Content related to Enterprise AI Meets Access and Entitlement Challenges: A Framework for Securing Content and Data for AI
LLM Solutions PoC to Production: From RAGs to Riches (Part 1)
In the past year, many of the organizations EK has partnered with have been developing Large Language Model (LLM) based Proof-of-Concepts (PoCs). These projects are often pushed for by an enthusiastic IT Team, or internal initiative – with the low … Continue reading
The Journey to Unified Entitlements
Now, more than ever, organizations need a clear and consistent way to ensure that the access permissions for all their data are applied consistently across the enterprise. We call this unified entitlements Continue reading
Inside the Unified Entitlements Architecture
Today’s enterprises face a perfect storm in data access governance. The shift to cloud-native architectures has created a sprawling landscape of data sources, each with its own security model. For example, a typical enterprise might store customer data in Snowflake, … Continue reading
Unified Entitlements: The Hidden Vulnerability in Modern Enterprises
Maria, a finance analyst at a multinational corporation, needs quarterly revenue data for her report. She logs into her company’s data portal, runs a query against the company’s data lake, and unexpectedly retrieves highly confidential merger negotiations that should be … Continue reading
Maturing Data Processes at a Decentralized Federal Organization
A large government agency sought EK’s help in addressing significant data management challenges they were facing. The agency had a decentralized organizational structure and a complex technical ecosystem, which created unique challenges for remote employees in finding, accessing, and sharing critical data at the time of need. These challenges resulted … Continue reading
Optimizing Historical Knowledge Retrieval: Leveraging an LLM for Content Cleanup
Enterprise Knowledge (EK) recently worked with a Federally Funded Research and Development Center (FFRDC) that was having difficulty retrieving relevant content in a large volume of archival scientific papers. Researchers were burdened with excessive search times and the potential for knowledge loss … Continue reading
Graph Analytics in the Semantic Layer: Architectural Framework for Knowledge Intelligence
Introduction As enterprises accelerate AI adoption, the semantic layer has become essential for unifying siloed data and delivering actionable, contextualized insights. Graph analytics plays a pivotal role within this architecture, serving as the analytical engine that reveals patterns and relationships … Continue reading
What is a Knowledge Asset?
Over the course of Enterprise Knowledge’s history, we have been in the business of connecting an organization’s information and data, ensuring it is findable and discoverable, and enriching it to be more useful to both humans and AI. Though use … Continue reading
Beyond Traditional Machine Learning: Unlocking the Power of Graph Machine Learning
Traditional machine learning (ML) workflows have proven effective in a wide variety of use cases, from image classification to fraud detection. However, traditional ML leaves relationships between data points to be inferred by the model, which can limit its ability … Continue reading
The Role of Taxonomy in Labeled Property Graphs (LPGs) & Graph Analytics
Taxonomies play a critical role in deriving meaningful insights from data by providing structured classifications that help organize complex information. While their use is well-established in frameworks like the Resource Description Framework (RDF), their integration with Labeled Property Graphs (LPGs) … Continue reading