Design for Trust, Build for Good
Safety is not an afterthought; we engineer it into AI systems. Compliance? That's just what happens when you build it right.
Catch issues upstream, and keep legal downstream.
Research Insights
Technical Risk Assessment: Agent-First vs. Code-First Architectures in Enterprise AI
This is not merely a preference for different tools, but a profound difference in engineering philosophy.
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The Governance Gap: Why Classical Audits Fail on Foundation Models
The governance of artificial intelligence is currently facing an epistemological crisis. The core issue is a "category error": regulators are attempting to govern Foundation Models (FMs) using frameworks designed for a fundamentally different class of technology.
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Imagining the Future of AI Governance: Why Database Principles are the Bedrock of Trust
The high-level conversation about AI governance—focusing on fairness, transparency, and accountability—frequently overlooks the foundational layer upon which all trustworthy AI is built: the data architecture.
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