Privacy Controls for Modern Data Stacks: A Complete Overview
It’s nearly impossible to participate in today’s society without having to share your personal information. We routinely offer up our data when we visit the doctor, shop online, do our taxes, and in hundreds of other instances. Most times, this happens without a second thought – but as privacy violations increasingly make headlines, it’s becoming more difficult to not think twice about providing your sensitive personal data to others.
Even behemoths like Amazon, Microsoft, and Meta are not immune from data privacy breaches, each having been fined millions or billions of dollars for mishandling their users’ information. This begs the question – what can be done to protect individuals’ privacy in today’s data-driven world? This guide gives a comprehensive look at data privacy controls and how to effectively implement them in order to avoid breaches and preserve your customers’ trust.
What Are Privacy Controls?
While the term “privacy controls” often gets conflated with other data security buzzwords, it refers to a specific set of mechanisms meant to protect sensitive data. According to NIST, privacy control is defined as “the administrative, technical, and physical safeguards employed within an agency to ensure compliance with applicable privacy requirements and manage privacy risks.” We’ll focus primarily on the technical side of privacy controls in this guide, but it’s worth noting that achieving data privacy is a multidimensional objective with shared responsibilities.
The goal of data privacy is to protect the means by which personal information is collected, stored, and used. By extension, privacy controls are meant to ensure individuals have rights to control how their data is used, and to mitigate threats of unauthorized access, malicious actors, and other potential risks. The focus on protecting the rights and data of individuals is what differentiates data privacy from data security.
Data security refers to how organizations handle and protect data, including the technology and processes put in place to safeguard it. Like privacy controls, security controls can be procedural, technical, and physical, but they are less focused on giving individuals rights over how their data is used and more directed at controlling how others use data assets. Given the fine nuance, there may be overlap in how exactly these controls are implemented.
Privacy controls are key to an organization’s data security posture because they help proactively protect data from the vast threat landscape and reduce the chance of an adverse event compromising sensitive information.
4 Popular Types of Privacy Controls
While there are many approaches to protecting data privacy, we’ll focus on four popular technical methods – data access control, encryption, anonymization, and data loss prevention (DLP).
1. Data Access Controls
Data access can be a tug-of-war between security and governance teams that want to ensure compliant data use, and the data analysts and scientists that need fast, efficient access in order to do their jobs. At their core, access controls aim to satisfy both sides.
As data use has become ubiquitous and volumes of assets and users grow, RBAC (role-based access control) and ABAC (attribute-based access control) have emerged as the two most common approaches to controlling who can access what information.
Role-Based Access Control
RBAC has been adopted by many data teams since its introduction in the early 90s, due in part to its relative ease of implementation when compared to mandatory and discretionary controls. In this model, user roles are the basis for data access policies. System admins create and manage roles, and users are only granted access to data that aligns with their predetermined role(s).
Since this approach is static and linear, any changes to organizational structures, regulatory requirements, or personnel require admins to create new roles and policies to ensure access permissions remain current. This makes RBAC well suited for small organizations with few data users and assets, but difficult to scale in larger, more complicated data ecosystems.
Attribute-Based Access Control
The ABAC model has quickly gained popularity in today’s cloud and hybrid environments, which require flexible, agile, and dynamic approaches to managing data access. Since access is granted or restricted at query time based on various attributes about data users, objects, actions, and environments, permission decisions are highly granular and scalable.
Whereas new RBAC policies must be created for any system changes that impact data, a single ABAC policy can achieve the same outcomes by leveraging user metadata to automatically adjust as necessary. In fact, a study by GigaOm Research found that ABAC required 93x fewer data policies than RBAC to accomplish the same objectives – which could save organizations an estimated $500,000 in operational costs.
Encryption is an advanced approach to data privacy that leverages a mathematically-generated key to conceal data at rest, in transit, or in use. The two primary types of encryption are symmetric, which leverages a single key, and asymmetric, which involves separate keys for encryption and decryption. Only those who are authorized to access the data should have access to the key, which reduces the likelihood of sensitive information being intercepted and compromised by bad actors. Since encryption relies on an algorithm to transform data from plaintext into ciphertext, the chance of it being guessed by a human or computer is very low (though not impossible).
Encryption is a popular privacy control because it allows individuals to go about their daily lives without constantly wondering if their personal privacy is at risk. For instance, browsing the internet using a VPN and/or “https” address, and withdrawing money from an ATM are both processes that rely on encryption to ensure the secure transfer of data.
Data anonymization involves removing or encrypting sensitive information in a data set so as to protect individuals’ privacy without impacting data storage and use. It is essential for both satisfying major data compliance laws and regulations like GDPR and HIPAA and enabling easy and secure data sharing.
There are several different data anonymization techniques that teams can employ, including dynamic data masking, pseudonymization, generalization, perturbation, swapping, and synthetic data generation. Choosing the right approach or combination of techniques is contingent on an organization’s specific data assets, needs, and objectives.
4. Data Loss Prevention (DLP)
Data loss prevention (DLP) is a privacy control aimed primarily at detecting and avoiding instances in which data is leaked, inappropriately accessed, or inadvertently destroyed. Whereas the previously mentioned controls focus on who can access data and what they can see, DLP preserves privacy by providing visibility into how data is stored, transferred, and used, and bolstering network-level security. Therefore, data access control and encryption may be elements of a data loss prevention policy.
Best Practices for Implementing Privacy Controls
Incorporating privacy controls as part of a holistic data security strategy requires understanding and coordinating an organization’s data assets, users, processes, and technologies, as well as relevant regulations. If mismanaged, this can easily complicate or cause mistakes in implementation. But following a few best practices can streamline the process and ensure that privacy controls are sufficiently protecting sensitive information.
Lead with Privacy by Design
Privacy by design, which posits that privacy should be considered throughout a data project’s lifecycle, is the cornerstone of data privacy control implementation. This concept involves embedding data protection and privacy principles into the design, architecture, and operation of systems and processes, rather than treating them as an afterthought. Just as you wouldn’t build an entire car and then determine where to add the brakes, privacy by design is meant to ensure that privacy controls are baked into and seamlessly coordinated with systems and processes.
Many of the subsequent best practices are examples of privacy by design in action. At a conceptual level, privacy by design should be proactive, standard, end-to-end, transparent, fully functional within system designs, and focused on user privacy.
Conduct a Privacy Risk Assessment
The first step in privacy control implementation is getting a handle on potential risks to data and its integrity. Conducting a privacy risk assessment is the most systematic, objective way to identify potential threats so they can be proactively addressed. The two most common approaches are a privacy impact assessment (PIA) and a data protection impact assessment (DPIA).
- A PIA helps data, security, and governance teams pinpoint and manage risks to data privacy at an organizational level. It assesses systems and processes that interact with sensitive data to provide visibility into how data is managed, used, and protected.
- A DPIA documents purposes for data processing, legal bases for data collection, potential privacy risks, and mitigation tactics. It is required by the GDPR as a way to evaluate risk and acknowledge whether the right mitigation tactics are in place.
Automate Data Discovery & Classification
With an understanding of potential privacy risks, the next best practice for privacy control implementation is enabling data discovery and classification within data ecosystems. In the context of security, data discovery identifies sensitive information such as PII and PHI, giving organizations visibility into the location of sensitive data so they can build controls to manage and protect it. Employing a data security solution that automatically identifies and applies sensitivity classifications to data will streamline data access policy enforcement and avoid manual processes, helping to ensure that sensitive data is handled appropriately and in compliance with privacy standards.
Develop Data Minimization & Retention Protocols
Data minimization and retention are key data privacy controls that limit data activity and reduce the risks of data breaches, unauthorized access, or other potential violations. Minimization restricts data collection, storage, and usage to specific purposes, while retention establishes limits for how long data can be stored based on regulatory requirements, business needs, and processing purposes. Both approaches help protect data privacy, simplify data management, and achieve compliance with regulations like GDPR, which explicitly calls for limitations on data retention.
Monitor Data Use to Detect Risks
Implementing privacy controls cannot completely eliminate threats to your data. That’s why continuous data monitoring is not just a best practice – it’s a necessity. Keeping tabs on factors like user activity, query histories, and changes to data over time can help detect anomalous behavior and raise red flags about risks before they spiral out of control. Knowing when to execute risk remediation and response tactics is key to maintaining data privacy, or in worst case scenarios, minimizing the impacts of unauthorized access.
Data monitoring also plays an important role in generating a data audit trail to prove compliance with regulations like the GDPR and HIPAA, which have strict privacy standards. With this information at their fingertips, data, legal, and compliance teams can respond to data subject access requests (DSARs) and provide individuals with legally required agency over their personal data.
Establishing Privacy Controls for Long Term Success
The strongest privacy controls are those that adapt to an organization’s evolving data needs and remain resilient despite growth and complexity. But a successful implementation also requires human engagement and upkeep.
For instance, data and compliance teams should periodically review their systems and processes to ensure that privacy controls are adequate for current business operations, and make adjustments where necessary. Additionally, training employees on data privacy awareness will help build a culture of security, compliance, and good data hygiene across the organization, which in turn bolsters the effectiveness of privacy controls. When combined with the best practices mentioned above, these steps help cultivate a comprehensive and dynamic approach to data privacy.