A practical overview of security architectures, threat models, and controls for protecting proprietary enterprise data in retrieval-augmented generation (RAG) systems.

The criticisms aimed at the technology — the lack of reliability, data leakage, inconsistency — offer a playbook for growing a business that competes with rival companies that leverage AI.

Chinese and Singaporean researchers have developed a defense mechanism that poisons proprietary knowledge graph data, making ...

For financial institutions, threat modeling must shift away from diagrams focused purely on code to a life cycle view ...

The consumer goods giant is taking its advanced analytics approach and adding AI for greater value, but its data leader, ...

From data poisoning to prompt injection, threats against enterprise AI applications and foundations are beginning to move ...

Ritesh Singh Chandel, Rushabh Kothari, and Sonu Kumar Prashant’s Arivihan has a fully automated GenAI tutor that helps ...

Joining the ranks of a growing number of smaller, powerful reasoning models is MiroThinker 1.5 from MiroMind, with just 30 ...

"SpreadJS v19 brings a helpful AI Assistant to JavaScript spreadsheets that can create or explain formulas, generate or analyze PivotTables, and even do things like translation right within cells," ...

OpenAI continues its push into healthcare with the launch of ChatGPT Health, a new feature that connects its AI chatbot with ...

Overview Brand mentions build trust through context and repetition, not just links or technical optimization.LLMs rely on ...

Secure stronger market performance by unifying your brand signals across search, AI platforms, and on-site experiences.