As AI development outpaces the availability of real-world data, synthetic data is quickly becoming a compelling alternative. It’s scalable, flexible and less encumbered by privacy constraints – but it also comes with real questions around accuracy, security and compliance.
The concept of synthetic data isn’t new – in fact, it’s been around for decades. However, with AI’s insatiable appetite for data, synthetic data has been thrust into the limelight.
That’s why it’s critical for organizations exploring synthetic data to follow a pragmatic, privacy-first roadmap. We’ll draw one up today, breaking down how teams can position themselves to innovate confidently and ethically in a privacy-sensitive world.
Synthetic data’s strengths and weaknesses
The upside of synthetic data is multifaceted, but ultimately comes down to one word: control. Synthetic data is inherently malleable, allowing users a far greater degree of freedom in how they source and leverage it.
The task of training and deploying an AI model calls for a tremendous amount of data, a requirement more easily met through synthetic data use. This data can be generated both on demand and in near-limitless supply, making scalability a given.
Synthetic data also offers superior flexibility. Its generation is faster and more cost-effective than gathering and organizing real-world data, putting less strain on an organization’s resources. It can be tailored to the specific requirements of the task at hand, whether through multimodal data generation or targeted augmentation. This allows users to shape the data to help achieve the desired end result.
Perhaps most importantly, synthetic data can be engineered to be free of personally identifiable information (PII), alleviating privacy concerns. This is a boon to collaboration, making it easier for teams to maneuver by reducing the security risks of working with PII.
However, the advantages of synthetic data are accompanied by just as many drawbacks.
Perhaps the most difficult issue to overcome is accuracy. By definition, synthetic data lacks realism, and can fail to reflect the complexities of real-world datasets. Only by comparing synthetic data to the real thing can users ensure their synthetic data is representative and unbiased.
It’s also crucial for those working with this technology to remember that ‘synthetic’ doesn’t automatically mean ‘risk-free.’ The methods used to generate this data can still present significant risks. For instance, the potential for re-identification – the reverse-engineering of the de-identification process – remains a real concern.
Therefore, all the usual data protection principles still hold true. Regulations like GDPR, CCPA, and HIPAA must be adhered to diligently. Furthermore, any synthetic data that is made public needs robust safeguards against manipulation.
Key considerations
How can organizations venturing into the realm of synthetic data navigate these challenges? Several considerations come to the forefront.
First, planning the entire process is paramount, even where speed is typically valued. Understanding the ‘how’ behind the synthetic data generation is crucial for building trust and ensuring its usefulness.
Thorough documentation of every step is also essential, enabling repeatability and providing necessary explanations, especially when ethics and security questions arise.
Organizations should proactively test their synthetic data against known vulnerabilities and weaknesses inherent in its creation to identify and mitigate potential risks.
Finally, given the increasing demand for transparency from customers, organizations should adopt a ‘start with the end in mind’ approach, ensuring their synthetic data practices are clear, understandable and prepared for scrutiny.
Building internal safeguards
To effectively safeguard user privacy internally, organizations should prioritize several key measures.
Companies should adhere to well-established security frameworks like the NIST CSF and conduct regular self or third-party audits. Doing so provides a robust foundation for data protection.
Additionally, implementing role-based access control (RBAC) alongside comprehensive event logging and alerting forms the essential ‘blocking and tackling’ of security, ensuring only authorized personnel can access specific data, and that any suspicious activity is promptly detected.
Maintaining data integrity through methodologies that prevent unauthorized manipulation is crucial for preserving the trustworthiness of user information.
Finally, acknowledging the inherent risks of working with data necessitates addressing data residency and expiration. Establishing clear policies for the data lifecycle, including the secure disposal of data when it’s no longer needed to support customers, significantly minimizes potential exposure.
As AI technology and adoption continue to advance, it can be tempting to get caught up in the buzz and leap without looking. But the last thing newcomers to synthetic data usage can afford is to plow ahead at security’s expense.
There are right and wrong ways to go about harnessing the potential of synthetic data. If handled correctly, it can be a powerful catalyst for innovation.
