In AWS Lake Formation, effective governance is crucial for managing data lakes securely and efficiently. Here are best practices focusing on schema evolution, audit logging, and compliance:

### Schema Evolution

1. **Version Control**: Implement version control for your schemas. Use a consistent versioning strategy to track schema changes over time. This can help manage schema evolution effectively and roll back to previous versions if necessary.

2. **Backward Compatibility**: Ensure that schema changes are backward-compatible. This includes strategies like having default values for new fields and avoiding changes that break existing integrations.

3. **Schema Registry**: Use a centralized schema registry. While Lake Formation doesn’t provide this feature out-of-the-box, you can utilize AWS Glue to maintain a catalog of schema versions, making it easier to manage changes across different datasets.

4. **Automated Testing**: Implement automated tests for schema changes. Use test datasets to ensure new schema versions work as expected without breaking existing functionality.

5. **Communication**: Establish clear communication channels with stakeholders when updating schemas. This ensures all parties understand the changes, impacts, and necessary adjustments.

### Audit Logging

1. **Enable Logging**: Turn on logging in Lake Formation to capture detailed activity logs. AWS CloudTrail and AWS CloudWatch Logs can be configured to monitor API calls and access patterns.

2. **Regular Review**: Regularly review audit logs for unusual activity. This can help identify any unauthorized access attempts or misconfigurations that could lead to data breaches.

3. **Anomaly Detection**: Implement anomaly detection mechanisms using AWS services like Amazon GuardDuty or custom machine learning models to identify suspicious activities in the audit logs.

4. **Access Context**: Capture context around data access events, including who accessed the data, when, and from where. This information is crucial for traceability and forensic analysis.

5. **Retention Policy**: Define a clear retention policy for audit logs. Consider regulatory requirements and business needs to decide how long logs should be retained.

### Compliance

1. **Data Classification**: Classify your data based on sensitivity and compliance requirements. Lake Formation can leverage AWS Glue Data Catalog tags to manage and enforce data classifications.

2. **Access Controls**: Implement fine-grained access controls using AWS Lake Formation’s built-in capabilities. Use Data Permissions to restrict access at the column, row, or cell level to meet privacy and compliance obligations.

3. **Encryption**: Encrypt data at rest and in transit. Utilize AWS Key Management Service (KMS) for managing encryption keys and ensure encryption is applied consistently across the data lake.

4. **Regulatory Alignment**: Ensure that your data governance policies align with relevant regulatory frameworks such as GDPR, HIPAA, or CCPA. Regularly review and update policies to adhere to changes in regulations.

5. **Automated Compliance Checks**: Use AWS Config rules and other AWS compliance tools to automate the monitoring of compliance status and drift detection. Establish alerts for any compliance violations to address them promptly.

By adhering to these best practices, organizations can effectively govern their data lakes in AWS Lake Formation, ensuring data security, regulatory compliance, and operational efficiency in managing schema evolution.