The volume and variety of data handled by government agencies is continuously expanding, ranging from public records like legislative documents and public health statistics to classified information that could compromise security if improperly disclosed. In an era of increasingly sophisticated cyber threats and persistent human risks, effectively managing this data and maintaining compliance is crucial. This applies to every stage of the data lifecycle—from anonymization during development, protection in production, encryption at rest, and archiving for retention, to secure disposal. No matter the phase, proper data classification is essential.
What is Data Classification, and Why Is It Important?
Data classification is the process an organization uses to characterize its data assets using persistent labels so those information assets can be managed properly. By doing so, agencies can better protect their data and comply with regulatory requirements. More recently, data classification also plays an essential role in machine learning (ML) and artificial intelligence (AI) initiatives by ensuring that the data used for training models is accurately labeled and organized.
In government agencies, data is classified into two primary categories: 'classified' and 'controlled unclassified information’ or CUI, forming the foundation of data security protocols. Classified data includes information that, if disclosed without authorization, could harm national security or public safety. CUI is sensitive information that does not meet the criteria for classification but must still be protected, as it can directly impact the ability of the Federal Government to successfully conduct its essential missions and functions.
Imagine, for example, a breach of confidential data. With a well-structured data classification system, an agency can quickly identify associated notification requirements, determine whether encryption at rest was used, and trace all downstream systems that interacted with the compromised data. However, not all data breaches are immediately recognizable as high-risk. For instance, research data, which may not always be seen as sensitive, still requires proper classification to meet compliance standards.
This is particularly relevant for institutions like universities, where government regulations around CUI apply. In such cases, agencies and higher education institutions must be able to quickly assess the impact of a breach and demonstrate compliance with relevant regulations to mitigate risk effectively.
Beyond security, data classification enhances data management and retrieval, making it easier to unlock insights from valuable information. Knowing exactly what data exists and how it’s used enables government agencies to leverage it more effectively, supporting mission objectives and improving overall efficiency.
How Data Classification Fits with Zero Trust and the Principle of Least Privilege (PoLP)
Recent federal directives have introduced the Zero Trust architecture, a modern security framework that emphasizes the Principle of Least Privilege (PoLP)—ensuring users have access only to the data they absolutely need and nothing more. This "never trust, always verify" approach is key to limiting access and protecting sensitive information. However, PoLP can only be effectively implemented if agencies have a clear understanding of what data they possess and where it's stored, which is where data classification becomes critical.
Without proper data classification, enforcing PoLP becomes nearly impossible. Agencies must know the sensitivity and relevance of each data asset in order to limit access appropriately. This not only strengthens security but also aligns with the Zero Trust mandate for federal agencies.
Implementing Data Classification
While classifying your data is critical, manual data classification is cumbersome, especially in systems like Salesforce that hold so much data. Yes, classifying your data yourself is better than not classifying at all. However, manual data classification can often be:
- Complicated: Gathering a comprehensive list of data elements into a spreadsheet and updating it is convoluted and time-consuming.
- Disconnected: Storing data classification details in spreadsheets outside of Salesforce makes it difficult to connect, summarize, and gain visibility into data trends.
- Error-Prone: Inaccuracies are inevitable when manually updating spreadsheets, leading to incomplete or incorrect data.
- Messy: With multiple users having access, there’s a risk of version control issues, which can compromise the integrity of information.
- Costly and Resource Heavy: Manual efforts require significant labor hours, resulting in indirect costs.
By contrast, automated data classification offers a more efficient and effective approach, enabling agencies to identify and tag sensitive data accurately and providing more granular access controls crucial for implementing Zero Trust. As agencies increasingly embrace automation across various operations, data classification should be no exception.
Own Secure: Automated Salesforce Data Classification for Agencies
To help agencies streamline their journey toward Zero Trust compliance in Salesforce, Own offers a solution that streamlines your Salesforce data classification process. Own Secure simplifies reporting, reduces the time spent on manual tasks, and enables agencies to identify and manage sensitive data more effectively.
With Secure's guided data classification capabilities, agencies can also position themselves for a smoother transition to upcoming security frameworks, such as the one currently being developed by the Federal CISO and CDO Council. This new data security framework, set to be introduced soon, focuses on a Zero Trust approach to enhance data protection across government agencies. The joint initiative aims to enhance protection for sensitive government data and improve risk management practices, both of which Own can help within the context of Salesforce.
Learn more about Own Secure here or request a free guided zero-trust risk assessment for Salesforce today.
