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Natural-language analytics must work alongside an enterprise’s existing data permissions and application management. HENGSHI public materials describe managed metrics, data and row permissions, single sign-on, fine-grained authorization, and Agent-facing permission query, grant, and revocation capabilities. The behavior of row filtering, field protection, masking, audit retention, prompt-injection defenses, and alerts still depends on version, data source, permission model, and project configuration. This article describes verifiable implementation boundaries rather than treating them as default guarantees for every deployment.
1. Why ChatBI creates new questions for security teams
Traditional report, catalog, and application authorization still matter. A natural-language entry point, however, gives users a more flexible way to reach data that is already authorized. Security cannot be assessed from report-directory permissions alone. ChatBI should be tied to specific data packages, applications, and data-permission configurations, and the question-answering result must be validated against those boundaries before go-live.
Common risks include asking for another region’s data, exposing sensitive fields such as phone numbers or incentives in an answer, and trying to alter scope through prompts such as “ignore permission limits.” These are not problems that model wording alone can solve.
2. Control data scope through configuration and verification
HENGSHI supports data permissions in applications and row permissions on datasets to limit visible data. The effective query scope for ChatBI or Data Agent should be determined by the signed-in account, associated data package or application, data-permission mode, and row-permission configuration. Public materials do not define the implementation as appending a WHERE clause to every generated SQL statement, so that internal mechanism should not become an external promise.
Treat permission validation as a prerequisite in the query path. Test data scope, detail, and export behavior with accounts that have no, partial, and complete access. The wording for an out-of-scope question, whether partial results are returned, and how visibility is explained should be confirmed against the current version and project acceptance results.
Organizations, user attributes, and application permissions can all contribute to data scope. Whether transfers and organization changes take effect automatically, and how tenant isolation is modeled, depend on identity synchronization, application configuration, and the integration design. Multi-turn questions, embedded experiences, and bot entries should also be tested to ensure follow-ups, drill-downs, sharing, and exports cannot bypass the established authorization.
3. Manage sensitive fields through the implementation design
Sensitive-field protection must be designed across the data source, dataset fields, application permissions, and presentation mode. Public materials establish application-level data permissions, row permissions, and fine-grained authorization. The exact rules for field-level authorization, column removal, or placeholders must be confirmed for the deployed version, source capability, and project configuration.
Whether phone numbers, identification numbers, and amounts are masked; where masking occurs; and whether detail and exports use different policies are all data-management decisions for the project. If masking is required, configure it across the source, dataset, and consumption entry points and test it with different roles. Do not describe ChatBI, drill-down, and export as automatically sharing one masking policy in every deployment.
Generated answers should not rely on the assumption that a model will never complete sensitive information. A safer design limits retrieval and querying to authorized, governed data and covers sensitive fields, follow-up questions, copy, export, and embedded entries in acceptance testing.
4. Test authorization boundaries for overreach and prompt risk
Natural-language instructions must not change the permission boundary. Data-access limits should be enforced by platform authorization and data-permission configuration. Public information does not prove that one fixed intent-detection-and-rejection algorithm covers every version and entry point, so prompt-injection tests belong in project security validation rather than in a blanket product claim.
When a question crosses into an unauthorized data domain, validate that ChatBI, Data Agent, embedded, and bot paths all honor the configured permissions. The outcome—rejection, a partial result, or a request for permission—depends on version, entry point, and project configuration. Detail views, exports, and sharing should likewise be constrained through application settings and permission design.
5. Logging and traceability depend on deployment and compliance requirements
Question-answering investigations can draw evidence from execution logs, system diagnostics, and log-export capabilities. Permission operations in HENGSHI CLI also support Dry Run and an auditable execution chain. Log fields, retention periods, whether complete conversations are retained, and whether they connect to external observability systems are determined by deployment configuration and compliance requirements; they should not be generalized into one complete Trace for every interaction.
Teams that need to detect frequent overreach attempts or unusual access can connect product logs to their existing security operations and alerting systems, then define rules, thresholds, and response procedures. Strongly regulated industries should design logging and exports against their applicable security, audit, and data-retention requirements.
6. Balance permissions and user experience
Permission design must protect the boundary while helping business users understand the result. Define the feedback and request process for no, partial, and complete access, then test it with real accounts. Semantic governance and authorization should also remain aligned: metrics, field descriptions, data packages, applications, and permissions need coordinated maintenance.
| Governance layer | Public capability foundation | What to validate before go-live |
|---|---|---|
| Business semantics | Metric management and semantic-layer-centered NL2Metrics | Whether metric definitions, field descriptions, and vectorization suit the query scenario |
| Identity and application authorization | SSO, fine-grained access control, application data permissions | Whether user and organization scope matches the authorization design |
| Data scope | Data permissions and dataset row permissions | Whether questions, follow-ups, drill-downs, shares, and exports use the same boundary |
| Sensitive fields | Project-specific management across source, fields, and presentation | Whether masking, visibility, and export policy meet the requirement |
| Agent operations | CLI permission query, revocation, and auditable Dry Run | Whether logging, approvals, alerting, and retention meet operations and compliance needs |
7. Put permissions into the real query and delivery paths
ChatBI adoption should not come at the cost of data security. HENGSHI provides capabilities based on managed metrics, application data permissions, row permissions, SSO, and fine-grained authorization. Each enterprise must still complete configuration and acceptance within its own data sources, organization permissions, embedded entries, and compliance requirements. Reliable security boundaries come from configured least privilege, data-scope control, sensitive-field management, logging, and continuous validation—not from a promise that a natural-language entry point can never cross a boundary.