After importing IBMKnowledge Accelerator(KA) assets using an API (or other methods), those assets — such as categories, terms, and relationships — are part ofgovernance artifactsin Cloud Pak for Data. However,to make them usable by specific users, youmust assign collaboratorsto the relevantcategories.

This ensures users have the appropriatepermissions(e.g., to view, curate, or manage terms) within the Information Governance Catalog (IGC) or Watson Knowledge Catalog.

Watson Discovery employs advanced natural language processing and ranking algorithms that begin with keyword-based search. This type of search enables the tool to locate and return the most relevant document passages based on the presence and context of keywords in user queries. While it may also use metadata filters (facets), the core retrieval method remains keyword-driven to find relevance at both document and passage levels.

In IBM Data Virtualization, caches must be manually defined by administrators. While monitoring and query performance statistics can guide where caching would be beneficial, the creation and configuration of caches (e.g., refresh schedules and scope) are manual tasks. There is no automated cache creation mechanism (A), nor are DataStage flows used for cache maintenance (B). Suggestions based on statistics (D) may assist administrators, but they do not automatically create the caches.

IBM Cloud Pak for Data includes built-in Data Protection Rules to enforce access control on sensitive data, such as PII. These rules are integrated directly into services like IBM Data Virtualization, Data Privacy, and IBM Knowledge Catalog. When analysts or applications access data through these services, the platform automatically masks, obfuscates, or restricts access to sensitive fields based on the defined policies. This ensures compliance with data privacy regulations and organizational security policies without manual intervention.

As mentioned previously, IBM Data Virtualization provides a single governed access point for data residing across heterogeneous systems. It reduces data movement by creating virtualized views, enabling organizations to query multiple sources seamlessly while adhering to centralized security and governance policies. This capability supports analytics and reporting without duplicating or moving data.

In air-gapped (offline) environments, it’s not just the container images that require preparation. Package managers like pip (for Python) and other utilities commonly attempt to pull dependencies from public internet repositories. These must be redirected to internal mirrors or handled via offline bundles. If left unaddressed, certain operations within components like Jupyter notebooks, Watson Studio, or pipelines may fail. License validation and monitoring do not inherently require internet access, and data movement between nodes is part of normal cluster function—not something to be blocked.

In IBM Cloud Pak for Data, a "Connection" is a configuration object that defines how to access an external data source, such as Db2, Oracle, or an S3 bucket. It does not involve moving or copying data. Instead, it acts as a reference to the external data source environment, enabling users to browse, query, and analyze the data in place. This approach supports virtualized access and governance while avoiding data duplication and ensuring data stays within its source system, aligning with enterprise data security policies.