Databricks Security Basics

Overview

Implement Databricks security: secret scopes for credential storage, token rotation, least-privilege access via Unity Catalog grants, and security auditing via system tables. Secrets API uses

PUT /api/2.0/secrets/put

Prerequisites

• Databricks CLI configured
• Workspace admin access (for secret scope creation)
• Unity Catalog enabled

Instructions

Step 1: Create and Manage Secret Scopes

# Create a Databricks-backed secret scope
databricks secrets create-scope my-app-secrets

# Create Azure Key Vault-backed scope (Azure only)
databricks secrets create-scope azure-kv \
  --scope-backend-type AZURE_KEYVAULT \
  --resource-id "/subscriptions/<sub>/resourceGroups/<rg>/providers/Microsoft.KeyVault/vaults/<vault>" \
  --dns-name "https://<vault>.vault.azure.net/"

# List all scopes
databricks secrets list-scopes

Step 2: Store and Access Secrets

# Store a secret (prompts for value interactively)
databricks secrets put-secret my-app-secrets db-password

# Store from CLI argument
databricks secrets put-secret my-app-secrets api-key --string-value "sk_live_abc123"

# List secrets (values always hidden)
databricks secrets list-secrets my-app-secrets

# Access secrets in notebooks and jobs — values auto-redacted in output
db_password = dbutils.secrets.get(scope="my-app-secrets", key="db-password")
api_key = dbutils.secrets.get(scope="my-app-secrets", key="api-key")

# Printing shows [REDACTED] — Databricks prevents accidental exposure
print(f"Password: {db_password}")  # Output: Password: [REDACTED]

# Use in JDBC connections
jdbc_url = f"jdbc:postgresql://host:5432/db?user=app&password={db_password}"
df = spark.read.format("jdbc").option("url", jdbc_url).load()

Step 3: Secret Scope Access Control

# Grant READ to a user
databricks secrets put-acl my-app-secrets user@company.com READ

# Grant MANAGE to a group (full control)
databricks secrets put-acl my-app-secrets data-engineers MANAGE

# List ACLs for a scope
databricks secrets list-acls my-app-secrets

Step 4: Token Audit and Rotation

from databricks.sdk import WorkspaceClient
from datetime import datetime

w = WorkspaceClient()

def audit_tokens() -> list[dict]:
    """Audit all PATs for expiration and rotation needs."""
    findings = []
    for token in w.tokens.list():
        created = datetime.fromtimestamp(token.creation_time / 1000)
        expiry = datetime.fromtimestamp(token.expiry_time / 1000) if token.expiry_time else None

        finding = {
            "token_id": token.token_id,
            "comment": token.comment,
            "created": created.isoformat(),
            "expires": expiry.isoformat() if expiry else "NEVER",
            "days_until_expiry": (expiry - datetime.now()).days if expiry else None,
        }

        if not expiry:
            finding["risk"] = "HIGH — no expiration set"
        elif (expiry - datetime.now()).days < 30:
            finding["risk"] = "MEDIUM — expires within 30 days"
        else:
            finding["risk"] = "LOW"

        findings.append(finding)
    return findings

def rotate_token(old_token_id: str, lifetime_days: int = 90) -> str:
    """Create new token and delete old one."""
    new = w.tokens.create(
        comment=f"Rotated {datetime.now().isoformat()}",
        lifetime_seconds=lifetime_days * 86400,
    )
    w.tokens.delete(token_id=old_token_id)
    return new.token_value  # Store this immediately — shown only once

for finding in audit_tokens():
    print(f"{finding['comment']}: {finding['risk']} (expires {finding['expires']})")

Step 5: Unity Catalog Least Privilege

-- Grant minimal access per role
-- Engineers: read/write bronze+silver, read gold
GRANT USAGE ON CATALOG analytics TO `data-engineers`;
GRANT CREATE, MODIFY, SELECT ON SCHEMA analytics.bronze TO `data-engineers`;
GRANT CREATE, MODIFY, SELECT ON SCHEMA analytics.silver TO `data-engineers`;
GRANT SELECT ON SCHEMA analytics.gold TO `data-engineers`;

-- Analysts: read-only on curated gold tables
GRANT USAGE ON CATALOG analytics TO `data-analysts`;
GRANT SELECT ON SCHEMA analytics.gold TO `data-analysts`;

-- Audit current grants
SHOW GRANTS ON SCHEMA analytics.gold;
SHOW GRANTS `data-analysts` ON CATALOG analytics;

Step 6: Column-Level Masking and Row-Level Security

-- Mask email for non-privileged users
CREATE OR REPLACE FUNCTION analytics.gold.mask_email(email STRING)
  RETURN IF(IS_ACCOUNT_GROUP_MEMBER('data-engineers'), email,
            REGEXP_REPLACE(email, '(.).*@', '$1***@'));

ALTER TABLE analytics.gold.customers ALTER COLUMN email
  SET MASK analytics.gold.mask_email;

-- Row-level security: restrict by department
CREATE OR REPLACE FUNCTION analytics.gold.dept_filter(dept STRING)
  RETURN IF(IS_ACCOUNT_GROUP_MEMBER('data-admins'), true,
            dept = session_user_department());

ALTER TABLE analytics.gold.sales
  SET ROW FILTER analytics.gold.dept_filter ON (department);

Step 7: Security Audit via System Tables

-- Recent permission changes (last 7 days)
SELECT event_time, user_identity.email AS actor,
       action_name, request_params
FROM system.access.audit
WHERE action_name IN ('grantPermission', 'revokePermission',
                       'changeJobPermissions', 'changeClusterPermissions')
  AND event_date >= current_date() - 7
ORDER BY event_time DESC;

-- Failed authentication attempts
SELECT event_time, user_identity.email, source_ip_address,
       response.error_message
FROM system.access.audit
WHERE action_name = 'tokenLogin' AND response.status_code != 200
  AND event_date >= current_date() - 7
ORDER BY event_time DESC;

Output

• Secret scopes with ACL-based access control
• Token audit report identifying expiring/non-expiring tokens
• Unity Catalog grants enforcing least privilege by role
• Column masking and row-level security on sensitive tables
• Audit queries for ongoing security monitoring

Error Handling

audit_tokens()

NEVER

SHOW GRANTS

tokenLogin

Examples

Security Checklist

• All PATs have expiration dates (max 90 days)
• Secrets stored in Databricks Secret Scopes, not env vars
• No hardcoded credentials in notebooks or repos
• Service principals for all automated workflows
• Unity Catalog enforcing least privilege
• Column masking on PII fields
• IP access lists configured (Admin Console > Workspace Settings)
• Cluster policies restrict instance types and auto-termination
• Audit log queries scheduled for weekly review

Resources

• Secret Management
• Unity Catalog Security
• Row and Column Filters
• Audit Logs

Next Steps

For production deployment, see

databricks-prod-checklist