A Glossary of Key Terms in Data Governance & Compliance for HR Tech

In the rapidly evolving landscape of HR technology and talent acquisition, understanding the nuances of data governance and compliance is no longer optional—it’s foundational. As businesses leverage automation and AI to streamline HR processes, the sheer volume and sensitivity of employee and candidate data necessitate a robust framework for its protection, management, and ethical use. This glossary provides HR and recruiting professionals with essential definitions to navigate the complexities of data governance, ensuring your operations remain compliant, secure, and trustworthy.

Data Governance

Data governance refers to the overall management of the availability, usability, integrity, and security of data used in an enterprise. It establishes the policies, procedures, and responsibilities for ensuring data quality, privacy, and compliance with regulations. In HR tech, effective data governance means defining who has access to candidate and employee data, how it’s stored and processed, and how long it’s retained. For automation, this means configuring systems like Make.com to adhere to these rules, ensuring that data flows between HRIS, ATS, and other platforms are secure and compliant, reducing the risk of data breaches and non-compliance fines.

Data Compliance

Data compliance is the act of adhering to data protection laws, regulations, and industry standards that dictate how organizations must collect, store, process, and disclose sensitive data. For HR and recruiting, this involves navigating laws like GDPR, CCPA, and various local privacy acts that govern personal identifiable information (PII) related to applicants and employees. Non-compliance can lead to significant financial penalties, reputational damage, and loss of trust. Implementing automation within HR tech must factor in compliance from the outset, ensuring automated workflows for candidate screening, onboarding, or payroll processing align with all relevant legal requirements.

Personal Identifiable Information (PII)

PII is any data that can be used to identify a specific individual. This includes direct identifiers like name, social security number, or email address, as well as indirect identifiers that, when combined, can uniquely identify someone (e.g., date of birth, place of birth, and mother’s maiden name). In HR tech, PII is central to nearly every operation, from resume parsing to employee record management. Robust data governance dictates how PII is collected, encrypted, stored, and shared, particularly when automating processes. Ensuring PII is handled securely across integrated systems, such as ATS to HRIS transfers, is paramount to protect individuals’ privacy and meet compliance obligations.

General Data Protection Regulation (GDPR)

GDPR is a landmark data privacy law in the European Union and European Economic Area, enacted in 2018, that provides strict rules for how personal data of EU citizens is collected, stored, and processed, regardless of where the organization is located. Key principles include lawful processing, data minimization, accuracy, storage limitation, integrity, and confidentiality. For global HR and recruiting teams, GDPR impacts how candidate applications are managed, how consent for data processing is obtained, and how employee records are handled. Automation systems must be designed to respect these rights, including the “right to be forgotten” and data portability, particularly when orchestrating international talent pipelines.

California Consumer Privacy Act (CCPA) / California Privacy Rights Act (CPRA)

The CCPA, enhanced by the CPRA, is a state-specific law in California that grants consumers significant rights regarding their personal information. It applies to businesses that meet certain thresholds and affects how employee and candidate data of California residents are handled. Key rights include the right to know what data is collected, the right to delete personal information, and the right to opt-out of the sale or sharing of personal information. HR tech platforms and automated recruiting workflows must be configured to accommodate these rights, ensuring transparency in data collection practices and providing mechanisms for individuals to exercise their privacy preferences.

Data Minimization

Data minimization is a core principle of data protection, advocating for collecting only the absolute necessary personal data for a specific purpose. It aims to reduce the risk associated with data breaches and simplify data management. In HR and recruiting, this means reconsidering what information is truly essential on a job application or during the onboarding process. For instance, do you need a candidate’s full address during initial screening? Implementing automation can help enforce data minimization by designing workflows that only request or transfer specific data fields at appropriate stages, preventing the unnecessary accumulation of sensitive information.

Consent Management

Consent management refers to the process of obtaining, recording, and managing individuals’ permissions for collecting, processing, and using their personal data. Under regulations like GDPR, consent must be freely given, specific, informed, and unambiguous. For HR tech, this is crucial when collecting resumes, running background checks, or using AI for candidate profiling. Automated systems can play a vital role in capturing and documenting consent efficiently, for example, by integrating consent forms directly into application portals or using email automation to periodically re-confirm consent for talent pools, ensuring compliance and transparency.

Data Security

Data security encompasses the protective measures used to safeguard data from unauthorized access, corruption, or theft throughout its lifecycle. This includes physical security, technical controls like encryption and access management, and administrative policies. In HR tech, where highly sensitive PII and confidential company data are processed, robust data security is paramount. Automation tools like Make.com should be configured with secure API connections, multi-factor authentication, and data encryption protocols. Regular security audits and employee training are also critical to mitigate risks associated with human error or malicious intent, protecting both the organization and its data subjects.

Data Lifecycle Management

Data lifecycle management (DLM) is a policy-based approach to managing the flow of an information system’s data from its creation and initial storage to the time it is deleted. This includes phases such as creation, storage, usage, archiving, and destruction. For HR data, DLM dictates how long candidate applications are kept, when employee records are moved to an archive, and how data is securely disposed of after retention periods expire. Automated retention policies within HRIS or ATS platforms, often integrated via automation tools, ensure compliance with legal requirements and minimize unnecessary data storage, reducing liabilities and simplifying data audits.

Anonymization & Pseudonymization

Anonymization is the process of irreversibly removing personal identifiers from data so that the individual can no longer be identified. Pseudonymization is a data management and de-identification procedure by which PII fields within a data record are replaced by one or more artificial identifiers, or pseudonyms. Both are critical for HR analytics, AI training, and research, allowing organizations to derive insights without compromising individual privacy. While anonymization is permanent, pseudonymization allows for re-identification if the key is available. Automation can facilitate these processes, applying transformation rules to data before it’s used for aggregate reporting or feeding into AI models, especially when adhering to strict privacy regulations.

Data Subject Rights (DSRs)

Data Subject Rights are the entitlements granted to individuals regarding their personal data under privacy regulations like GDPR and CCPA. These typically include the right to access their data, the right to rectification (correct inaccuracies), the right to erasure (“right to be forgotten”), the right to restrict processing, the right to data portability, and the right to object to processing. HR and recruiting teams must establish clear processes for handling DSR requests. Automation can significantly streamline this by creating workflows for acknowledging requests, retrieving data from disparate systems, and executing changes or deletions, ensuring timely and compliant responses.

Data Mapping

Data mapping is the process of creating a visual representation or detailed record of how data flows through an organization’s systems and processes. It identifies where data originates, where it is stored, how it is transformed, and where it ultimately resides or is destroyed. For HR tech, data mapping is essential for understanding the journey of candidate and employee data across various platforms (e.g., ATS, HRIS, payroll, background check vendors). This clarity is vital for establishing data governance policies, identifying potential compliance gaps, and designing effective automation workflows that respect data privacy rules at every touchpoint.

Third-Party Data Sharing

Third-party data sharing involves transferring personal data to external entities, such as background check providers, payroll services, benefits administrators, or external recruiters. This practice is heavily regulated, requiring clear agreements, data processing addendums, and often explicit consent from the data subject. In HR automation, it’s common to integrate with numerous third-party tools. Data governance demands that any automated data transfer to a third party is documented, secure, and compliant with privacy laws, ensuring that vendors uphold the same data protection standards. Robust contracts and due diligence are crucial to mitigate risks associated with vendor non-compliance.

Data Breach Response Plan

A data breach response plan is a predefined set of procedures that an organization will follow in the event of a data security incident. This plan outlines steps for identifying the breach, containing its impact, investigating the cause, notifying affected individuals and regulatory authorities (if required), and implementing measures to prevent future occurrences. For HR and recruiting, a breach involving PII can have severe consequences. An effective plan often leverages automation for rapid internal communication, isolating affected systems, and managing stakeholder notifications, ensuring a swift and compliant reaction to minimize damage and maintain trust.

Security by Design / Privacy by Design

Security by Design and Privacy by Design are approaches that embed security and privacy considerations into the very architecture and design of systems, processes, and products from the earliest stages of development. Rather than being an afterthought, security and privacy are fundamental components. In HR tech and automation, this means designing new HR applications, ATS integrations, or automated recruiting workflows with data protection principles built-in. This proactive stance ensures that privacy safeguards (like data minimization or encryption) and security measures are integral to how data is handled, rather than costly retrofits, aligning with regulatory expectations and best practices.

If you would like to read more, we recommend this article: Adobe Workfront: Orchestrating Strategic HR & Talent Acquisition with AI & Automation