From the course: CompTIA SecAI+ (CY0-001) Cert Prep

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Data anonymization

Data anonymization

From the course: CompTIA SecAI+ (CY0-001) Cert Prep

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Data anonymization

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Data anonymization is the process of removing or modifying identifiers in a data set so that individuals or sensitive entities cannot be identified. It protects privacy by ensuring that the data cannot be traced back to a specific person, even when shared or analyzed. In AI systems, anonymization should occur as early as possible in the data pipeline to prevent private information being exposed during processing or model training. Complete anonymization must eliminate both direct and indirect identifiers, leaving no link to the original identity. Achieving this level of protection is difficult because data can sometimes be re-identified when combined with other data sets. For this reason, many organizations rely on pseudonymization instead of full anonymization. Pseudonymization replaces identifiers with fictitious values rather than deleting them entirely. For example, a name, says Alice, might become person A. This allows an individual's records to remain connected within the data…

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