The best reason to implement a data retention policy is to limit the liability associated with storing and protecting information. A data retention policy is a business' established protocol for maintaining information, typically defining what data needs to be retained, the format in which it should be kept, how long it should be stored for, whether it should eventually be archived or deleted, who has the authority to dispose of it, and what procedure to follow in the event of a policy violation1. A data retention policy can help an organization to:
Comply with legal and regulatory requirements that mandate the retention and disposal of certain types of data, such as financial records, health records, or personal data Reduce the risk of data breaches, theft, loss, or corruption by minimizing the amount of data stored and ensuring proper security measures are in place Save costs and resources by optimizing the use of storage space and reducing the need for backup and recovery operations Enhance operational efficiency and performance by eliminating unnecessary or outdated data and improving data quality and accessibility Support business continuity and disaster recovery plans by ensuring critical data is available and recoverable in case of an emergency Facilitate audit trails and investigations by providing evidence of data authenticity, integrity, and provenance Therefore, by implementing a data retention policy, an organization can limit its liability associated with storing and protecting information, as well as improve its data governance and management practices.
References:
Data Retention Policy 101: Best Practices, Examples & More

The IS auditor should be most concerned if completeness testing has not been performed on the log data, as this could indicate that some logs are missing, corrupted, or tampered with, and that the log aggregation system is not reliable or accurate12. Completeness testing is a process of verifying that all the logs generated by the source systems are successfully collected, transferred, and stored by the log aggregation system, and that there are no gaps or inconsistencies in the log data34. Completeness testing is essential for ensuring the integrity and validity of the log data, and for supporting the risk management practices of the organization.
References
1: Log Aggregation: How it Works, Methods, and Tools - Exabeam2 2: Log Aggregation & Monitoring Relation in Cybersecurity4 3: Log Aggregation: What It Is & How It Works | Datadog3 4: Data Flow Testing
- GeeksforGeeks1