Database Audit Logger

Overview

Implement database audit logging to track all data modifications (INSERT, UPDATE, DELETE) with full before/after values, user identity, timestamps, and application context. This skill supports trigger-based auditing for PostgreSQL and MySQL, change data capture (CDC) patterns, and application-level audit logging.

Prerequisites

• Database credentials with CREATE TABLE, CREATE FUNCTION, and CREATE TRIGGER permissions
• psql or mysql CLI for executing audit setup DDL
• Understanding of applicable compliance requirements (which tables, which operations, retention period)
• Estimated storage for audit logs: plan for 10-30% of the audited table's data volume per year
• Separate tablespace or storage volume for audit data to prevent audit growth from affecting application performance

Instructions

1. Identify tables requiring audit logging based on compliance and business needs:

   • Tables containing PII (users, contacts, addresses) -- GDPR/HIPAA requirement
   • Tables containing financial data (transactions, payments, invoices) -- SOX/PCI-DSS requirement
   • Tables containing access control data (roles, permissions, API keys) -- security requirement
   • Determine which operations to audit per table: INSERT, UPDATE, DELETE, or all three

2. Create the audit log table with comprehensive metadata:

   CREATE TABLE audit_log (
     id BIGSERIAL PRIMARY KEY,
     table_name VARCHAR(100) NOT NULL,
     record_id TEXT NOT NULL,
     action VARCHAR(10) NOT NULL CHECK (action IN ('INSERT', 'UPDATE', 'DELETE')),
     old_values JSONB,
     new_values JSONB,
     changed_columns TEXT[],
     changed_by VARCHAR(100),
     changed_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
     client_ip INET,
     application_name VARCHAR(100),
     transaction_id BIGINT
   );
   

3. Add indexes for common audit queries:

   • CREATE INDEX idx_audit_table_record ON audit_log (table_name, record_id)
   • CREATE INDEX idx_audit_changed_at ON audit_log (changed_at)
   • CREATE INDEX idx_audit_changed_by ON audit_log (changed_by)
   • CREATE INDEX idx_audit_action ON audit_log (table_name, action)

4. Create the PostgreSQL audit trigger function:

   CREATE OR REPLACE FUNCTION audit_trigger_func() RETURNS TRIGGER AS $$
   BEGIN
     IF TG_OP = 'INSERT' THEN
       INSERT INTO audit_log (table_name, record_id, action, new_values, changed_by, client_ip, application_name, transaction_id)
       VALUES (TG_TABLE_NAME, NEW.id::text, 'INSERT', to_jsonb(NEW), current_setting('app.user', true), inet_client_addr(), current_setting('application_name'), txid_current());
     ELSIF TG_OP = 'UPDATE' THEN
       INSERT INTO audit_log (table_name, record_id, action, old_values, new_values, changed_by, client_ip, application_name, transaction_id)
       VALUES (TG_TABLE_NAME, NEW.id::text, 'UPDATE', to_jsonb(OLD), to_jsonb(NEW), current_setting('app.user', true), inet_client_addr(), current_setting('application_name'), txid_current());
     ELSIF TG_OP = 'DELETE' THEN
       INSERT INTO audit_log (table_name, record_id, action, old_values, changed_by, client_ip, application_name, transaction_id)
       VALUES (TG_TABLE_NAME, OLD.id::text, 'DELETE', to_jsonb(OLD), current_setting('app.user', true), inet_client_addr(), current_setting('application_name'), txid_current());
     END IF;
     RETURN COALESCE(NEW, OLD);
   END;
   $$ LANGUAGE plpgsql;
   

5. Attach triggers to each audited table:

   • CREATE TRIGGER audit_users AFTER INSERT OR UPDATE OR DELETE ON users FOR EACH ROW EXECUTE FUNCTION audit_trigger_func()
   • Repeat for each table requiring audit logging

6. Pass application-level user context to the database session so audit logs capture the actual application user (not just the database role):

   • At the start of each request: SET LOCAL app.user = 'user@example.com'
   • For connection pools, set in the connection checkout hook
   • This value is captured by current_setting('app.user', true) in the trigger

7. Partition the audit_log table by month for efficient querying and archival:

   • CREATE TABLE audit_log (...) PARTITION BY RANGE (changed_at)
   • Create monthly partitions: CREATE TABLE audit_log_2024_01 PARTITION OF audit_log FOR VALUES FROM ('2024-01-01') TO ('2024-02-01')
   • Automate partition creation for future months

8. Protect audit log integrity:

   • Revoke UPDATE and DELETE permissions on audit_log from all application users
   • Grant only INSERT permission to the trigger execution context
   • Consider using pg_audit extension for additional tamper protection
   • Ship audit logs to an external system (SIEM, S3) for independent retention

9. Create compliance report queries:

   • Change history for a record: SELECT * FROM audit_log WHERE table_name = 'users' AND record_id = '12345' ORDER BY changed_at
   • All changes by a user: SELECT * FROM audit_log WHERE changed_by = 'user@example.com' ORDER BY changed_at DESC
   • Bulk operations detection: SELECT changed_by, table_name, action, COUNT(*) FROM audit_log WHERE changed_at > NOW() - INTERVAL '1 hour' GROUP BY 1,2,3 HAVING COUNT(*) > 100
   • Off-hours activity: SELECT * FROM audit_log WHERE EXTRACT(HOUR FROM changed_at) NOT BETWEEN 8 AND 18

10. Set up audit log archival: move audit records older than the retention period to cold storage (S3, Azure Blob). Maintain the archive manifest for retrieval. Typical retention: 1-3 years in database, 7+ years in cold storage for financial data.

Output

• Audit table DDL with proper columns, indexes, and partitioning
• Audit trigger function capturing full before/after values with user context
• Trigger attachment scripts for each audited table
• Compliance report queries for common audit scenarios
• Archival configuration for audit log lifecycle management

Error Handling

Examples

HIPAA-compliant audit logging for a healthcare database: Audit triggers on patient_records, prescriptions, and lab_results tables capture all modifications with practitioner identity. Audit logs are immutable (no UPDATE/DELETE grants), partitioned monthly, and archived to encrypted S3 after 1 year. Quarterly compliance reports show access patterns per practitioner and flag unusual access (patient records accessed without an appointment).

Detecting unauthorized data modifications: Audit log query reveals 500 DELETE operations on the billing table by a service account at 3 AM, outside normal business hours. Alert triggers for bulk operations exceeding 100 rows. Investigation traces the operations to a misconfigured cleanup job. Audit log provides the complete list of deleted records for restoration.

GDPR data access request fulfillment: When a user requests their data access log under GDPR Article 15, the audit system provides a complete history of who accessed or modified their personal data: SELECT changed_by, action, changed_at, changed_columns FROM audit_log WHERE table_name = 'users' AND record_id = '12345' ORDER BY changed_at. The report is generated within the 30-day compliance window.

Resources

• PostgreSQL triggers: https://www.postgresql.org/docs/current/plpgsql-trigger.html
• pgAudit extension: https://www.pgaudit.org/
• MySQL audit log plugin: https://dev.mysql.com/doc/refman/8.0/en/audit-log.html
• GDPR data processing records: https://gdpr-info.eu/art-30-gdpr/
• SOX compliance for databases: https://www.postgresql.org/docs/current/pgaudit.html