Secure AI starts with secure identity
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…
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The Quest Blog
38 articles · Page 1 of 4
The #1 question to reduce AI costs
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…
The four identity capabilities organizations need for agentic AI security
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…
The 90-second data model: Why AI-generated data models aren’t a modeling practice
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…
Replacing PowerDesigner: Real migration & modernization success stories
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…
A new approach to identity security in regulated industries
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…
25 customer meetings in 3 days: 5 recurring themes about data and AI in 2026
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…
Identity security in the age of autonomous AI
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…
AI won’t fix a broken data foundation: It amplifies it
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…
Lost in translation: Why semantic chaos is the real AI risk
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 …
Data product reusability: Cut costs & scale AI with 3 key changes
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,…
The cost of standing still: what data inaction really risks in an AI-driven market
Resisting change in data management leads to lost competitive advantage because fragmented, ungoverned data prevents organizations from reacting quickly to market shifts. To remain…