An organization is working to minimize the eDiscovery costs associated with the extensive analysis of...

This question aligns with CHFI v11 objectives underComputer Forensics FundamentalsandeDiscovery and Digital Evidence Management. CHFI v11 emphasizes that one of the most effective ways to reduce eDiscovery costs and timelines is throughearly data reduction and intelligent filtering. Organizations increasingly rely onTechnology-Assisted Review (TAR), also known as predictive coding, combined with data reduction techniques such as deduplication, de-NISTing, keyword filtering, and relevance scoring.

TAR leverages machine learning algorithms to identify patterns in relevant documents and automatically prioritize or exclude data that is unlikely to be responsive. This significantly reduces the volume of data requiring manual review while maintaining defensibility and compliance with legal and regulatory requirements. CHFI v11 highlights TAR as a best practice for handling large-scale electronic evidence efficiently, especially in litigation and regulatory investigations.

The other options support eDiscovery but do not directly reduce review scope: data retention focuses on lifecycle management, chain of custody ensures evidence integrity, and data mapping identifies data sources. None directly addressexcluding irrelevant data early in the review process. Therefore, consistent with CHFI v11 eDiscovery best practices, usingtechnology-assisted review (TAR) and data reduction toolsis the correct answer.