docs: add talk-to-data case study
Add anonymized architecture, governance examples, diagrams, and interview materials.\n\nCo-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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@gabriel.pereira
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cortex_governance_config.yaml
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156
cortex_governance_config.yaml
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# Cortex Analytics Guardrails & Governance Configuration
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# Semantic Layer: Quotation Analysis
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# Purpose: Control data access, freshness, and output quality for LLM consumption
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cortex_policies:
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version: "1.0"
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semantic_model: "sv_quotation"
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# Data freshness contract: How fresh must data be for different use cases
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freshness:
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default_max_age_hours: 24
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rules:
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- use_case: "chatbot_quote_inquiry"
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max_age_hours: 24
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refresh_trigger: "daily_batch_08_00_utc"
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description: "Daily batch refresh sufficient for quote research; cutoff 8am UTC (5am EST)"
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- use_case: "real_time_dashboard"
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max_age_hours: 2
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refresh_trigger: "hourly"
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description: "Sales team dashboard requires 2-hour freshness maximum"
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# Access control: Who can query this semantic model
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access_control:
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default_role: "[REDACTED: SNOWFLAKE_ROLE]"
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authorized_teams:
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- name: "sales_team"
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snowflake_role: "[REDACTED: SALES_ROLE]"
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tables: ["sv_quotation", "sv_chatbot_quotation"]
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max_rows_returned: 100000
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- name: "operations_team"
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snowflake_role: "[REDACTED: OPS_ROLE]"
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tables: ["sv_quotation", "sv_chatbot_quotation"]
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max_rows_returned: 500000
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- name: "cortex_agent"
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snowflake_role: "[REDACTED: CORTEX_AGENT_ROLE]"
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tables: ["sv_chatbot_quotation"]
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max_rows_returned: 1000
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allowed_functions: ["semantic_search", "similarity_score"]
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# LLM output guardrails: Control what the AI model can do with the data
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llm_guardrails:
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- rule: "discount_policy_redaction"
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description: "Hide raw discount values from end users; show only 'within policy' or 'requires approval'"
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pattern: "discount_.*_pct"
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action: "redact_numeric_values"
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replacement_logic: |
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if discount_total_pct <= discount_ceiling_pct then
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"Within policy"
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else
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"Requires manager approval"
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end if
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impact: "User sees governance status, not raw discount rules"
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- rule: "customer_name_masking"
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description: "Mask actual customer names in chatbot responses"
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pattern: "customer_name|cliente_nome"
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action: "mask_value"
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replacement: "[REDACTED: Customer Information]"
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exception: "Sales team in authorized_teams can see unmasked values"
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- rule: "pii_scrubbing"
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description: "Remove personally identifiable information (contact names, emails, phone)"
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pattern: "sales_person|email|phone|contact_name"
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action: "redact"
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exception: "Internal sales team dashboard only"
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- rule: "discount_exception_escalation"
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description: "Flag when chatbot encounters discount above policy ceiling"
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pattern: "requires_exception_approval = 1"
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action: "escalate_to_human"
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notification: "Send to operations team for manual review"
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max_escalations_per_hour: 10
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description: "Prevent bot from auto-approving exceptions"
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# Quality gates: Validation rules before response generation
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quality_gates:
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- gate: "data_completeness"
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check: "All required dimensions present (quote_id, material_id, customer_id)"
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failure_action: "return_error_to_user"
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error_message: "Quote data incomplete. Please provide quote number."
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- gate: "data_staleness"
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check: "Data age < freshness.max_age_hours"
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failure_action: "warn_user"
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warning_message: "Quote data may be up to 24 hours old."
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- gate: "output_relevance"
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check: "Cortex confidence score > 0.7 on semantic match"
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failure_action: "escalate_to_human"
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threshold_score: 0.7
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description: "Only respond if model is confident about context"
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# Cost controls: Prevent runaway LLM usage
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cost_controls:
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monthly_budget_usd: "[REDACTED: BUDGET]"
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alert_threshold_pct: 80
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per_query_max_tokens: 2000
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max_concurrent_queries: 10
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# Audit & compliance: Track all access and LLM decisions
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audit:
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log_level: "full"
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events_logged:
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- "user_query"
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- "data_accessed"
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- "llm_response_generated"
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- "redaction_applied"
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- "exception_escalated"
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retention_days: 90
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compliance_flags:
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- "discount_policy_violation"
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- "unauthorized_access_attempt"
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- "data_freshness_breach"
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semantic_model_lineage:
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description: "How quotation data flows through transformations"
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stages:
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1_source:
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system: "[REDACTED: SAP_SYSTEM]"
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frequency: "Daily batch 05:00 UTC"
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tables: ["[REDACTED: SOURCE_QUOTE_TABLE]", "[REDACTED: SOURCE_DISCOUNT_TABLE]"]
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2_staging:
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schema: "STAGING"
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models: ["stg_quotes.sql", "stg_discount_conditions.sql"]
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transformations: "Column renaming, type casting, null handling"
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3_transform:
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schema: "TRANSFORM"
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models: ["trf_quotation.sql", "trf_chatbot_quotation.sql"]
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transformations: "Discount calculation, portfolio mapping, governance flags"
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4_semantic_layer:
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schema: "DISTRIBUTE_DDL"
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models: ["sv_quotation", "sv_chatbot_quotation"]
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purpose: "Semantic views expose business logic, hide implementation details"
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5_cortex_consumption:
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interface_1: "Snowflake Native App (BI)"
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interface_2: "[REDACTED: SIEMENS_WORKSPACE]"
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llm_model: "[REDACTED: LLM_VERSION]"
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prompt_template: |
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You are a sales support assistant. Answer quote questions using only
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the data provided. If discount exceeds policy, flag for human review.
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Do not disclose raw discount rules.
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notes:
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- "This configuration enforces 'semantic layer first' — governance lives in data, not prompt engineering."
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- "Redaction rules + access control + LLM guardrails work together: one fails, user sees safe fallback."
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- "Cortex policies are version-controlled; audit trail shows all policy changes and who approved them."
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- "Refresh schedule validates data freshness; chatbot rejects queries on stale data."
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