Without a clear view of where sensitive data lives, who can access it and how it moves, blind spots can quickly turn into ...

One of the greatest weaknesses of AI agents that read and understand vast amounts of enterprise data is "hallucination"—the generation of plausible-sounding but factually incorrect information. KAIST ...

Part of the SD Times 100 2026 series. See the full SD Times 100 2026 list for every category and honoree. Every conversation ...

High-impact AI implementations are more likely to treat data architecture, governance, and operationalization as strategic requirements, according to TDWI's 2026 Blueprint report.

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?

Google Cloud Summit came to London last week, and we took the opportunity to sit down with database execs Sailesh ...

Couchbase AI Data Plane combines persistent agent memory, vector search and an enterprise MCP server that runs on-device when ...

Roese's predictions: stronger AI governance, better data management, agentic AI infrastructure, resilient AI factories, and sovereign AI strategies.

NUS researchers' MRAgent framework reduces LLM agent memory retrieval to 118K tokens per query — vs. 3.26M for LangMem — ...

Sub-headline: HUST researchers systematize SNA methods, building an evolutionary taxonomy based on graph representation ...

Genie Ontology aims to unify business definitions across systems, but analysts say data quality and governance will make or ...