This work provides a systematization and critical analysis of existing methodologies for recovering information from damaged or inaccessible Redundant Array of Independent Disks (RAID) arrays. The relevance of the study is determined by the fact that the reliability of corporate storage directly affects the continuity of business processes and the stability of government operations. The objective of the research is to conduct a comprehensive review of algorithmic approaches to data recovery with a focus on automated identification of key array configuration parameters and reconstruction of information at the logical level. In particular, traditional methods based on analysis of metadata and block placement tables are examined, as well as modern techniques employing entropy-based assessment of bit distributions, detection of file system signatures, and application of heuristic machine learning models. It is noted that the combination of automatic recognition of RAID parameters (level, striping algorithm, block size) with in-depth analysis of internal file system structure minimizes operator intervention and significantly increases the likelihood of successful data retrieval even in the absence of complete configuration information. This work will be useful for IT data recovery engineers, information security and digital forensics specialists, and researchers addressing reliability and fault tolerance of modern storage systems.

RAID, data recovery, RAID 5, RAID 6, damaged array, data redundancy, file system

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