Data Tagging: Best Practices, Security & Implementation Tips
Data tagging helps businesses enhance data security, improve data governance, and better manage data and data risks. It’s being widely adopted by organisations that really need to manage their data risks – like the US Department of Defense, and life sciences researchers that collect swathes of incredibly sensitive data. But data tagging can be relatively uncomplicated to set up and comes with immense benefits for organisations looking to better manage data.
Implementing Data Tagging
Data tagging involves attaching labels (or tags) to data to help identify, categorise, use, and protect it more effectively. If your company has a document management system (even a rudimentary one), it’s likely that your company already has some form of data tagging system.
However, the demands of today’s privacy and security practices mean that most organisations will need to improve their data tagging processes and frameworks.
Each system will vary depending on an organisation’s goals, risk appetite, technology, and the types of data it collects and manages. But, broadly speaking, any organisation can improve its data management by identifying the categories of data to manage, documenting those categories, and training the team to ‘tag’ data using those categories.
For example, a university aiming to better manage data across departments might tag information based on its content, purpose, and sensitivity. Student transcripts might be tagged “Academic,” “Confidential,” and “StudentID_12345.” Research data might carry tags such as “Biology”, “Grant-Funded,” or “Pending Publication.” This structured approach ensures that data is easily searchable, accessible to the right personnel, and safeguarded based on its sensitivity.
Data Tagging in Practice: Zero Trust Architecture
Zero-trust is a security model that suggests organizations shouldn’t trust any user or system by default, regardless of whether they are inside or outside of the organization’s perimeter. Instead, every access request is fully authenticated, authorized, and encrypted before granting access. The mantra is “never trust, always verify.”
Zero-trust architecture is currently seen as the gold standard in cybersecurity, and it is being broadly adopted by governments worldwide. Data tagging plays a key role in a zero-trust environment because it allows for:
- Granular Access Control: With data properly tagged (e.g., confidential, public, personal), zero-trust models can provide more granular access controls. For instance, a user might be allowed to view “public” data but will need additional verification steps to access “confidential” data.
- Data Classification for Security Protocols: Depending on the data’s tag, different security protocols can be enacted. Highly sensitive data might require multi-factor authentication (MFA) or might only be accessible from specific devices or locations.
- Dynamic Policies: With data tagging, security policies can be dynamically applied. If a user typically accesses non-sensitive data and suddenly requests access to a highly sensitive tag, this can raise a flag for verification or even deny access based on abnormal behaviour.
- Auditing and Compliance: Data tagging can assist in ensuring that zero-trust protocols are being correctly applied. By tracking which users access which tags, organizations can audit access patterns and ensure compliance with internal policies and external regulations.
- Data Loss Prevention (DLP): In a zero-trust framework, DLP solutions can leverage data tags to monitor and control data transfers. If data tagged as “personal information” is being transferred outside the organization, the DLP can trigger alerts or block the transfer.
- Micro-segmentation: In advanced zero-trust implementations, network resources are broken down into micro-segments. Data tagging can help drive this segmentation, ensuring that sensitive data is stored in tightly controlled segments with specific access controls.
Data Tagging Best Practices
Create & Maintain ‘A Single Source of Truth’ For Your Data
It’s a good practice to create and maintain one single centralised data repository, if possible. Maintaining a single data repository is generally more cost-effective, promotes efficient decision-making, reduces waste and value leakage, and helps to maintain a high standard of data accuracy, integrity, and consistency. It can also help to drive innovation, increase agility, and improve the customer and employee experience.
Implement a Data Governance Framework
A data governance framework provides your organisation with a structured approach to managing, using, and safeguarding data. Yours should outline roles, responsibilities, processes, quality assurances, and the tools currently being used to manage and tag data.
Implementing a data governance framework (alongside data tagging) helps to future-proof your data management practices.
Automate Data Tagging, Where Possible
Automating data tagging involves deploying machine learning or AI-enabled tools. It can reduce the amount of time humans spend tagging data and the risk of human error (when it is set up well).
Combine Data Tagging with Access Management
When data tagging is paired with access management control, these tags dictate who can access, modify, or distribute specific data. In practice, this streamlines data accessibility, so employees can efficiently obtain the information they need without compromising security, leading to a balanced approach to data protection and operational efficiency.
Audit Your Tagged Data
You should routinely audit your tagged data to ensure the tags are being applied appropriately, accurately, and consistently. During the audit, you should make note of any tags that are regularly being misapplied, as well as any new data tags that could improve your system.
Data Governance with Privacy 108
The team at Privacy 108 can help your organisation better understand and manage its data. If you’re looking to improve your organisation’s data governance, reach out. We would love to help you.