Shift Left Privacy: A Key Enabler for Cloud and Data Mesh

With the rise of cloud-native services and modern data architectures, data privacy protection is increasingly central to compliance and business operations.

To remain competitive, organizations need to proactively respond to regulatory changes and foster collaboration between privacy and data teams.

A “Shift Left” privacy approach, integrated with privacy-preserving technologies is an effective solution, enhancing protection, reducing costs, and boosting trust and compliance.

What is Shift Left Privacy?

The concept of "Shift Left" in the context of privacy refers to integrating privacy considerations and practices earlier in the software development lifecycle or product development process. Traditionally, privacy concerns have often been addressed as an afterthought or during later stages of development, which leads to vulnerabilities and privacy breaches.

“Shift Left” privacy means that organizations embed privacy controls in the data from the outset. Privacy policy enforcement and automated workflows are critical to modern privacy teams to be able to move at the speed of other business processes. Implementing the Shift Left security and privacy approach allows data to be pre-approved for use, enabling use cases to be carried out without fears of delay or privacy blocks at a later stage.

For example, imagine a retail company that decides to migrate its data operations to the cloud. Traditionally, privacy considerations might be implemented after the data has been moved to the cloud, during the 'data-at-rest' phase.

With a Shift Left Privacy approach, these considerations would be integrated from the earliest stages of the migration process.

As the retail company team plans the migration, they would work in close collaboration with the privacy and security team to identify the different types of data to be moved (such as customer personal data, transaction records, etc.), and understand the associated privacy regulations and requirements.

Based on this understanding, the teams would then apply appropriate privacy controls and tools to automate security.

These actions would protect data not only at rest and in transit, but, most importantly, in use. These controls would be implemented before the data is moved to the cloud, ensuring the cloud security from the outset.

Applying the concept of "Shift Left" to data privacy can bring several key benefits, including:

  • Enhanced privacy protection, reducing the likelihood of privacy breaches and enhancing the overall protection of sensitive data.

  • Cost Reduction. The Shift Left approach helps address privacy concerns early in the development process and avoid costly rework and legal consequences associated with privacy breaches.

  • Improved User Trust and Satisfaction. Privacy is a significant concern for users, and organizations that prioritize data privacy are more likely to gain user trust and loyalty.

  • Compliance with Privacy Regulations. Shifting left on data privacy helps organizations stay compliant with privacy regulations and frameworks, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).
In implementing the Shift Left approach, collaboration between data privacy and security teams, as well as product development teams plays a pivotal role.

By working together from the early stages, these devops and security teams can proactively identify and mitigate privacy risks while striking a balance between privacy requirements and technical feasibility.

The Shift Left approach allows business, privacy, and development teams to move faster and in unison, without coming into conflict at a later stage of the project.

Common Challenges of Shift Left Privacy

As organizations modernize and scale up their operations, they encounter a set of common data privacy hurdles and security issues.

Traditional protection mechanisms, such as consent, contracts, and perimeter controls, are becoming less effective.

While international treaties and legal agreements offer some recourse after a privacy breach, they don't prevent such breaches from happening.

In this context, both privacy and security teams play an important role in avoiding security flaws and designing robust defense mechanisms that can pre-empt potential breaches.

In the era of cloud storage and processing, there's a pressing need to implement technical controls to secure sensitive information and prevent unauthorized access. One way to achieve this is by introducing security controls that are tightly integrated with the data lifecycle.

A common issue, however, is that data protection technologies have traditionally been a trade-off: bolstering security often compromises the data utility. This imbalance can cause tension within businesses, as teams working with the data need access to detailed and comprehensive datasets to achieve their objectives.

Additionally, organizations are required to meet global privacy regulations like GDPR and CCPA. It's vital that companies shift from a reactive stance to a proactive one when it comes to changes in their privacy protection approach.

Those who wait for regulatory changes to occur often find themselves lagging in innovation and struggle to respond proactively to security concerns.

To overcome these hurdles, organizations should consider a dynamic privacy protection approach, which adapts to individual use cases and evolves with time.

For example, Anonos Data Embassy utilizes the Shift Left Privacy approach by embedding adjustable security controls early in the development cycle or/and in the data processing pipeline, creating secure versions of data called Variant Twins.

Variant Twins can be utilized in the cloud or for any given use case. The use of security automation in this context ensures a smooth integration of privacy controls, minimizing potential disruption to data utility.

Integrating Shift Left Privacy Into Data Mesh Architecture

Data architectures such as data mesh have come to the fore. Data mesh revolutionizes the data landscape by decentralizing data ownership, treating data as a product, and organizing it by specific business domains. Each team, or 'domain', within an organization assumes responsibility for their data, ensuring it's fit for use.

Metadata plays a crucial role within this architecture. Metadata, in the context of a data mesh, is essentially information about the data - detailing its origin, quality, compliance with privacy policies, and more. It assists in navigating the distributed nature of a data mesh, allowing domains to understand and utilize data more effectively.

When we apply the Shift Left security and privacy approach to cloud-native applications and data mesh architecture, privacy considerations become integrated from the very start of the data lifecycle.

Privacy controls are 'shifted' to the left, i.e., they are implemented early on in the process of data creation, storage, and usage.

Implementing the Shift Left Approach with Anonos' Variant Twins

Incorporating the Shift Left Privacy approach, along with cloud and data mesh, introduces new possibilities for data usage while ensuring robust enforcement of privacy and security policies.

Anonos’ patented technology, Variant Twins, plays a key role here. Variant Twins are protected outputs of original datasets with built-in privacy protections. These are created by embedding controls that enforce policies from the outset, clearly outlining what level of protection is required.

These Variant Twins can then be disseminated via a data architecture like Informatica’s Inteligent Data Management Cloud (IDMC).

With IDMC, because Variant Twins are already associated with privacy requirements, they become part of the data catalog in the data mesh and can be freely leveraged within the IDMC environment.

Users can trust in the quality and compliant use of these datasets, as the metadata and approved uses are inherently tied to them.

By automating the process of privacy policy application and data approvals, the speed of project approval can be significantly increased.

Moreover, with the integration of machine learning and AI applications, privacy protection can be taken a step further. This transformative approach shifts from reliance on paper contracts to a data-driven environment where privacy protection is embedded within the data itself, thus ensuring trust.
Shift Left Privacy and Automated Data Mesh Data Delivery. Anonos Data Embassy / Informatica IDMC Reference Architecture.
Shift Left Privacy and Automated Data Mesh Data Delivery. Anonos Data Embassy / Informatica IDMC Reference Architecture.


With the increasing adoption of modern data architectures, the need for effective privacy protection becomes critical.

Traditional solutions like contracts are insufficient by themselves to prevent breaches.

A Shift Left privacy approach, embedding protections directly into data, can revolutionize data privacy. This allows for broader data usage, enabling innovation while ensuring protection and compliance.

By applying privacy and security tools such as Anonos Variant Twins, privacy-protected data can be accessed and processed by data users, who know that the data is already safe and trusted to use.

This revolution in data privacy allows greater data enablement, a wider range of use cases, and data protection that actually encourages innovation, rather than inhibiting it.