Privacy by Design, an idea born in the 1990s, pushes for privacy to be integrated from the very inception of any technological solution. In the context of the Software Development Lifecycle (SDLC), this means weaving privacy considerations into each stage.
The development and adoption of Artificial Intelligence (AI) has added another layer of complexity to this issue. In an era of revolutionary AI, software developers must ensure that privacy is not an afterthought but a foundational element.
Shifting Left in the Software Development Life Cycle
To guarantee privacy, much like with cybersecurity, we need to “shift left.” By this term, we mean ensuring that privacy considerations start early and remain consistent throughout the product development lifecycle.
TrustArc’s updated Nymity Privacy Management Accountability Framework™ (PMAF), which accounts for recent AI developments, recognizes this need. Our 4th imperative, “Embed Data Privacy Into Operations,” now includes integrating privacy into the SDLC. Further, a new operational template provides specific guidance on the need to “integrate data privacy into the System Development Lifecycle”.
Of course, the principles of privacy by design apply at every stage of development, but let’s delve into how certain principles should be emphasized at each of the five stages of the SDLC:
1) Design Stage (Initial Design, Architecture, Research & Requirements Definition)
Be proactive, not reactive, preventative, not remedial. Embed privacy into the design.
At the design stage, consider potential privacy risks and design solutions to address them. For instance, ensure that data collection is minimal and relevant. Privacy needs to be the default. Design software to have user data protection on by default. Users should not need to take extra steps to secure their privacy.
2) Development (Software Development and Unit Testing)
Ensure full functionality. Ensure end-to-end security. Developers should be trained to write secure, privacy-compliant code. Any tools or libraries incorporated should also uphold these standards.
Ensure that implementing privacy measures does not hamper the software’s functionality. Use encrypted protocols, secure databases, and build anonymization techniques for data early in data engineering pipelines.
3) Testing (Development Completed and Integration Testing)
Ensure visibility and transparency. Respect user privacy. Testing should include checks for transparency in how data is used and ensuring there are no hidden processes. Include user testing to understand and respect user privacy concerns. This involves making sure users are aware of data collection and usage practices.
4) Deployment (Testing Completed and Deployment to Production)
Ensure end-to-end security. Regularly update software to patch vulnerabilities. Deploy threat modeling and penetration testing to identify potential weak spots, early and then regularly when updating any code. Being preventative, not remedial, requires monitoring for potential breaches or vulnerabilities and addressing them promptly. Provide visibility and transparency, with documentation and clear communication to users about data practices.
5) Post Deployment (Ongoing Operations and Maintenance)
Respect user privacy. Schedule regular reviews of user feedback regarding privacy concerns and be sure to address them. Engineer data warehouses and pipelines with expressed means for deletion and retention issues, both ensuring that data is not retained longer than necessary and that end users can have their data promptly removed. Implement and regularly audit data deletion protocols.
These protocols are part of good data stewardship. They require that you continually ensure data ownership rights are clear and respected. Users should have the right to access, edit, or delete their data.
Why AI Makes Privacy by Design Especially Critical
AI presents unique challenges that heighten the importance of integrating privacy considerations throughout the SDLC. As AI systems rely heavily on large and diverse datasets to learn, this can pose unique threats to individual privacy. The inherent nature of AI to constantly evolve and learn also complicates static privacy protocols.
Additionally, it is unlikely most companies will be developing AI products from scratch. Instead, they will be integrating technology from commercially available and/or open-source communities and then adapting and training these to their needs. This approach makes practical sense, but it also brings with it considerable privacy exposure.
TrustArc’s updated Privacy Management Accountability Framework™, which includes activities like “Maintaining defined roles and responsibilities for third parties (e.g. partners, vendors, processors, customers)” as part of “Managing Third Party Risk” is so important.
AI Amplifies the Necessity of Adhering to Privacy by Design Principles
High Stakes of Data Breaches: AI applications can potentially handle vast amounts of personal data. A data breach in an AI context means the exposure of sensitive data on a massive scale.
Invasive Data Collection: AI applications, particularly those relying on deep learning, might collect more data than is immediately necessary, justifying it for potential future needs or improved model accuracy.
Interpreting Encoded Data: Even if data is anonymized, AI algorithms might decipher patterns that can re-identify individuals.
Algorithmic Management and Workplace Surveillance: AI systems can be used to manage workers, monitor their every move, analyze their productivity, and even predict future behaviors. Here, the principles of “visibility and transparency” and “respect for user privacy” become crucial. Workers must know if they are being monitored and what data the AI collects.
Software development that incorporates AI and biometric surveillance is another clear example. We accept biometric privacy intrusion when traveling internationally or before boarding a plane. What other applications do we accept its use?
From the early software Design Stage onward, important privacy questions need to be thought through.
As vehicles become smarter, there is potential for biometric data collection, like monitoring drivers’ eye movements or heart rates. A case could easily be made for reducing vehicle theft, reducing drunk driving, always obeying speed limits, the list goes on. For all the potential positive use cases argued for safety and security purposes, where is that data gathered, how is it trained, who owns it, and who has access to it?
The Fine Line Between Data Usage and Privacy
Incorporating Privacy by Design principles at each stage of the software development lifecycle is a complex yet crucial endeavor in the new age of AI. As AI systems become an integral part of our daily lives, the line between acceptable data usage and privacy invasion blurs.
It is imperative for organizations to prioritize privacy, not just as a legal obligation, but as a foundation of trust with their users and in their contribution to society.
By embedding Privacy by Design principles into each stage of the SDLC, and joining into broader conversations on its acceptable use, software companies can ensure that they are not only compliant with regulations but also earn the trust and respect of their stakeholders.