As privacy laws and regulations gain traction globally, organizations have increasingly embedded privacy principles and safeguards into their product development processes for their products and services. However, a critical blind spot often remains in safeguarding user data within their internal systems, such as databases and data warehouses.
Organizations rely on user data analytics to drive innovation, optimize operations and inform strategic decision-making. This includes behavioral analysis, predictive modeling, A/B testing, social media monitoring and web analytics. Data analysis is critical in ensuring an organization's operational integrity, including fraud detection, debugging, and compliance with legal obligations and law enforcement requests.
While all these data analysis applications are generally pursued with good intentions by an organization's employees, inadequate privacy guardrails can expose sensitive data to unintended or unauthorized access. These lapses compromise user trust and heighten the risk of data breaches and reputational damage.
Without clear policies, robust controls and a proactive approach to privacy within internal systems, companies inadvertently leave a critical vulnerability unaddressed, underlining the need for a broader commitment to protect user data privacy.
Extending privacy review to data analytics
Meta designed a privacy review process that goes beyond reviewing products and services. It includes reviewing the uses of user data for analytical and measurement purposes.
All data analytics involving user data must comply with established privacy expectations and safeguards to protect user data. This was particularly challenging for a large company, where data analyses are run by thousands of engineers, including software data engineers, analysts and data scientists, for various purposes crucial to the organization's operations. By that estimate, tens of thousands of queries are being run monthly for analysis purposes. This requires making the privacy review process more adaptable and scaling it appropriately.
Key recommendations for privacy review in data analytics
As organizations increasingly rely on data analytics using user data to drive decision-making and innovation, it becomes essential to ensure that these processes are conducted with a strong emphasis on privacy. Implementing a robust privacy review process for user data analytics can help safeguard sensitive information and maintain user trust. This is crucial for organizations of all sizes, as it ensures that data is handled responsibly and in compliance with privacy standards.
For smaller organizations, where team sizes may be limited, it's important to adapt these processes to fit the resources. This might involve consolidating responsibilities across fewer individuals or leveraging automated tools to assist with privacy compliance or engaging with third-party consulting firms who specialize in providing privacy services. By prioritizing key privacy principles and fostering a culture of data responsibility, even small teams can effectively manage privacy risks.
Build infrastructure to detect when user data is being queried
The privacy team is responsible for developing a robust program to ensure that all the data stored in production and offline data stores is accurately classified with user data tags. The classification can be achieved through the use of machine learning classifiers, which can crawl through the data across stores and ensure user data is classified with tags or have data annotation teams manually classify the data. A combination of both has the benefit of being the most effective without overreliance on any one methodology and can ensure more accuracy.
Develop a comprehensive data analysis policy
The privacy team, in partnership with the policy team, should develop a comprehensive data analysis policy. This policy should provide clear definitions and guidance on what constitutes data analysis, the types of user data involved, and clearly delineate sensitive data types that require additional reviews. Additionally, the policy should also describe the purposes/reasons for analysis that are acceptable at the company.
Define review criteria based on purpose
Perform an inventory exercise to meet with stakeholders across the company, understand the different purposes for data analyses and develop a succinct list of these data analysis purposes.
Some categories of data analysis purposes could include product understanding, debugging and operational monitoring, research, safety/integrity, and others.
Standardized requirements for common analysis use cases
Identify and define common data analysis purposes or reasons with standardized requirements and low risk. For example, analyses related to safety/integrity are critical and take precedence as they can help identify spam and threat actors and even assist law enforcement agencies in preventing catastrophic events. In such cases, it's essential to establish clear guidelines and standardized privacy requirements that employees must acknowledge and adhere to, ensuring a balance between efficiency and privacy compliance.
Additional review for high-risk data types and purposes
Certain high-risk data types are of heightened sensitivity, and they should always be reviewed for additional risks and have appropriate privacy requirements (safeguards) before they can be used for data analysis purposes. These data types should be more inclusive than just the special categories of data, such as location, contact points, financial data, etc.
Privacy requirements
Ensure that both standardized analysis purposes and those requiring additional reviews have privacy requirements for employees to acknowledge. Implement these safeguards before running the data analysis to ensure compliance with retention, deletion, access and transfer privacy requirements.
Access controls
Implement strict access controls to restrict data access based on roles and responsibilities. This minimizes the risk of unauthorized access and data breaches. Access controls can act as an additional safeguard on top of the existing purpose-based enforcement for data analytics, preventing employees from accessing sensitive data.
Training and awareness
Provide regular training for employees involved in data analytics projects to enhance their understanding of the data analytics policy, privacy obligations, and safe data handling practices. Upon performing periodic quality checks, if there are learnings that employees need additional training or resources, there should be emphasis on creating easy-to-understand wiki pages/documents providing additional tailored training where there may be issues. In-tool training tips are also a great way to push employees to understand the privacy review processes at run time.
Documentation and accountability
Maintain comprehensive documentation of privacy assessments, decisions and actions taken regarding policy changes and any tooling functionality changes. This supports compliance and enhances transparency and accountability; documentation can be used as evidence for regulators.
Stakeholder engagement
Engage various stakeholders, including legal, privacy, policy, security, engineering and product teams, in policy development and privacy review processes. Their insights can provide valuable perspectives on privacy implications, compliance requirements and product perspectives. Involving product and engineering teams is crucial as the data analysis policy is more of a technical policy and engineering and product teams are probably best to provide insights into the detailed technicalities of the data analysis tools they would typically use.
Real-time monitoring
Build real-time monitoring tools to detect and respond to any unauthorized access or anomalies in data querying activities. The tools should help the employee understand which data types they are querying are classified as user data and which are of heightened sensitivity and provide clarification if they are denied access to certain data types.
Regular quality checks
The organization should implement regular quality checks to ensure compliance with the ongoing privacy policy and standards. Quality checks help identify deviations or gaps in privacy practices, improve employee understanding, and facilitate necessary remediation actions.
Auditable logging
The privacy team, in collaboration with the infrastructure team should maintain auditable logs of all data access and querying activities performed by all employees at the company. These logs are crucial for monitoring, auditing, and investigating any suspicious activities or potential privacy incidents or violations. By ensuring that these logs are comprehensive and regularly reviewed, the organization can enhance its ability to detect and respond to privacy risks effectively.
Conclusion
By embedding these design recommendations, organizations can achieve seamless privacy compliance across all internal data queries and data analysis activities.
Extending the privacy review process to data analytics is a critical step in upholding user trust and ensuring compliance with evolving privacy standards. By implementing these recommendations, organizations can establish a robust framework that balances the need for data-driven insights with the imperative of protecting sensitive user data. Transparency, accountability and user-centricity must be prioritized in data practices. This prioritization can foster a culture of trust and responsibility and ultimately drive innovation and growth while safeguarding the rights and interests of users.
Rahul Chidugulla, CIPM, is privacy program manager, product at Meta.
