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AI and the essential role of data classification and governance
Dr. Alan R. Shark
March 18, 2024
6 Min Read
In an era where artificial intelligence (AI) is reshaping the landscapes of various sectors, its implementation in the public sector stands out for its potential to enhance efficiency, decision-making, and service delivery. However, the cornerstone of any effective AI system lies in its ability to process and analyze data accurately. This is where data classification becomes pivotal. Data classification is not just a technical procedure; it’s a strategic imperative that underpins the responsible and effective use of AI in public services. And this is always the centerpiece of AI discussion.
Some struggle with the meaning of data classification; after all, isn’t most stored data already organized into categories? This leads to better defining exactly what data classification is in the context of AI. Data classification involves categorizing data into different types, based on its nature, sensitivity, and the impact of its exposure or loss. This process helps in data management, governance, compliance and security. For AI applications, data classification ensures that algorithms are trained on well-organized, relevant and secure data sets, leading to more accurate and reliable outcomes.
Today, data managers in the public sector should focus on several key elements to ensure effective data classification, which includes the following:
Accuracy and consistency: Ensuring data is accurately classified and consistently managed across all departments is crucial. This minimizes the risk of data breaches and ensures compliance with legal and regulatory requirements.
Privacy and security: Sensitive data, such as personal information, should be identified and classified with the highest security measures to protect against unauthorized access and breaches.
Accessibility: While securing sensitive data, it’s equally important to ensure that non-sensitive, public information remains accessible to those who need it, fostering transparency and trust in public services.
Scalability: As data volumes grow, classification systems should be scalable to manage increased loads without compromising efficiency or accuracy.
Implementing effective data classification in the public sector requires a comprehensive approach, where clear data governance is paramount. This involves developing a clear data classification policy and defining what data needs to be classified and the criteria for classification. In addition, data governance should be aligned with legal and regulatory requirements and communicated across all departments.
The principles of data classification apply equally to existing data and new data acquisition, although the approaches and challenges might differ for each.
For existing data, the primary challenge is assessing and categorizing data that has already been collected and stored, often under various formats, standards and sensitivity levels. This process involves:
Auditing and inventory: Conducting comprehensive audits to identify and catalog existing data assets. This step is crucial for understanding the scope of data that needs to be classified.
Cleansing and organizing: Existing data might be outdated, duplicated or stored in inconsistent formats. Cleansing and organizing this data is a preparatory step for effective classification.
Retroactive classification: Implementing classification schemes on existing data can be time-consuming and require substantial manual effort, especially if automated classification tools are not readily available or cannot be easily retrofitted to legacy systems.
New data acquisition, by contrast, allows for the opportunity to embed data classification processes at the point of entry, making the process more seamless and integrated. This involves:
Pre-defined classification schemes: Establishing and integrating classification protocols into the data collection process ensures that all new data is classified upon acquisition.
Automation and AI tools: Leveraging advanced technologies to automate the classification of incoming data can significantly reduce manual labor and improve accuracy.
Data governance policies: Implementing strict data governance policies from the outset can ensure that all newly acquired data is handled according to predefined classification standards.
Both existing data and new data acquisition require attention for several reasons:
Compliance and security: Both data sets must comply with legal, regulatory and security requirements. Misclassification or neglect could lead to breaches, legal penalties and loss of public trust.
Efficiency and accessibility: Proper classification ensures that data, whether old or new, is easily accessible to authorized personnel and systems, thereby improving operational efficiency and decision-making.
Scalability: As new data is acquired, systems that handle existing data must be scalable to accommodate growth without compromising classification standards or processes.
While developing and managing a sound data classification policy is essential, it can be labor-intensive to go back to decades of data and records management, often operating under different conditions and policies. Here, automation and technology can play a pivotal role. This is where one can leverage AI and machine learning tools to automate the data classification process. These technologies can handle large volumes of data efficiently and can adapt to changing data landscapes.
The good news is that several tools and technologies are available that can automate much of the data classification process, making it more efficient and effective. These tools typically use rule-based systems, machine learning, and natural language processing (NLP) to identify, classify and manage data across various dimensions (e.g., sensitivity, relevance, compliance requirements). Some prominent examples include:
Data loss prevention (DLP) software: DLP tools are designed to prevent unauthorized access and transfer of sensitive information. They can automatically classify data based on predefined criteria and policies and apply appropriate security controls.
Information governance and compliance tools: These solutions help organizations manage their information in compliance with legal and regulatory requirements. They can automatically classify data based on compliance needs, and help manage retention, disposal and access policies.
Machine learning and AI-based tools: Some advanced tools use machine learning algorithms to classify data. They can learn from past classification decisions, improving their accuracy and efficiency. These tools effectively handle large volumes of unstructured data, such as text documents, emails and images.
Cloud data management interfaces: Many cloud storage and data management platforms offer built-in classification features that can be customized according to the organization’s needs. These tools can automatically tag and classify new data as it is uploaded, based on predefined rules and policies.
Implementing these tools requires a clear understanding of the organization’s data classification needs, including the data types handled, regulatory requirements, and the information’s sensitivity level. It’s also crucial to regularly review and update the classification rules and machine learning models to adapt to new data types, changing regulations and evolving security threats.
Data classification is not a one-time activity. Regular reviews and updates are necessary to ensure the classification reflects the current data environment and regulatory landscape. To sum up, data classification is a foundational element for successfully integrating AI in the public sector. It ensures the protection of sensitive information and enhances the efficiency and effectiveness of public services. By prioritizing accuracy, privacy, accessibility and scalability, data managers can lay the groundwork for responsible and effective AI applications that serve the public good.
Dr. Alan R. Shark is the executive director of the Public Technology Institute (PTI), a division of the nonprofit Fusion Learning Partners; and associate professor for the Schar School of Policy and Government, George Mason University, where he is also an affiliate faculty member at the Center for Advancing Human-Machine Partnership (CAHMP). Shark is a National Academy of Public Administration Fellow and co-chair of the Standing Panel on Technology Leadership. Shark also hosts the bi-monthly podcast Sharkbytes.net. Dr. Shark acknowledges collaboration with generative AI in developing certain materials.
