The Cyber Defense eMagazine February Edition for 2024

The Crucial Role of Data Categorization
Effective data tagging, filing, and categorization emerge as pivotal for Federal agencies due to several
key reasons. These processes (often automated and background leveraging some form of Machine
Learning to learn and evolve over time) enhance data understanding, enabling agencies to identify and
prioritize essential information for critical operations or decision-making. Searching for data gets a
significant boost (note here that search for data is different from search in data) and makes the data
corpus or ecosystem of the organization more organized and accessible to its various stakeholders from
IT through decision makers through data practitioners. Streamlining resource allocation is facilitated by
directing attention and resources towards managing and securing the most critical and valuable data,
thereby reducing operational costs associated with unnecessary information. Additionally, wellcategorized
data supports strategic decision-making, enabling agencies to derive meaningful insights
and drive efficient operations to enhance mission objectives.
Establishing Comprehensive Data Governance Policies
In parallel, the implementation of comprehensive data governance policies is crucial for Federal agencies,
recognizing the diverse needs of each agency. Standardized policies covering data classification criteria,
access controls, data lifecycle stages, compliance requirements, and guidelines for integrating artificial
intelligence can greatly benefit these agencies. Well-defined criteria and standards for classification guide
the handling, storage, and access of different data types, ensuring the application of appropriate security
measures and promoting a more unified and secure data environment.
Addressing Security Risks in the Digital Age
Addressing security risks in the digital age is a crucial aspect of this landscape. The security risks of
retaining unnecessary data are heightened as obsolete or redundant data increases the attack surface,
providing cyber attackers with more potential entry points. Implementing secure data destruction methods
remains essential for records management, and AI can be utilized to automate the identification and
disposal of irrelevant data. Regular audits and compliance checks should focus on AI-driven processes
to verify adherence to data disposal policies and regulatory compliance, addressing both human and
machine learning errors. Ensuring data integrity involves additional considerations, such as data
encryption, to safeguard sensitive information during transit and at rest. Regular data backups, dynamic
tiering, and robust recovery mechanisms become essential to mitigate risks of data loss or system failures
as well as ensuring the right data is being delivered to the data users (and obsolete data is not diminishing
the data access and analytics processing time).
Leveraging AI's Role in Steering Data Lifecycle Integration
The role of AI in data management is underscored by the understanding that AI is only as strong as the
data that feeds it. Federal agencies must ensure they use relevant and timely data, recognizing that, like
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