By Dr. Pragyansmita Nayak, Chief Data Scientist, Hitachi Vantara Federal
National security is amid a transformative journey driven by technological progress in more ways than one. One of those ways has led Federal agencies, like the Department of Defense (DoD) to find themselves at a critical juncture, where meticulous consideration of data location, storage, and management is crucial for security and decision making. But while actively adopting architectures rooted in data fabric or data mesh to fortify security and ensure authorized data accessibility, they simultaneously grapple with the accumulation of vast volumes of data for various purposes, some of which may go unused and thus never harnessed for a myriad of valuable insights before their value or utility starts depreciating. The evolving landscape demands not just a forward-thinking approach to data categorization and tagging but also strategic measures to address challenges such as data sovereignty, redundancy, and the potential consequences of data decay.
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, well-categorized 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 any perishable good, data has a shelf life. Processing the right data unlocks endless possibilities, as AI plays a pivotal role in data management by enhancing efficiency and accuracy in categorization processes. Machine learning algorithms, learning from patterns, automatically tag and categorize data based on predefined criteria, accelerating the categorization process while ensuring consistency and accuracy, thus mitigating the risk of both human and machine learning errors (apart from the enormous time saved in contrast to manual review and automation of a repetitive task). This underscores the critical connection between technological advancements, data management, and national security imperatives.
Continuously revisiting the data lifecycle and its management, particularly with the integration of AI, is crucial. Implementing a structured approach to managing data, from creation to disposal, ensures the utilization of AI in enhancing efficiency and accuracy. Protocols for data retention periods, archival processes, and secure disposal methods must be defined to minimize risks associated with retaining unnecessary or outdated data.
About the Author
Dr. Pragyansmita Nayak is the Chief Data Scientist at Hitachi Vantara Federal (HVF). She explores the “Art to the Science” of solution architectures orchestrating data, APIs, algorithms, and applications. She has over 25+ years of experience in software development and data science (Analytics, Machine Learning and Deep Learning). She has led projects for several Federal Government agencies (DoD/Civilian) in the domain of Federal Accounting, Operational Analytics, Data Fabric, Object Storage, Metadata management, Records Management and Data Governance. She holds a Ph.D. in Computational Sciences and Informatics from GMU (Fairfax, VA) and Bachelor of Science in Computer Science. For more information on Pragyan’s professional experience, please visit her LinkedIn and Twitter profiles.
