This question aligns with CHFI v11 objectives under Computer Forensics Fundamentals and eDiscovery and Digital Evidence Management . CHFI v11 emphasizes that one of the most effective ways to reduce eDiscovery costs and timelines is through early data reduction and intelligent filtering . Organizations increasingly rely on Technology-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 address excluding irrelevant data early in the review process . Therefore, consistent with CHFI v11 eDiscovery best practices, using technology-assisted review (TAR) and data reduction tools is the correct answer.