerabilities much faster than hu…
While general-purpose AI can quickly generate plausible data models, these isolated snapshots lack the governance, connectivity to live databases, and security required for enterpr…
As SAP PowerDesigner approaches its end of life, many leading organizations in highly regulated sectors like finance and aviation are migrating to Quest Software’s erwin Data Model…
As the rapid rise of non-human identities like AI agents and APIs creates a dynamic new risk surface, traditional, siloed identity management tools are no longer sufficient for reg…
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 …