| d9f2ac31d430475f… | In the Fabric-native approach, metric definitions and business logic documentation are maintained in Fabric and Power BI so that official business logic remains in native analytics tooling. | active | contextual | yes | |
| a0b54c8b87c6e9d0… | In the Pyrana-on-Fabric approach, each answer logs the Context-Unit IDs used, and each unit traces to the source document, version, approver, and verbatim quote so that AI outputs are fully traceable to approved evidence. | active | contextual | yes | |
| 952c00ad4d8483da… | For source-to-AI traceability in the Fabric-native approach, each agent must be designed and built individually so that traceability is implemented per agent rather than provided as a native shared mechanism. | active | contextual | yes | |
| 5e553a9c4fa771c7… | In the Pyrana-on-Fabric approach, source-to-dashboard traceability is preserved through Power BI on Fabric so that dashboard lineage remains intact. | active | contextual | yes | |
| dbdfb93da84a336d… | For source-to-dashboard traceability, the Fabric-native approach relies on Purview lineage as a strong capability so that dashboard outputs can be traced back through governed lineage. | active | contextual | yes | |
| e26c90374e46006a… | In the Pyrana-on-Fabric approach, agents consume the same governed model and cite governed metric definitions instead of recomputing them so that metric usage stays aligned with approved business definitions. | active | contextual | yes | |
| fd2d254b5d7b0a42… | For the governed data model and metrics layer, the Fabric-native approach uses the Fabric semantic model as a strong native capability so that governed metrics are centrally modeled. | active | contextual | yes | |
| 64d527501307f039… | The minimum security, governance, and data integrity requirements include Fabric architecture, workspace structure, RBAC, data-source inventory, governed metrics, validation, traceability, sensitive-data handling, deployment management, documentation, and handoff so that the solution is operationally governed end to end. | active | contextual | yes | |
| d004b5ba53481769… | In the Pyrana-on-Fabric approach, agent governance includes native agent telemetry, an evaluation harness, audit logging, and human-in-the-loop approvals so that agents can be monitored, tested, and controlled. | active | contextual | yes | |
| 1b894d3092c07571… | For agent governance, testing, and monitoring, Assemble provides agent-level telemetry in the Fabric-native approach, but this is identified as a current gap so that native coverage is recognized as incomplete. | active | contextual | yes | |
| e1e6fc47172e5e7a… | In the Pyrana-on-Fabric approach, agent and workflow behavior is deployed through configuration-over-code using versioned YAML shipped through GitHub CI/CD so that new use cases can be added without platform changes. | active | contextual | yes | |
| 88d784c1ff3d4874… | For production deployment changes, the Fabric-native approach uses Fabric deployment pipelines so that production changes are promoted through native deployment tooling. | active | contextual | yes | |
| 5442dc0c99afee15… | In the Pyrana-on-Fabric approach, Pyrana inherits labels and enforces private inference, no training on customer data, tenant isolation, and a rule to never expose raw confidential data in the output gateway so that sensitive data remains protected during AI use. | active | contextual | yes | |
| d5fd5f2bfa2ee208… | For protecting sensitive data, the Fabric-native approach uses Purview labels, data loss prevention, and encryption so that sensitive information is classified and protected through native controls. | active | contextual | yes | |
| b654316749bd66c0… | In the Pyrana-on-Fabric approach, the same Entra ID is used while Pyrana adds per-knowledge-set access scoping and need-to-know controls down to the Context-Unit level so that access can be restricted at a finer knowledge granularity. | active | contextual | yes | |
| 861e5abd8068507a… | For access control, the Fabric-native approach relies on Entra ID together with Fabric and Purview role-based access control so that user permissions are managed through native identity and governance services. | active | contextual | yes | |
| a97e03116a7e3c0b… | In the Pyrana-on-Fabric approach, data handling remains unchanged while Pyrana adds secure document extraction, validation, and storage with provenance and refresh monitoring so that document-based knowledge is governed alongside data workflows. | active | contextual | yes | |
| 841ea890f5ddac7e… | For ingestion, transformation, refresh, and security, the Fabric-native approach uses Fabric pipelines together with Purview so that data movement and governance are handled natively. | active | contextual | yes | |
| 0a5a22b746d5ddf5… | In the Pyrana-on-Fabric approach, OneLake remains the data store while Pyrana’s knowledge graph stores only cited claims and provenance in the customer’s Azure tenant so that governed storage is unchanged and knowledge artifacts remain traceable. | active | contextual | yes | |
| dabb82f6eaf216f4… | In the Fabric-native approach, data is stored in OneLake under governance controls so that the storage layer remains centrally governed. | active | contextual | yes | |
| 1d892b7a47f59b45… | IT must have visibility and approval over data storage, ingestion, transformation, refresh, security, access, sensitive-data protection, production deployment changes, and AI-agent governance, testing, and monitoring so that the platform remains controlled and auditable. | active | contextual | yes | |
| ffc2336856240b5b… | The requirement-by-requirement crosswalk presents every requirement category from the Summer Fridays RFP with the Fabric-native approach and the Pyrana-on-Fabric approach side by side so that a compliant and differentiated vendor response can be built. | active | contextual | yes | |
| 57cc60eaa2985c81… | The RFP further requires AI agents to use only approved sources, respect permissions, provide source traceability, identify incomplete or unavailable data, avoid inventing metrics or conclusions, resist prompt injection, and clearly separate recommendations from validated facts. | active | contextual | yes | |
| 06152ac040d0be51… | The RFP requires AI agents to be secure, governed, limited to approved use cases, and documented for purpose, sources, audience, access, limitations, testing, and monitoring so that agent deployment remains controlled and reviewable. | active | contextual | yes | |
| 070d94a1053bc0c1… | In the Pyrana-on-Fabric approach, an output gateway normalizes and validates every agent response and withholds raw confidential or customer-level data unless explicitly approved so that sensitive outputs are controlled before release. | active | contextual | yes | |
| f1e82e412620f843… | For preventing exposure of raw sensitive data, the Fabric-native approach uses Purview DLP so that data loss prevention policies control disclosure. | active | contextual | yes | |
| 4cd9faf231ddc219… | In the Pyrana-on-Fabric approach, dashboards reconcile to Fabric and agent claims are validated against source text before use so that generated outputs remain aligned with approved data and documents. | active | contextual | yes | |
| 6d0e4e5e01993a7c… | For data validation and reconciliation, the Fabric-native approach is characterized as strong so that native Fabric capabilities are considered sufficient in this area. | active | contextual | yes | |
| 33de10ffe662d8f7… | In the Pyrana-on-Fabric approach, metric definitions and business logic are captured as governed Context Units so that agents and humans share a single definition library. | active | contextual | yes | |
| 0feee5f08552b871… | In the Fabric-native approach, metric definitions and business logic documentation are maintained in Fabric and Power BI so that reporting logic remains documented within native analytics tools. | active | contextual | yes | |
| 6cc3b2818d04395a… | In the Pyrana-on-Fabric approach, each AI answer logs the Context-Unit IDs it used, and each unit traces back to the source document, version, approver, and verbatim quote so that AI outputs are fully auditable. | active | contextual | yes | |
| c5181d69b279750b… | For source-to-AI traceability, a Fabric-native solution requires design and build work for each agent so that traceability is implemented individually rather than provided as a common native pattern. | active | contextual | yes | |
| c399c607b1886ca3… | In the Pyrana-on-Fabric approach, source-to-dashboard traceability is preserved for Power BI on Fabric so that existing dashboard lineage remains intact. | active | contextual | yes | |
| 51d7a0f53863d285… | For source-to-dashboard traceability, the Fabric-native approach relies on Purview lineage as a strong capability so that dashboard lineage can be tracked through native governance metadata. | active | contextual | yes | |
| 321b1f7af2a6926c… | In the Pyrana-on-Fabric approach, agents consume the same governed Fabric model and cite metric definitions instead of recomputing them so that business metrics remain consistent with the approved semantic layer. | active | contextual | yes | |
| a369b827486f9336… | For the governed data model and metrics layer, the Fabric-native approach uses the Fabric semantic model as a strong native capability. | active | contextual | yes | |
| bdbd208cba5349e6… | The minimum security, governance, and data integrity requirements include Fabric architecture, workspace structure, RBAC, data-source inventory, governed metrics, validation, traceability, sensitive-data handling, deployment controls, and documentation handoff. | active | contextual | yes | |
| 06f3781f033da669… | In the Pyrana-on-Fabric approach, agent governance includes native telemetry, an evaluation harness, audit logging, and human-in-the-loop approvals so that agent behavior can be tested and monitored with oversight. | active | contextual | yes | |
| f2f177039cf98f6e… | For agent governance, testing, and monitoring, the document identifies native Fabric capabilities as a current gap where Assemble provides agent-level telemetry. | active | contextual | yes | |
| 133d86bff07f9b62… | In the Pyrana-on-Fabric approach, agent and workflow behavior is managed as versioned YAML through GitHub CI/CD using configuration over code so that new use cases can be added without platform changes. | active | contextual | yes | |
| f12cfea039bb0e27… | For production deployment changes, the Fabric-native approach uses Fabric deployment pipelines so that changes are promoted through native Fabric deployment mechanisms. | active | contextual | yes | |
| 30432a97cecd4278… | In the Pyrana-on-Fabric approach, Pyrana inherits labels and adds private inference, no training on customer data, tenant isolation, and an output gateway that enforces never exposing raw confidential data so that sensitive information remains protected during AI use. | active | contextual | yes | |
| 8419f6ccdf3b7a5d… | For sensitive-data protection, the Fabric-native approach uses Purview labels, data loss prevention, and encryption so that confidential information is protected through native Microsoft controls. | active | contextual | yes | |
| b69e914d6c5f3b14… | In the Pyrana-on-Fabric approach, the same Entra ID is used while Pyrana adds per-knowledge-set scoping and need-to-know controls down to the Context-Unit level so that access can be restricted more granularly. | active | contextual | yes | |
| 8162fc9807ef4a32… | For access control, the Fabric-native approach relies on Entra ID combined with Fabric and Purview role-based access control so that permissions are managed through native Microsoft identity and governance services. | active | contextual | yes | |
| 1317adb0f1c7c079… | In the Pyrana-on-Fabric approach, data workflows remain unchanged while Pyrana adds secure document extraction, validation, and storage with provenance and refresh monitoring so that document knowledge is governed separately from raw data pipelines. | active | contextual | yes | |
| f2c0f9978362d9b6… | For ingestion, transformation, refresh, and security, the Fabric-native approach uses Fabric pipelines together with Purview so that operational data workflows remain within native Fabric governance tooling. | active | contextual | yes | |
| 4697ef5775ddde3b… | In the recommended Pyrana-on-Fabric approach, OneLake remains the data store while Pyrana stores only cited claims and provenance in the customer’s Azure tenant so that primary data location is unchanged. | active | contextual | yes | |
| 299c8535bdfe34ab… | In the Fabric-native approach, governed data storage is provided through OneLake so that the platform’s storage location remains under Fabric governance. | active | contextual | yes | |
| b60b4e923ef7f497… | The RFP requires IT to have visibility and approval over data storage, ingestion, transformation, refresh, security, access, sensitive-data protection, production deployment changes, and AI-agent governance, testing, and monitoring. | active | contextual | yes | |