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-->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003ESafeguarding sensitive data at scale is nonnegotiable for regulatory compliance. In this essential hands-on lab you will master the art of automated sensitive data discovery and protection. You will leverage the power of AI to classify PII across your data estate, generate robust data protection policies, explore the Trust Center Data Security UI to verify your governance posture, establish an airtight audit trail, and use Cortex Code governance skills to assess maturity and build policies in plain English.\u003C/p\u003E\n","\u003Cp\u003EYou'll work through a step-by-step guide using a synthetic customer dataset containing names, SSNs, emails, credit cards, phone numbers, and order transactions — a realistic representation of sensitive data in enterprise systems.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThree personas, one dataset:\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPersona\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERole\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EResponsibility\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EData Engineer\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EHRZN_DATA_ENGINEER\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreates and loads data assets; explores RBAC\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EData Governor\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EHRZN_DATA_GOVERNOR\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EClassifies data; defines and applies all policies\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIT Admin\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EHRZN_IT_ADMIN\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAudits access history and compliance trail\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with SQL\u003C/li\u003E\u003Cli\u003EBasic understanding of data governance concepts\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECore Governance:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAutomate PII discovery by setting up Automatic Sensitive Data Classification using a CLASSIFICATION_PROFILE and custom tag mapping\u003C/li\u003E\u003Cli\u003EUse the Trust Center Data Security UI to verify classification results and generate Sensitive Data Entitlement Reports — no SQL required\u003C/li\u003E\u003Cli\u003EProtect sensitive data with multi-type tag-based masking policies that scale automatically to derived tables\u003C/li\u003E\u003Cli\u003EApply geographic row access policies for defense-in-depth\u003C/li\u003E\u003Cli\u003EUse aggregation and projection policies to prevent individual record access\u003C/li\u003E\u003Cli\u003EEstablish a complete audit trail with Access History and column-level lineage\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAI-Powered Governance:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERedact PII from free-form text with AI_REDACT\u003C/li\u003E\u003Cli\u003EUse Cortex Code governance skills to assess governance maturity, create best-practice policies, and run compliance audits in plain English\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake\u003C/a\u003E account with ACCOUNTADMIN access (trial accounts work)\u003C/li\u003E\u003Cli\u003EApproximately 90 minutes to complete all steps\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA complete data governance framework with RBAC roles for three personas\u003C/li\u003E\u003Cli\u003EA custom 5-tier data classification taxonomy (PII → RESTRICTED → SENSITIVE → INTERNAL → PUBLIC)\u003C/li\u003E\u003Cli\u003ETag-based masking policies (STRING, NUMBER, DATE, TIMESTAMP) that auto-apply to all tagged columns\u003C/li\u003E\u003Cli\u003EConsent-based and geographic row access policies\u003C/li\u003E\u003Cli\u003ETrust Center Data Security dashboard with Sensitive Data Entitlement reporting\u003C/li\u003E\u003Cli\u003EPrivacy-safe analytics on unstructured customer feedback text\u003C/li\u003E\u003Cli\u003ECortex Code skill-generated compliance reports and best-practice policy recommendations\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EKey Features in This Lab\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EFeature\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ECLASSIFICATION_PROFILE\u003C/strong\u003E with tag_map\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAuto-tag 50+ PII types using your custom taxonomy\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EDATA_CLASSIFICATION\u003C/strong\u003E tag with PROPAGATE\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETags flow automatically to all derived tables\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMulti-type tag-based masking\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOne policy per data type; auto-applied to all tagged columns\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERow access policies\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EConsent-based and geographic row filtering\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETrust Center Data Security\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENo-SQL dashboard for classification results and entitlement reporting\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAI_REDACT\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERemove PII from free-form text for safe ML and analytics\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex Code governance skills\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPlain-English governance: maturity scoring, policy creation, audit\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESource of Truth:\u003C/strong\u003E The full SQL scripts for this lab are in the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/advanced-data-governance-summit-hol/assets\"\u003EGitHub repository\u003C/a\u003E. This guide provides narrative context; the SQL files are the canonical source for execution.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ERun the Setup Script\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EScript:\u003C/strong\u003E \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/advanced-data-governance-summit-hol/assets/0-lab-setup.sql\"\u003E0-lab-setup.sql\u003C/a\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, navigate to \u003Cstrong\u003EProjects > Workspaces\u003C/strong\u003E, create a new SQL worksheet named \u003Ccode\u003E0_lab_setup\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ECopy the contents of \u003Ccode\u003E0-lab-setup.sql\u003C/code\u003E into your worksheet\u003C/li\u003E\u003Cli\u003ERun all statements as \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EWhat the Setup Creates\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EObject\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EHRZN_DATA_ENGINEER\u003C/code\u003E role\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreates and loads data assets\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EHRZN_DATA_GOVERNOR\u003C/code\u003E role\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EClassification, masking policies, governance\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EHRZN_DATA_USER\u003C/code\u003E role\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERestricted analyst — sees masked data, MA state only\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EHRZN_IT_ADMIN\u003C/code\u003E role\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAudits access history and compliance\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EHRZN_WH\u003C/code\u003E warehouse\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECompute for all lab steps\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EHRZN_DB\u003C/code\u003E database\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESchemas: \u003Ccode\u003EHRZN_SCH\u003C/code\u003E, \u003Ccode\u003ETAG_SCHEMA\u003C/code\u003E, \u003Ccode\u003ECLASSIFIERS\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ECUSTOMER\u003C/code\u003E table\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1,000 rows of synthetic customer PII\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ECUSTOMER_ORDERS\u003C/code\u003E table\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOrder transactions linked to customers\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EROW_POLICY_MAP\u003C/code\u003E table\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMaps roles to visible states (MA for \u003Ccode\u003EHRZN_DATA_USER\u003C/code\u003E)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETrust Center grants\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ETRUST_CENTER_ADMIN\u003C/code\u003E, \u003Ccode\u003EDATA_SECURITY_ADMIN\u003C/code\u003E for \u003Ccode\u003EHRZN_DATA_GOVERNOR\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex Code skills grants\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EGOVERNANCE_VIEWER\u003C/code\u003E, \u003Ccode\u003ESECURITY_VIEWER\u003C/code\u003E for \u003Ccode\u003EACCOUNT_USAGE\u003C/code\u003E access\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EKey Setup Concepts\u003C/h3\u003E\n","\u003Cp\u003EThe setup script demonstrates Snowflake's \u003Cstrong\u003ERole-Based Access Control (RBAC)\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECustom roles are created and assigned to \u003Ccode\u003ESYSADMIN\u003C/code\u003E in the hierarchy\u003C/li\u003E\u003Cli\u003EEach role gets the minimum privileges needed for its persona\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EAPPLY TAG\u003C/code\u003E, \u003Ccode\u003EAPPLY MASKING POLICY\u003C/code\u003E, \u003Ccode\u003EAPPLY ROW ACCESS POLICY\u003C/code\u003E on account allow the governor to define and attach policies without object ownership\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAccess Control\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EScript:\u003C/strong\u003E \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/advanced-data-governance-summit-hol/assets/1-access-control.sql\"\u003E1-access-control.sql\u003C/a\u003E | Role: \u003Ccode\u003EHRZN_DATA_ENGINEER\u003C/code\u003E\u003C/p\u003E\n","\u003Ch3\u003ERBAC and DAC Fundamentals\u003C/h3\u003E\n","\u003Cp\u003ESnowflake access control combines:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDiscretionary Access Control (DAC):\u003C/strong\u003E Object owners grant access to their objects\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERole-Based Access Control (RBAC):\u003C/strong\u003E Privileges are assigned to roles; roles are assigned to users\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAccess is denied by default in Snowflake.\u003C/strong\u003E Every privilege must be explicitly granted. Without \u003Ccode\u003EUSAGE\u003C/code\u003E on the database, querying any object inside it returns: \u003Ccode\u003E"Database 'HRZN_DB' does not exist or not authorized."\u003C/code\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EThe Complete Privilege Grant Pattern\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- 1. Create the role (USERADMIN)\nCREATE OR REPLACE ROLE HRZN_DATA_ANALYST COMMENT = 'Analyst role';\n\n-- 2. Grant warehouse access (SECURITYADMIN)\nGRANT USAGE ON WAREHOUSE HRZN_WH TO ROLE HRZN_DATA_ANALYST;\n\n-- 3. Grant database and schema access\nGRANT USAGE ON DATABASE HRZN_DB TO ROLE HRZN_DATA_ANALYST;\nGRANT USAGE ON ALL SCHEMAS IN DATABASE HRZN_DB TO ROLE HRZN_DATA_ANALYST;\n\n-- 4. Grant object-level privileges\nGRANT SELECT ON ALL TABLES IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_ANALYST;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EExplore the Raw Data\u003C/h3\u003E\n","\u003Cp\u003EAfter completing this step you will observe that the CUSTOMER table exposes 15 columns of PII — SSN, email, credit card, phone, birthdate — with no masking. This is the problem Step 2 solves.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Takeaways:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAll Snowflake access is deny-by-default; every privilege requires an explicit GRANT\u003C/li\u003E\u003Cli\u003ERole hierarchy means a role inherits all privileges of roles below it\u003C/li\u003E\u003Cli\u003EThe raw CUSTOMER table exposes PII to anyone with SELECT — Step 2 fixes this\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EKnow and Protect Your Data\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EScript:\u003C/strong\u003E \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/advanced-data-governance-summit-hol/assets/2-classification-and-policies.sql\"\u003E2-classification-and-policies.sql\u003C/a\u003E | Role: \u003Ccode\u003EHRZN_DATA_GOVERNOR\u003C/code\u003E\u003C/p\u003E\n","\u003Ch3\u003EClassification with the BYOT Pattern\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003EBYOT (Bring Your Own Tags)\u003C/strong\u003E pattern maps detected system categories to your organization's custom taxonomy. Create the enterprise DATA_CLASSIFICATION tag with propagation enabled:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TAG HRZN_DB.TAG_SCHEMA.DATA_CLASSIFICATION\n    ALLOWED_VALUES 'PII', 'RESTRICTED', 'SENSITIVE', 'INTERNAL', 'PUBLIC'\n    PROPAGATE = ON_DEPENDENCY_AND_DATA_MOVEMENT;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPROPAGATE = ON_DEPENDENCY_AND_DATA_MOVEMENT\u003C/strong\u003E automatically flows this tag to tables created via CTAS, INSERT...SELECT, and views — downstream tables inherit governance automatically.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ECreate a Classification Profile mapping detected categories to your custom tag values:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE SNOWFLAKE.DATA_PRIVACY.CLASSIFICATION_PROFILE\n    HRZN_DB.HRZN_SCH.HRZN_STANDARD_CLASSIFICATION_PROFILE({\n    'auto_tag': true,\n    'tag_map': {\n      'column_tag_map': [\n        { 'tag_name': 'HRZN_DB.TAG_SCHEMA.DATA_CLASSIFICATION',\n          'tag_value': 'PII',\n          'semantic_categories': ['EMAIL', 'US_SOCIAL_SECURITY_NUMBER', 'CREDIT_CARD_NUMBER'] },\n        { 'tag_name': 'HRZN_DB.TAG_SCHEMA.DATA_CLASSIFICATION',\n          'tag_value': 'RESTRICTED',\n          'semantic_categories': ['PHONE_NUMBER', 'DATE_OF_BIRTH'] }\n      ]\n    }\n});\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun classification and test the profile before enabling auto-classification:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL SYSTEM$CLASSIFY(\n    'HRZN_DB.HRZN_SCH.CUSTOMER',\n    'HRZN_DB.HRZN_SCH.HRZN_STANDARD_CLASSIFICATION_PROFILE'\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEnable auto-classification by attaching classification profile at the database level:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER DATABASE HRZN_DB\n    SET CLASSIFICATION_PROFILE = 'HRZN_DB.HRZN_SCH.HRZN_STANDARD_CLASSIFICATION_PROFILE';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETag-Based Masking Policies (Multi-Type)\u003C/h3\u003E\n","\u003Cp\u003ECreate one masking policy per data type and attach all to the DATA_CLASSIFICATION tag. Any column tagged automatically gets the correct masking applied:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPolicy\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EData Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPII\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERESTRICTED\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESENSITIVE\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EDATA_CLASSIFICATION_MASK_STRING\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVARCHAR\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E***PII-REDACTED***\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E***-1234\u003C/code\u003E (last 4)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESHA2 hash\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EDATA_CLASSIFICATION_MASK_NUMBER\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENUMBER\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ENULL\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERounded\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EABS(HASH)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EDATA_CLASSIFICATION_MASK_DATE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDATE\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ENULL\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYear only\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMonth only\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EDATA_CLASSIFICATION_MASK_TIMESTAMP\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETIMESTAMP\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ENULL\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDay only\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMonth only\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Attach all policies to the DATA_CLASSIFICATION tag\nALTER TAG HRZN_DB.TAG_SCHEMA.DATA_CLASSIFICATION\n    SET MASKING POLICY HRZN_DB.TAG_SCHEMA.DATA_CLASSIFICATION_MASK_STRING;\n-- Repeat for NUMBER, DATE, TIMESTAMP\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Benefit:\u003C/strong\u003E Tag a column → masking is applied immediately, for the correct data type, automatically.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ERow Access Policies\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EConsent-based (opt-in) policy:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE ROW ACCESS POLICY HRZN_DB.TAG_SCHEMA.CUSTOMER_OPTIN_POLICY\n    AS (OPTIN_STATUS STRING) RETURNS BOOLEAN ->\n    CASE\n        WHEN CURRENT_ROLE() IN ('HRZN_DATA_GOVERNOR') THEN TRUE\n        WHEN CURRENT_ROLE() = 'HRZN_DATA_USER' AND OPTIN_STATUS = 'Y' THEN TRUE\n        ELSE FALSE\n    END;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EState-based (geographic) policy using a mapping table:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE ROW ACCESS POLICY HRZN_DB.TAG_SCHEMA.CUSTOMER_STATE_RESTRICTIONS\n    AS (STATE STRING) RETURNS BOOLEAN ->\n       CURRENT_ROLE() IN ('ACCOUNTADMIN', 'HRZN_DATA_GOVERNOR')\n        OR EXISTS (\n            SELECT 1 FROM HRZN_DB.TAG_SCHEMA.ROW_POLICY_MAP rp\n            WHERE rp.ROLE = CURRENT_ROLE() AND rp.STATE_VISIBILITY = STATE\n        );\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDefense-in-Depth:\u003C/strong\u003E Row access policies control WHO sees WHICH records. Masking policies control HOW data appears when visible. Together they provide layered protection.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EAggregation and Projection Policies\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Aggregation: force non-admin users to group by ≥100 rows\nCREATE OR REPLACE AGGREGATION POLICY HRZN_DB.TAG_SCHEMA.AGGREGATION_POLICY\n    AS () RETURNS AGGREGATION_CONSTRAINT ->\n    CASE\n        WHEN CURRENT_ROLE() IN ('HRZN_DATA_GOVERNOR') THEN NO_AGGREGATION_CONSTRAINT()\n        ELSE AGGREGATION_CONSTRAINT(MIN_GROUP_SIZE => 100)\n    END;\n\n-- Projection: block ZIP from appearing in SELECT output (but allow it in WHERE)\nCREATE OR REPLACE PROJECTION POLICY HRZN_DB.TAG_SCHEMA.PROJECTION_POLICY\n    AS () RETURNS PROJECTION_CONSTRAINT ->\n    CASE\n        WHEN CURRENT_ROLE() IN ('HRZN_DATA_GOVERNOR') THEN PROJECTION_CONSTRAINT(ALLOW => true)\n        ELSE PROJECTION_CONSTRAINT(ALLOW => false)\n    END;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETag Propagation to Derived Tables\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE TABLE HRZN_DB.HRZN_SCH.CUSTOMER_COPY AS\nSELECT * FROM HRZN_DB.HRZN_SCH.CUSTOMER;\n-- DATA_CLASSIFICATION tags propagate automatically\n-- Masking policies apply to CUSTOMER_COPY without any manual configuration\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Takeaways:\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPolicy Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EControls\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EUse Case\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EMasking\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHOW data appears\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHide PII from analysts\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ERow Access\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWHICH rows visible\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGeographic or consent filtering\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAggregation\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EQuery type allowed\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EForce aggregate-only access\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EProjection\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EColumn projectability\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBlock specific columns in SELECT\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cul\u003E\u003Cli\u003ETag propagation scales governance to thousands of derived tables automatically\u003C/li\u003E\u003Cli\u003ETag-based masking scales better than per-column policies — add a table, just tag the columns\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy we tackle redaction and Cortex Code Skills next:\u003C/strong\u003E The Trust Center Data Security dashboard (Step 5) and the Audit Trail step (Step 6) both rely on \u003Ccode\u003ESNOWFLAKE.ACCOUNT_USAGE\u003C/code\u003E views, which can have up to a few hours of latency before newly-classified objects and recent queries appear. To use that wait time productively, the lab now explores AI_REDACT for unstructured PII (Step 3) and the Cortex Code governance skills (Step 4) first, then resumes Trust Center verification and the audit trail review afterward — by which point the \u003Ccode\u003EACCOUNT_USAGE\u003C/code\u003E data should be ready.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAI_REDACT for Unstructured PII\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EScript:\u003C/strong\u003E \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/advanced-data-governance-summit-hol/assets/3-ai-redact.sql\"\u003E3-ai-redact.sql\u003C/a\u003E | Role: \u003Ccode\u003EHRZN_DATA_GOVERNOR\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003ESteps 1–2 protect \u003Cstrong\u003Estructured columns\u003C/strong\u003E. But customer feedback, support tickets, and survey responses are free-form text with PII embedded anywhere. AI_REDACT handles this automatically.\u003C/p\u003E\n","\u003Ch3\u003EThe Problem: PII in Free-Form Text\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E"Customer John Smith called from 555-123-4567. Email: john.smith@email.com"\n"Customer Sarah Williams mentioned her SSN 123-45-6789 was visible on invoice."\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EColumn masking cannot help here — the PII is embedded in a text string, not a typed column.\u003C/p\u003E\n","\u003Ch3\u003EAI_REDACT: Automatic PII Removal\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    original_feedback,\n    SNOWFLAKE.CORTEX.AI_REDACT(original_feedback) AS redacted_feedback\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS LIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EResult:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EORIGINAL: "Customer John Smith called from 555-123-4567. Email: john.smith@email.com"\nREDACTED: "Customer [NAME] called from [PHONE_NUMBER]. Email: [EMAIL]"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAI_REDACT detects and replaces 50+ PII types including [NAME], [EMAIL], [PHONE_NUMBER], [US_SOCIAL_SECURITY_NUMBER], [STREET_ADDRESS] — no regex patterns required.\u003C/p\u003E\n","\u003Ch3\u003ESelective Redaction\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Redact only names and emails; preserve phone numbers\nSNOWFLAKE.CORTEX.AI_REDACT(text, ['NAME', 'EMAIL'])\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EPre-Compute + Secure View Pattern\u003C/h3\u003E\n","\u003Cp\u003ERunning AI_REDACT on every query is expensive. Best practice:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- 1. Create a redacted table once (batch job)\nCREATE TABLE HRZN_DB.HRZN_SCH.CUSTOMER_FEEDBACK_REDACTED AS\nSELECT ORDER_ID,\n    CUSTOMER_FEEDBACK AS original_feedback,\n    SNOWFLAKE.CORTEX.AI_REDACT(CUSTOMER_FEEDBACK) AS redacted_feedback\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS WHERE CUSTOMER_FEEDBACK IS NOT NULL LIMIT 100;\n\n-- 2. Secure view switches columns based on role\nCREATE SECURE VIEW HRZN_DB.HRZN_SCH.CUSTOMER_FEEDBACK_SECURE AS\nSELECT ORDER_ID,\n    CASE WHEN CURRENT_ROLE() IN ('HRZN_DATA_GOVERNOR') THEN original_feedback\n         ELSE redacted_feedback\n    END AS CUSTOMER_FEEDBACK\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_FEEDBACK_REDACTED;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EPrivacy-Safe Sentiment Analysis\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT redacted_feedback,\n    SNOWFLAKE.CORTEX.SENTIMENT(redacted_feedback) AS sentiment_score\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_FEEDBACK_REDACTED;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETag Propagation:\u003C/strong\u003E CUSTOMER_FEEDBACK_REDACTED automatically inherits DATA_CLASSIFICATION tags from CUSTOMER_ORDERS because of the propagation setting from Step 2.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Takeaways:\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EData Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EGovernance Approach\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStructured columns (email, SSN, phone)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EClassification + tag-based masking (Step 2)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUnstructured text (feedback, notes)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAI_REDACT (Step 3)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EBoth approaches are complementary — together they provide complete PII coverage.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECortex Code Skills\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EScript:\u003C/strong\u003E \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/advanced-data-governance-summit-hol/assets/4-cortex-code-governance-skills.sql\"\u003E4-cortex-code-governance-skills.sql\u003C/a\u003E | Role: \u003Ccode\u003EHRZN_DATA_GOVERNOR\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003ECortex Code is Snowflake's AI-powered coding assistant. Its built-in data governance skills let you accomplish governance tasks in plain English — no SQL required.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOpen Cortex Code:\u003C/strong\u003E Click the \u003Cstrong\u003E✨ Cortex Code\u003C/strong\u003E icon in the Snowsight left navigation sidebar.\u003C/p\u003E\n","\u003Ch3\u003ESkill Domains Used in This Lab\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESkill\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Does\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGeneral Data Governance\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAudit access, compliance posture, role hierarchy, tag/policy inventory\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESensitive Data Classification\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EScan for PII, analyze results, create profiles and custom classifiers\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EData Protection Policies\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreate/audit masking and row access policies with best practices\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003E4.1 — Governance Maturity Assessment\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E"What is the governance coverage for HRZN_DB?\n Which tables have PII but no masking policy applied?"\n\n"Generate a data governance health report for HRZN_DB.\n Include: tables with sensitive data, policy coverage percentage, and top risks."\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex Code queries DATA_CLASSIFICATION_LATEST and POLICY_REFERENCES and returns a synthesized health summary with a governance maturity grade.\u003C/p\u003E\n","\u003Ch3\u003E4.2 — AI Classification via Skills\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E"Scan HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS for PII and sensitive data."\n\n"Which tables in HRZN_DB need re-classification because their results are older than 30 days?"\n\n"Help me create a custom classifier for employee IDs matching the pattern EMP-XXXXX."\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex Code generates and executes SYSTEM$CLASSIFY calls automatically, then explains the results in plain English.\u003C/p\u003E\n","\u003Ch3\u003E4.3 — Policy Creation and Audit\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E"Audit all masking policies in HRZN_DB.TAG_SCHEMA. Are there anti-patterns?"\n\n"Create a GDPR-compliant masking policy for the SSN column in HRZN_DB.HRZN_SCH.CUSTOMER."\n\n"What is the recommended ABAC pattern for masking in Snowflake?"\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAnti-Pattern Teaching Moment:\u003C/strong\u003E The masking policies created in Step 2 use \u003Ccode\u003ECURRENT_ROLE()\u003C/code\u003E. Cortex Code will flag this: \u003Ccode\u003ECURRENT_ROLE()\u003C/code\u003E only evaluates the primary active role, while \u003Ccode\u003EIS_ROLE_IN_SESSION()\u003C/code\u003E evaluates all active roles including secondary roles. Best practice is always \u003Ccode\u003EIS_ROLE_IN_SESSION()\u003C/code\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ECortex Code generates complete, runnable \u003Ccode\u003ECREATE MASKING POLICY\u003C/code\u003E SQL using Snowflake best practices. Review the generated SQL before applying it to production tables.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003E4.4 — Compliance Audit\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode\u003E"Who has accessed HRZN_DB.HRZN_SCH.CUSTOMER in the last 30 days?"\n\n"Were there any queries on HRZN_DB sensitive tables outside of business hours?"\n\n"Which roles have the APPLY MASKING POLICY privilege on my account?"\n\n"Show the role hierarchy for HRZN_DATA_GOVERNOR."\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEach prompt generates SQL that queries ACCOUNT_USAGE automatically.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Takeaways:\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESkill\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EKey Value\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMaturity Assessment\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EScores governance coverage without SQL expertise\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EClassification\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERuns SYSTEM$CLASSIFY, explains detections in plain English\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPolicy Creation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGenerates best-practice policies; flags CURRENT_ROLE() anti-pattern\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECompliance Audit\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAnswers access audit questions from ACCOUNT_USAGE via natural language\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECortex Code Tips:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUse fully-qualified names: \u003Ccode\u003EDATABASE.SCHEMA.TABLE\u003C/code\u003E for the most accurate results\u003C/li\u003E\u003Cli\u003EStart broad (\u003Ccode\u003E"health report for HRZN_DB"\u003C/code\u003E) then drill into specific issues\u003C/li\u003E\u003Cli\u003EIf \u003Ccode\u003EACCOUNT_USAGE\u003C/code\u003E is not yet populated, tell Cortex Code: \u003Ccode\u003E"Check INFORMATION_SCHEMA as ACCOUNT_USAGE may not be populated yet"\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETrust Center Verification\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EScript:\u003C/strong\u003E \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/advanced-data-governance-summit-hol/assets/5-trust-center-data-security.sql\"\u003E5-trust-center-data-security.sql\u003C/a\u003E | Role: \u003Ccode\u003EHRZN_DATA_GOVERNOR\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003EThe Trust Center \u003Cstrong\u003EData Security\u003C/strong\u003E tab (GA: April 2026) provides a consolidated, no-SQL view of your sensitive data posture.\u003C/p\u003E\n","\u003Ch3\u003ENavigate the Dashboard\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENavigation:\u003C/strong\u003E Snowsight → \u003Cstrong\u003EGovernance & Security\u003C/strong\u003E → \u003Cstrong\u003ETrust Center\u003C/strong\u003E → \u003Cstrong\u003EData Security\u003C/strong\u003E tab\u003C/p\u003E\n","\u003Cp\u003EThe dashboard shows:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESensitive Objects\u003C/strong\u003E count — tables and columns with detected PII, PCI, or PHI\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPII / PCI / PHI breakdown\u003C/strong\u003E tiles\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EObjects that need review\u003C/strong\u003E — pending classification decisions\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPolicy coverage\u003C/strong\u003E — percentage of sensitive objects with masking policies\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EVerify Classification Results via SQL\u003C/h3\u003E\n","\u003Cp\u003ERun the Information Schema query for immediate results (no ACCOUNT_USAGE latency). See the SQL script for the full set of queries including the ACCOUNT_USAGE alternative.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E ACCOUNT_USAGE views have up to 3-hour latency. Use \u003Ccode\u003EINFORMATION_SCHEMA.TAG_REFERENCES_ALL_COLUMNS\u003C/code\u003E for immediate verification.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EObjects That Need Review (UI Walkthrough)\u003C/h3\u003E\n","\u003Cp\u003ESelect \u003Cstrong\u003E"Objects that need review"\u003C/strong\u003E to open the review workflow:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EInspect each column's \u003Cstrong\u003ECLASSIFICATION CATEGORY\u003C/strong\u003E | \u003Cstrong\u003ETAGS\u003C/strong\u003E | \u003Cstrong\u003ESAMPLE VALUES\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EAccept, change, or remove AI-generated recommendations\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E"Save and apply tags to selected tables"\u003C/strong\u003E to commit\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESensitive Data Entitlement Report\u003C/h3\u003E\n","\u003Cp\u003EThe Entitlement Report answers: \u003Cstrong\u003E"Who can access my sensitive data?"\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EEnable in UI:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ETrust Center\u003C/strong\u003E → \u003Cstrong\u003EData Security\u003C/strong\u003E → \u003Cstrong\u003ESettings\u003C/strong\u003E tab\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EReporting\u003C/strong\u003E section → \u003Cstrong\u003ESensitive Data Entitlement Report\u003C/strong\u003E → \u003Cstrong\u003EEnable\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EDaily\u003C/strong\u003E cadence → \u003Cstrong\u003EEnable report\u003C/strong\u003E → \u003Cstrong\u003ERun now\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuery the results:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT DISTINCT USER_NAME, ROLE_NAME, TABLE_NAME, PRIVILEGE\nFROM SNOWFLAKE.DATA_SECURITY.ENTITLEMENT_REPORT\nWHERE RUN_ID = (\n    SELECT RUN_ID FROM SNOWFLAKE.DATA_SECURITY.ENTITLEMENT_REPORT\n    ORDER BY CREATED_ON DESC LIMIT 1\n)\nORDER BY USER_NAME, TABLE_NAME;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Takeaways:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETrust Center provides a compliance dashboard without requiring SQL knowledge\u003C/li\u003E\u003Cli\u003EThe Entitlement Report is essential for "who has access" audits required by GDPR, HIPAA, and SOX\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAccess and Audit Trail\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003EScript:\u003C/strong\u003E \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/advanced-data-governance-summit-hol/assets/6-audit-trail.sql\"\u003E6-audit-trail.sql\u003C/a\u003E | Role: \u003Ccode\u003EHRZN_IT_ADMIN\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003EAccess History (SNOWFLAKE.ACCOUNT_USAGE.ACCESS_HISTORY) tracks every query that touched your data — what was read, what was written, when, and by whom.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E Access History has up to 3-hour latency. Some queries may return empty results immediately after lab steps. This is expected; plan for this latency in production compliance reporting.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EQuery Count and Read/Write Patterns\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Access count by object\nSELECT value:"objectName"::STRING AS object_name,\n    COUNT(DISTINCT query_id) AS number_of_queries\nFROM SNOWFLAKE.ACCOUNT_USAGE.ACCESS_HISTORY,\nLATERAL FLATTEN(input => direct_objects_accessed)\nWHERE object_name ILIKE 'HRZN%'\nGROUP BY object_name ORDER BY number_of_queries DESC;\n\n-- Read vs. write breakdown\nSELECT value:"objectName"::STRING AS object_name,\n    CASE WHEN object_modified_by_ddl IS NOT NULL THEN 'write' ELSE 'read' END AS query_type,\n    COUNT(DISTINCT query_id) AS number_of_queries,\n    MAX(query_start_time) AS last_access\nFROM SNOWFLAKE.ACCOUNT_USAGE.ACCESS_HISTORY,\nLATERAL FLATTEN(input => direct_objects_accessed)\nWHERE object_name ILIKE 'HRZN%'\nGROUP BY object_name, query_type;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESensitive Data Column-Level Lineage\u003C/h3\u003E\n","\u003Cp\u003EThe script includes a column-level lineage query that traces how tagged columns (PII, RESTRICTED, SENSITIVE) flow from source tables through INSERT/CTAS operations into derived objects. This proves to auditors that PII was not copied to unauthorized locations and confirms tag propagation covered all derived paths.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Takeaways:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003EDIRECT_OBJECTS_ACCESSED\u003C/code\u003E: objects named explicitly in the query\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EBASE_OBJECTS_ACCESSED\u003C/code\u003E: objects actually read (including through views)\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EOBJECTS_MODIFIED\u003C/code\u003E: objects written (INSERT, CTAS, DDL)\u003C/li\u003E\u003Cli\u003EAccess History is the foundation for GDPR data subject access reports, SOX audit logs, and HIPAA access audits\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've completed the Advanced Data Governance Summit HOL.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETopic\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EFeature\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EStep\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAccess Control\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERBAC, privilege grants, deny-by-default\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStep 1\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EClassification\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECLASSIFICATION_PROFILE, BYOT tag_map\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStep 2\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMasking\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMulti-type tag-based masking with auto-propagation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStep 2\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERow Access\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EConsent-based and geographic row filtering\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStep 2\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAdvanced Policies\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAggregation and projection policies\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStep 2\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUnstructured PII\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAI_REDACT, pre-compute + secure view pattern\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStep 3\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex Code Skills\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMaturity score, policy creation, compliance audit\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStep 4\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETrust Center UI\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EData Security dashboard, Entitlement Report\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStep 5\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAudit Trail\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAccess History, column-level PII lineage\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStep 6\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EClean Up\u003C/h3\u003E\n","\u003Cp\u003ERun \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/advanced-data-governance-summit-hol/assets/99-teardown.sql\"\u003E99-teardown.sql\u003C/a\u003E to remove all lab objects from your account.\u003C/p\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-horizon\"\u003ESnowflake Horizon Catalog\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/classify-intro\"\u003EIntroduction to Sensitive Data Classification\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/classify-ui-trust-center\"\u003ETrust Center Data Security\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/security-column-ddm-intro\"\u003EMasking Policies\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/security-row-intro\"\u003ERow Access Policies\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/ai_redact\"\u003EAI_REDACT Function\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/governance-skills\"\u003ECortex Code Governance Skills\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/classify-ui-trust-center#sensitive-data-entitlement-report\"\u003ESensitive Data Entitlement Report\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/advanced-data-governance-summit-hol/assets\"\u003ELab GitHub Repository\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Advanced Data Governance: AI-Powered Sensitive Data Discovery and Protection at Scale","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nSafeguarding sensitive data at scale is nonnegotiable for regulatory compliance. In this essential hands-on lab you will master the art of automated sensitive data discovery and protection. You will leverage the power of AI to classify PII across your data estate, generate robust data protection policies, explore the Trust Center Data Security UI to verify your governance posture, establish an airtight audit trail, and use Cortex Code governance skills to assess maturity and build policies in plain English.\n\nYou'll work through a step-by-step guide using a synthetic customer dataset containing names, SSNs, emails, credit cards, phone numbers, and order transactions — a realistic representation of sensitive data in enterprise systems.\n\n**Three personas, one dataset:**\n\n| Persona | Role | Responsibility |\n|---------|------|----------------|\n| Data Engineer | `HRZN_DATA_ENGINEER` | Creates and loads data assets; explores RBAC |\n| Data Governor | `HRZN_DATA_GOVERNOR` | Classifies data; defines and applies all policies |\n| IT Admin | `HRZN_IT_ADMIN` | Audits access history and compliance trail |\n\n### Prerequisites\n- Familiarity with SQL\n- Basic understanding of data governance concepts\n\n### What You'll Learn\n\n**Core Governance:**\n- Automate PII discovery by setting up Automatic Sensitive Data Classification using a CLASSIFICATION_PROFILE and custom tag mapping\n- Use the Trust Center Data Security UI to verify classification results and generate Sensitive Data Entitlement Reports — no SQL required\n- Protect sensitive data with multi-type tag-based masking policies that scale automatically to derived tables\n- Apply geographic row access policies for defense-in-depth\n- Use aggregation and projection policies to prevent individual record access\n- Establish a complete audit trail with Access History and column-level lineage\n\n**AI-Powered Governance:**\n- Redact PII from free-form text with AI_REDACT\n- Use Cortex Code governance skills to assess governance maturity, create best-practice policies, and run compliance audits in plain English\n\n### What You'll Need\n- A [Snowflake](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) account with ACCOUNTADMIN access (trial accounts work)\n- Approximately 90 minutes to complete all steps\n\n### What You'll Build\n- A complete data governance framework with RBAC roles for three personas\n- A custom 5-tier data classification taxonomy (PII → RESTRICTED → SENSITIVE → INTERNAL → PUBLIC)\n- Tag-based masking policies (STRING, NUMBER, DATE, TIMESTAMP) that auto-apply to all tagged columns\n- Consent-based and geographic row access policies\n- Trust Center Data Security dashboard with Sensitive Data Entitlement reporting\n- Privacy-safe analytics on unstructured customer feedback text\n- Cortex Code skill-generated compliance reports and best-practice policy recommendations\n\n### Key Features in This Lab\n\n| Feature | Description |\n|---------|-------------|\n| **CLASSIFICATION_PROFILE** with tag_map | Auto-tag 50+ PII types using your custom taxonomy |\n| **DATA_CLASSIFICATION** tag with PROPAGATE | Tags flow automatically to all derived tables |\n| Multi-type tag-based masking | One policy per data type; auto-applied to all tagged columns |\n| Row access policies | Consent-based and geographic row filtering |\n| Trust Center Data Security | No-SQL dashboard for classification results and entitlement reporting |\n| **AI_REDACT** | Remove PII from free-form text for safe ML and analytics |\n| Cortex Code governance skills | Plain-English governance: maturity scoring, policy creation, audit |\n\n\u003C!-- ------------------------ --\u003E\n## Setup\n\n\u003E **Source of Truth:** The full SQL scripts for this lab are in the [GitHub repository](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/advanced-data-governance-summit-hol/assets). This guide provides narrative context; the SQL files are the canonical source for execution.\n\n### Run the Setup Script\n\n**Script:** [0-lab-setup.sql](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/advanced-data-governance-summit-hol/assets/0-lab-setup.sql)\n\n1. In Snowsight, navigate to **Projects \u003E Workspaces**, create a new SQL worksheet named `0_lab_setup`\n2. Copy the contents of `0-lab-setup.sql` into your worksheet\n3. Run all statements as `ACCOUNTADMIN`\n\n### What the Setup Creates\n\n| Object | Purpose |\n|--------|---------|\n| `HRZN_DATA_ENGINEER` role | Creates and loads data assets |\n| `HRZN_DATA_GOVERNOR` role | Classification, masking policies, governance |\n| `HRZN_DATA_USER` role | Restricted analyst — sees masked data, MA state only |\n| `HRZN_IT_ADMIN` role | Audits access history and compliance |\n| `HRZN_WH` warehouse | Compute for all lab steps |\n| `HRZN_DB` database | Schemas: `HRZN_SCH`, `TAG_SCHEMA`, `CLASSIFIERS` |\n| `CUSTOMER` table | 1,000 rows of synthetic customer PII |\n| `CUSTOMER_ORDERS` table | Order transactions linked to customers |\n| `ROW_POLICY_MAP` table | Maps roles to visible states (MA for `HRZN_DATA_USER`) |\n| Trust Center grants | `TRUST_CENTER_ADMIN`, `DATA_SECURITY_ADMIN` for `HRZN_DATA_GOVERNOR` |\n| Cortex Code skills grants | `GOVERNANCE_VIEWER`, `SECURITY_VIEWER` for `ACCOUNT_USAGE` access |\n\n### Key Setup Concepts\n\nThe setup script demonstrates Snowflake's **Role-Based Access Control (RBAC)**:\n- Custom roles are created and assigned to `SYSADMIN` in the hierarchy\n- Each role gets the minimum privileges needed for its persona\n- `APPLY TAG`, `APPLY MASKING POLICY`, `APPLY ROW ACCESS POLICY` on account allow the governor to define and attach policies without object ownership\n\n\u003C!-- ------------------------ --\u003E\n## Access Control\n\n**Script:** [1-access-control.sql](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/advanced-data-governance-summit-hol/assets/1-access-control.sql) | Role: `HRZN_DATA_ENGINEER`\n\n### RBAC and DAC Fundamentals\n\nSnowflake access control combines:\n- **Discretionary Access Control (DAC):** Object owners grant access to their objects\n- **Role-Based Access Control (RBAC):** Privileges are assigned to roles; roles are assigned to users\n\n\u003E **Access is denied by default in Snowflake.** Every privilege must be explicitly granted. Without `USAGE` on the database, querying any object inside it returns: `\"Database 'HRZN_DB' does not exist or not authorized.\"`\n\n### The Complete Privilege Grant Pattern\n\n```sql\n-- 1. Create the role (USERADMIN)\nCREATE OR REPLACE ROLE HRZN_DATA_ANALYST COMMENT = 'Analyst role';\n\n-- 2. Grant warehouse access (SECURITYADMIN)\nGRANT USAGE ON WAREHOUSE HRZN_WH TO ROLE HRZN_DATA_ANALYST;\n\n-- 3. Grant database and schema access\nGRANT USAGE ON DATABASE HRZN_DB TO ROLE HRZN_DATA_ANALYST;\nGRANT USAGE ON ALL SCHEMAS IN DATABASE HRZN_DB TO ROLE HRZN_DATA_ANALYST;\n\n-- 4. Grant object-level privileges\nGRANT SELECT ON ALL TABLES IN SCHEMA HRZN_DB.HRZN_SCH TO ROLE HRZN_DATA_ANALYST;\n```\n\n### Explore the Raw Data\n\nAfter completing this step you will observe that the CUSTOMER table exposes 15 columns of PII — SSN, email, credit card, phone, birthdate — with no masking. This is the problem Step 2 solves.\n\n**Key Takeaways:**\n- All Snowflake access is deny-by-default; every privilege requires an explicit GRANT\n- Role hierarchy means a role inherits all privileges of roles below it\n- The raw CUSTOMER table exposes PII to anyone with SELECT — Step 2 fixes this\n\n\u003C!-- ------------------------ --\u003E\n## Know and Protect Your Data\n\n**Script:** [2-classification-and-policies.sql](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/advanced-data-governance-summit-hol/assets/2-classification-and-policies.sql) | Role: `HRZN_DATA_GOVERNOR`\n\n### Classification with the BYOT Pattern\n\nThe **BYOT (Bring Your Own Tags)** pattern maps detected system categories to your organization's custom taxonomy. Create the enterprise DATA_CLASSIFICATION tag with propagation enabled:\n\n```sql\nCREATE OR REPLACE TAG HRZN_DB.TAG_SCHEMA.DATA_CLASSIFICATION\n    ALLOWED_VALUES 'PII', 'RESTRICTED', 'SENSITIVE', 'INTERNAL', 'PUBLIC'\n    PROPAGATE = ON_DEPENDENCY_AND_DATA_MOVEMENT;\n```\n\n\u003E **PROPAGATE = ON_DEPENDENCY_AND_DATA_MOVEMENT** automatically flows this tag to tables created via CTAS, INSERT...SELECT, and views — downstream tables inherit governance automatically.\n\nCreate a Classification Profile mapping detected categories to your custom tag values:\n\n```sql\nCREATE SNOWFLAKE.DATA_PRIVACY.CLASSIFICATION_PROFILE\n    HRZN_DB.HRZN_SCH.HRZN_STANDARD_CLASSIFICATION_PROFILE({\n    'auto_tag': true,\n    'tag_map': {\n      'column_tag_map': [\n        { 'tag_name': 'HRZN_DB.TAG_SCHEMA.DATA_CLASSIFICATION',\n          'tag_value': 'PII',\n          'semantic_categories': ['EMAIL', 'US_SOCIAL_SECURITY_NUMBER', 'CREDIT_CARD_NUMBER'] },\n        { 'tag_name': 'HRZN_DB.TAG_SCHEMA.DATA_CLASSIFICATION',\n          'tag_value': 'RESTRICTED',\n          'semantic_categories': ['PHONE_NUMBER', 'DATE_OF_BIRTH'] }\n      ]\n    }\n});\n```\n\nRun classification and test the profile before enabling auto-classification:\n```sql\nCALL SYSTEM$CLASSIFY(\n    'HRZN_DB.HRZN_SCH.CUSTOMER',\n    'HRZN_DB.HRZN_SCH.HRZN_STANDARD_CLASSIFICATION_PROFILE'\n);\n```\n\nEnable auto-classification by attaching classification profile at the database level:\n```sql\nALTER DATABASE HRZN_DB\n    SET CLASSIFICATION_PROFILE = 'HRZN_DB.HRZN_SCH.HRZN_STANDARD_CLASSIFICATION_PROFILE';\n```\n\n### Tag-Based Masking Policies (Multi-Type)\n\nCreate one masking policy per data type and attach all to the DATA_CLASSIFICATION tag. Any column tagged automatically gets the correct masking applied:\n\n| Policy | Data Type | PII | RESTRICTED | SENSITIVE |\n|--------|-----------|-----|------------|-----------|\n| `DATA_CLASSIFICATION_MASK_STRING` | VARCHAR | `***PII-REDACTED***` | `***-1234` (last 4) | SHA2 hash |\n| `DATA_CLASSIFICATION_MASK_NUMBER` | NUMBER | `NULL` | Rounded | ABS(HASH) |\n| `DATA_CLASSIFICATION_MASK_DATE` | DATE | `NULL` | Year only | Month only |\n| `DATA_CLASSIFICATION_MASK_TIMESTAMP` | TIMESTAMP | `NULL` | Day only | Month only |\n\n```sql\n-- Attach all policies to the DATA_CLASSIFICATION tag\nALTER TAG HRZN_DB.TAG_SCHEMA.DATA_CLASSIFICATION\n    SET MASKING POLICY HRZN_DB.TAG_SCHEMA.DATA_CLASSIFICATION_MASK_STRING;\n-- Repeat for NUMBER, DATE, TIMESTAMP\n```\n\n\u003E **Key Benefit:** Tag a column → masking is applied immediately, for the correct data type, automatically.\n\n### Row Access Policies\n\n**Consent-based (opt-in) policy:**\n```sql\nCREATE OR REPLACE ROW ACCESS POLICY HRZN_DB.TAG_SCHEMA.CUSTOMER_OPTIN_POLICY\n    AS (OPTIN_STATUS STRING) RETURNS BOOLEAN -\u003E\n    CASE\n        WHEN CURRENT_ROLE() IN ('HRZN_DATA_GOVERNOR') THEN TRUE\n        WHEN CURRENT_ROLE() = 'HRZN_DATA_USER' AND OPTIN_STATUS = 'Y' THEN TRUE\n        ELSE FALSE\n    END;\n```\n\n**State-based (geographic) policy using a mapping table:**\n```sql\nCREATE OR REPLACE ROW ACCESS POLICY HRZN_DB.TAG_SCHEMA.CUSTOMER_STATE_RESTRICTIONS\n    AS (STATE STRING) RETURNS BOOLEAN -\u003E\n       CURRENT_ROLE() IN ('ACCOUNTADMIN', 'HRZN_DATA_GOVERNOR')\n        OR EXISTS (\n            SELECT 1 FROM HRZN_DB.TAG_SCHEMA.ROW_POLICY_MAP rp\n            WHERE rp.ROLE = CURRENT_ROLE() AND rp.STATE_VISIBILITY = STATE\n        );\n```\n\n\u003E **Defense-in-Depth:** Row access policies control WHO sees WHICH records. Masking policies control HOW data appears when visible. Together they provide layered protection.\n\n### Aggregation and Projection Policies\n\n```sql\n-- Aggregation: force non-admin users to group by ≥100 rows\nCREATE OR REPLACE AGGREGATION POLICY HRZN_DB.TAG_SCHEMA.AGGREGATION_POLICY\n    AS () RETURNS AGGREGATION_CONSTRAINT -\u003E\n    CASE\n        WHEN CURRENT_ROLE() IN ('HRZN_DATA_GOVERNOR') THEN NO_AGGREGATION_CONSTRAINT()\n        ELSE AGGREGATION_CONSTRAINT(MIN_GROUP_SIZE =\u003E 100)\n    END;\n\n-- Projection: block ZIP from appearing in SELECT output (but allow it in WHERE)\nCREATE OR REPLACE PROJECTION POLICY HRZN_DB.TAG_SCHEMA.PROJECTION_POLICY\n    AS () RETURNS PROJECTION_CONSTRAINT -\u003E\n    CASE\n        WHEN CURRENT_ROLE() IN ('HRZN_DATA_GOVERNOR') THEN PROJECTION_CONSTRAINT(ALLOW =\u003E true)\n        ELSE PROJECTION_CONSTRAINT(ALLOW =\u003E false)\n    END;\n```\n\n### Tag Propagation to Derived Tables\n\n```sql\nCREATE TABLE HRZN_DB.HRZN_SCH.CUSTOMER_COPY AS\nSELECT * FROM HRZN_DB.HRZN_SCH.CUSTOMER;\n-- DATA_CLASSIFICATION tags propagate automatically\n-- Masking policies apply to CUSTOMER_COPY without any manual configuration\n```\n\n**Key Takeaways:**\n\n| Policy Type | Controls | Use Case |\n|-------------|----------|---------|\n| **Masking** | HOW data appears | Hide PII from analysts |\n| **Row Access** | WHICH rows visible | Geographic or consent filtering |\n| **Aggregation** | Query type allowed | Force aggregate-only access |\n| **Projection** | Column projectability | Block specific columns in SELECT |\n\n- Tag propagation scales governance to thousands of derived tables automatically\n- Tag-based masking scales better than per-column policies — add a table, just tag the columns\n\n\u003E **Why we tackle redaction and Cortex Code Skills next:** The Trust Center Data Security dashboard (Step 5) and the Audit Trail step (Step 6) both rely on `SNOWFLAKE.ACCOUNT_USAGE` views, which can have up to a few hours of latency before newly-classified objects and recent queries appear. To use that wait time productively, the lab now explores AI_REDACT for unstructured PII (Step 3) and the Cortex Code governance skills (Step 4) first, then resumes Trust Center verification and the audit trail review afterward — by which point the `ACCOUNT_USAGE` data should be ready.\n\n\u003C!-- ------------------------ --\u003E\n## AI_REDACT for Unstructured PII\n\n**Script:** [3-ai-redact.sql](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/advanced-data-governance-summit-hol/assets/3-ai-redact.sql) | Role: `HRZN_DATA_GOVERNOR`\n\nSteps 1–2 protect **structured columns**. But customer feedback, support tickets, and survey responses are free-form text with PII embedded anywhere. AI_REDACT handles this automatically.\n\n### The Problem: PII in Free-Form Text\n\n```\n\"Customer John Smith called from 555-123-4567. Email: john.smith@email.com\"\n\"Customer Sarah Williams mentioned her SSN 123-45-6789 was visible on invoice.\"\n```\n\nColumn masking cannot help here — the PII is embedded in a text string, not a typed column.\n\n### AI_REDACT: Automatic PII Removal\n\n```sql\nSELECT\n    original_feedback,\n    SNOWFLAKE.CORTEX.AI_REDACT(original_feedback) AS redacted_feedback\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS LIMIT 5;\n```\n\n**Result:**\n```\nORIGINAL: \"Customer John Smith called from 555-123-4567. Email: john.smith@email.com\"\nREDACTED: \"Customer [NAME] called from [PHONE_NUMBER]. Email: [EMAIL]\"\n```\n\nAI_REDACT detects and replaces 50+ PII types including [NAME], [EMAIL], [PHONE_NUMBER], [US_SOCIAL_SECURITY_NUMBER], [STREET_ADDRESS] — no regex patterns required.\n\n### Selective Redaction\n\n```sql\n-- Redact only names and emails; preserve phone numbers\nSNOWFLAKE.CORTEX.AI_REDACT(text, ['NAME', 'EMAIL'])\n```\n\n### Pre-Compute + Secure View Pattern\n\nRunning AI_REDACT on every query is expensive. Best practice:\n\n```sql\n-- 1. Create a redacted table once (batch job)\nCREATE TABLE HRZN_DB.HRZN_SCH.CUSTOMER_FEEDBACK_REDACTED AS\nSELECT ORDER_ID,\n    CUSTOMER_FEEDBACK AS original_feedback,\n    SNOWFLAKE.CORTEX.AI_REDACT(CUSTOMER_FEEDBACK) AS redacted_feedback\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS WHERE CUSTOMER_FEEDBACK IS NOT NULL LIMIT 100;\n\n-- 2. Secure view switches columns based on role\nCREATE SECURE VIEW HRZN_DB.HRZN_SCH.CUSTOMER_FEEDBACK_SECURE AS\nSELECT ORDER_ID,\n    CASE WHEN CURRENT_ROLE() IN ('HRZN_DATA_GOVERNOR') THEN original_feedback\n         ELSE redacted_feedback\n    END AS CUSTOMER_FEEDBACK\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_FEEDBACK_REDACTED;\n```\n\n### Privacy-Safe Sentiment Analysis\n\n```sql\nSELECT redacted_feedback,\n    SNOWFLAKE.CORTEX.SENTIMENT(redacted_feedback) AS sentiment_score\nFROM HRZN_DB.HRZN_SCH.CUSTOMER_FEEDBACK_REDACTED;\n```\n\n\u003E **Tag Propagation:** CUSTOMER_FEEDBACK_REDACTED automatically inherits DATA_CLASSIFICATION tags from CUSTOMER_ORDERS because of the propagation setting from Step 2.\n\n**Key Takeaways:**\n\n| Data Type | Governance Approach |\n|-----------|-------------------|\n| Structured columns (email, SSN, phone) | Classification + tag-based masking (Step 2) |\n| Unstructured text (feedback, notes) | AI_REDACT (Step 3) |\n\nBoth approaches are complementary — together they provide complete PII coverage.\n\n\u003C!-- ------------------------ --\u003E\n## Cortex Code Skills\n\n**Script:** [4-cortex-code-governance-skills.sql](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/advanced-data-governance-summit-hol/assets/4-cortex-code-governance-skills.sql) | Role: `HRZN_DATA_GOVERNOR`\n\nCortex Code is Snowflake's AI-powered coding assistant. Its built-in data governance skills let you accomplish governance tasks in plain English — no SQL required.\n\n**Open Cortex Code:** Click the **✨ Cortex Code** icon in the Snowsight left navigation sidebar.\n\n### Skill Domains Used in This Lab\n\n| Skill | What It Does |\n|-------|-------------|\n| General Data Governance | Audit access, compliance posture, role hierarchy, tag/policy inventory |\n| Sensitive Data Classification | Scan for PII, analyze results, create profiles and custom classifiers |\n| Data Protection Policies | Create/audit masking and row access policies with best practices |\n\n### 4.1 — Governance Maturity Assessment\n\n```\n\"What is the governance coverage for HRZN_DB?\n Which tables have PII but no masking policy applied?\"\n\n\"Generate a data governance health report for HRZN_DB.\n Include: tables with sensitive data, policy coverage percentage, and top risks.\"\n```\n\nCortex Code queries DATA_CLASSIFICATION_LATEST and POLICY_REFERENCES and returns a synthesized health summary with a governance maturity grade.\n\n### 4.2 — AI Classification via Skills\n\n```\n\"Scan HRZN_DB.HRZN_SCH.CUSTOMER_ORDERS for PII and sensitive data.\"\n\n\"Which tables in HRZN_DB need re-classification because their results are older than 30 days?\"\n\n\"Help me create a custom classifier for employee IDs matching the pattern EMP-XXXXX.\"\n```\n\nCortex Code generates and executes SYSTEM$CLASSIFY calls automatically, then explains the results in plain English.\n\n### 4.3 — Policy Creation and Audit\n\n```\n\"Audit all masking policies in HRZN_DB.TAG_SCHEMA. Are there anti-patterns?\"\n\n\"Create a GDPR-compliant masking policy for the SSN column in HRZN_DB.HRZN_SCH.CUSTOMER.\"\n\n\"What is the recommended ABAC pattern for masking in Snowflake?\"\n```\n\n\u003E **Anti-Pattern Teaching Moment:** The masking policies created in Step 2 use `CURRENT_ROLE()`. Cortex Code will flag this: `CURRENT_ROLE()` only evaluates the primary active role, while `IS_ROLE_IN_SESSION()` evaluates all active roles including secondary roles. Best practice is always `IS_ROLE_IN_SESSION()`.\n\n\u003E Cortex Code generates complete, runnable `CREATE MASKING POLICY` SQL using Snowflake best practices. Review the generated SQL before applying it to production tables.\n\n### 4.4 — Compliance Audit\n\n```\n\"Who has accessed HRZN_DB.HRZN_SCH.CUSTOMER in the last 30 days?\"\n\n\"Were there any queries on HRZN_DB sensitive tables outside of business hours?\"\n\n\"Which roles have the APPLY MASKING POLICY privilege on my account?\"\n\n\"Show the role hierarchy for HRZN_DATA_GOVERNOR.\"\n```\n\nEach prompt generates SQL that queries ACCOUNT_USAGE automatically.\n\n**Key Takeaways:**\n\n| Skill | Key Value |\n|-------|-----------|\n| Maturity Assessment | Scores governance coverage without SQL expertise |\n| Classification | Runs SYSTEM$CLASSIFY, explains detections in plain English |\n| Policy Creation | Generates best-practice policies; flags CURRENT_ROLE() anti-pattern |\n| Compliance Audit | Answers access audit questions from ACCOUNT_USAGE via natural language |\n\n**Cortex Code Tips:**\n- Use fully-qualified names: `DATABASE.SCHEMA.TABLE` for the most accurate results\n- Start broad (`\"health report for HRZN_DB\"`) then drill into specific issues\n- If `ACCOUNT_USAGE` is not yet populated, tell Cortex Code: `\"Check INFORMATION_SCHEMA as ACCOUNT_USAGE may not be populated yet\"`\n\n\u003C!-- ------------------------ --\u003E\n## Trust Center Verification\n\n**Script:** [5-trust-center-data-security.sql](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/advanced-data-governance-summit-hol/assets/5-trust-center-data-security.sql) | Role: `HRZN_DATA_GOVERNOR`\n\nThe Trust Center **Data Security** tab (GA: April 2026) provides a consolidated, no-SQL view of your sensitive data posture.\n\n### Navigate the Dashboard\n\n**Navigation:** Snowsight → **Governance & Security** → **Trust Center** → **Data Security** tab\n\nThe dashboard shows:\n- **Sensitive Objects** count — tables and columns with detected PII, PCI, or PHI\n- **PII / PCI / PHI breakdown** tiles\n- **Objects that need review** — pending classification decisions\n- **Policy coverage** — percentage of sensitive objects with masking policies\n\n### Verify Classification Results via SQL\n\nRun the Information Schema query for immediate results (no ACCOUNT_USAGE latency). See the SQL script for the full set of queries including the ACCOUNT_USAGE alternative.\n\n\u003E **Note:** ACCOUNT_USAGE views have up to 3-hour latency. Use `INFORMATION_SCHEMA.TAG_REFERENCES_ALL_COLUMNS` for immediate verification.\n\n### Objects That Need Review (UI Walkthrough)\n\nSelect **\"Objects that need review\"** to open the review workflow:\n- Inspect each column's **CLASSIFICATION CATEGORY** | **TAGS** | **SAMPLE VALUES**\n- Accept, change, or remove AI-generated recommendations\n- Click **\"Save and apply tags to selected tables\"** to commit\n\n### Sensitive Data Entitlement Report\n\nThe Entitlement Report answers: **\"Who can access my sensitive data?\"**\n\n**Enable in UI:**\n1. **Trust Center** → **Data Security** → **Settings** tab\n2. **Reporting** section → **Sensitive Data Entitlement Report** → **Enable**\n3. Select **Daily** cadence → **Enable report** → **Run now**\n\n**Query the results:**\n```sql\nSELECT DISTINCT USER_NAME, ROLE_NAME, TABLE_NAME, PRIVILEGE\nFROM SNOWFLAKE.DATA_SECURITY.ENTITLEMENT_REPORT\nWHERE RUN_ID = (\n    SELECT RUN_ID FROM SNOWFLAKE.DATA_SECURITY.ENTITLEMENT_REPORT\n    ORDER BY CREATED_ON DESC LIMIT 1\n)\nORDER BY USER_NAME, TABLE_NAME;\n```\n\n**Key Takeaways:**\n- Trust Center provides a compliance dashboard without requiring SQL knowledge\n- The Entitlement Report is essential for \"who has access\" audits required by GDPR, HIPAA, and SOX\n\n\u003C!-- ------------------------ --\u003E\n## Access and Audit Trail\n\n**Script:** [6-audit-trail.sql](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/advanced-data-governance-summit-hol/assets/6-audit-trail.sql) | Role: `HRZN_IT_ADMIN`\n\nAccess History (SNOWFLAKE.ACCOUNT_USAGE.ACCESS_HISTORY) tracks every query that touched your data — what was read, what was written, when, and by whom.\n\n\u003E **Important:** Access History has up to 3-hour latency. Some queries may return empty results immediately after lab steps. This is expected; plan for this latency in production compliance reporting.\n\n### Query Count and Read/Write Patterns\n\n```sql\n-- Access count by object\nSELECT value:\"objectName\"::STRING AS object_name,\n    COUNT(DISTINCT query_id) AS number_of_queries\nFROM SNOWFLAKE.ACCOUNT_USAGE.ACCESS_HISTORY,\nLATERAL FLATTEN(input =\u003E direct_objects_accessed)\nWHERE object_name ILIKE 'HRZN%'\nGROUP BY object_name ORDER BY number_of_queries DESC;\n\n-- Read vs. write breakdown\nSELECT value:\"objectName\"::STRING AS object_name,\n    CASE WHEN object_modified_by_ddl IS NOT NULL THEN 'write' ELSE 'read' END AS query_type,\n    COUNT(DISTINCT query_id) AS number_of_queries,\n    MAX(query_start_time) AS last_access\nFROM SNOWFLAKE.ACCOUNT_USAGE.ACCESS_HISTORY,\nLATERAL FLATTEN(input =\u003E direct_objects_accessed)\nWHERE object_name ILIKE 'HRZN%'\nGROUP BY object_name, query_type;\n```\n\n### Sensitive Data Column-Level Lineage\n\nThe script includes a column-level lineage query that traces how tagged columns (PII, RESTRICTED, SENSITIVE) flow from source tables through INSERT/CTAS operations into derived objects. This proves to auditors that PII was not copied to unauthorized locations and confirms tag propagation covered all derived paths.\n\n**Key Takeaways:**\n- `DIRECT_OBJECTS_ACCESSED`: objects named explicitly in the query\n- `BASE_OBJECTS_ACCESSED`: objects actually read (including through views)\n- `OBJECTS_MODIFIED`: objects written (INSERT, CTAS, DDL)\n- Access History is the foundation for GDPR data subject access reports, SOX audit logs, and HIPAA access audits\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nCongratulations! You've completed the Advanced Data Governance Summit HOL.\n\n### What You Learned\n\n| Topic | Feature | Step |\n|-------|---------|------|\n| Access Control | RBAC, privilege grants, deny-by-default | Step 1 |\n| Classification | CLASSIFICATION_PROFILE, BYOT tag_map | Step 2 |\n| Masking | Multi-type tag-based masking with auto-propagation | Step 2 |\n| Row Access | Consent-based and geographic row filtering | Step 2 |\n| Advanced Policies | Aggregation and projection policies | Step 2 |\n| Unstructured PII | AI_REDACT, pre-compute + secure view pattern | Step 3 |\n| Cortex Code Skills | Maturity score, policy creation, compliance audit | Step 4 |\n| Trust Center UI | Data Security dashboard, Entitlement Report | Step 5 |\n| Audit Trail | Access History, column-level PII lineage | Step 6 |\n\n### Clean Up\n\nRun [99-teardown.sql](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/advanced-data-governance-summit-hol/assets/99-teardown.sql) to remove all lab objects from your account.\n\n### Related Resources\n- [Snowflake Horizon Catalog](https://docs.snowflake.com/en/user-guide/snowflake-horizon)\n- [Introduction to Sensitive Data Classification](https://docs.snowflake.com/en/user-guide/classify-intro)\n- [Trust Center Data Security](https://docs.snowflake.com/en/user-guide/classify-ui-trust-center)\n- [Masking Policies](https://docs.snowflake.com/en/user-guide/security-column-ddm-intro)\n- [Row Access Policies](https://docs.snowflake.com/en/user-guide/security-row-intro)\n- [AI_REDACT Function](https://docs.snowflake.com/en/sql-reference/functions/ai_redact)\n- [Cortex Code Governance Skills](https://docs.snowflake.com/en/user-guide/governance-skills)\n- [Sensitive Data Entitlement Report](https://docs.snowflake.com/en/user-guide/classify-ui-trust-center#sensitive-data-entitlement-report)\n- [Lab GitHub Repository](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/advanced-data-governance-summit-hol/assets)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-748ea8ac63","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-fb30301826",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-61df676bd7","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-06-02",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-f26bb88959","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake 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