If your AI feels slow, expensive or risky, the problem isn’t the models — it’s the data, and cognitive data architecture is the fix.

AI initiatives don’t stall because models aren’t good enough, but because data architecture lags the requirements of agentic systems.

The Southeast Asian country is pushing hard for digital transformation, seeing digital identity and governance as central to ...

An unsecured database exposed 4.3 billion LinkedIn-derived records, enabling large-scale phishing and identity-based attacks.

In the MCP era, there is no "expected behavior" to deviate from. Every workflow is unique. Every sequence of tool calls is ...

The best defense against prompt injection and other AI attacks is to do some basic engineering, test more, and not rely on AI to protect you.

Realsee3D is a large-scale multi-view RGB-D dataset designed to advance research in indoor 3D perception, reconstruction, and ...

1. Risk: AI Monoculture (Shared Blind Spots). This is the most critical and overlooked systemic vulnerability. Building your ...

Bharat Kumar Dokka spearheaded a comprehensive enterprise-wide SQL Server migration initiative across a major client's Administration Infrastructure project, successfully modernizing multiple ...

Moreover, LLMs are inference machines that rapidly adapt to infer sensitive details, such as your political leanings, health ...

When we prompted multiple AI models on why they lie, the first thing they wanted to do was differentiate lies from ...

Overview Cloud analytics platforms in 2025 are AI-native, enabling faster insights through automation, natural language ...