What's the Challange?
Everyone is hungry for data, but most organizations rigorously control who can see what data; preventing free, self-service access, keeping it safely locked behind ironclad barriers. The risks associated with exposing sensitive client and customer data to unauthorised parties demands this control. In the past, security constraints were driven by the threat of regulatory fines and brand destruction, but today there is even more at stake as company directors are ultimately held responsible, even risking jail time.
Not surprisingly, this fuels into the development of a strong risk-averse culture where it takes weeks, or even months to secure data approvals, leaving ideas unrealised and innovation delayed. Competitive advantage and new revenues that are sacrificed at the cost of mitigating possible risks. Operational agility demands ready access to sensitive data for blue sky innovation and rapid experimentation. So why is it constantly thwarted with teams sitting idle lacking the ability to work with sensitive data?
What’s Limiting Data Collaboration?
Complex approval processes and rigid data sharing policies exist due to a myriad of regulatory controls like GDPR and HIPPA. Further data movement constraints are found in nearly every country, all different in their restrictive covenants and driven by different agendas. Compound this with the ever present threat of data breaches and data leaks, and we have sufficient reason for ever stronger defensive positioning when it comes to accessing and collaborating with sensitive data. Sensitive data sharing becomes a significant hurdle due to these constraints.
There’s also the problem with data itself, usually disappointing in quality and shape, and often lacking in sufficient volume.
It’s well understood that the more data you feed a model the more accurate it becomes, but how do you solve this extreme data drought given the risks and restrictions that exist?
Enter Synthesized
The team at Synthesized has a deep understanding of all of these data challenges. It’s why we created the Synthesized DataOps Platform - to solve the problem of sharing and collaborating with sensitive data. Synthesized leverages sophisticated AI power to intelligently create artificial replicas of original data as measured across two dimensions: utility and privacy.
And we believe we do this better than anyone else.
Synthesized Data Utility
Our Machine Learning experts have perfected how Synthesized creates accurate, new data points using a sophisticated AI model built from a deep understanding and analysis of the original data. The performance of new Synthesized datasets can be proven using a range of common and advanced statistical metrics to demonstrate its utility closely matches the original data. We can use Machine Learning models to further demonstrate the accuracy and performance of Synthesized data compared to the original. Sensitive data sharing is made efficient with Synthesized, ensuring high utility.
Synthesized Data Privacy
Data masking and data anonymisation are insufficient means of ensuring data privacy, as they can be easily attacked by modern techniques and they drastically reduce data quality. Unlike these poor techniques, our unique Synthesized approach learns the complex statistical relationships in the data to automatically Synthesize new intelligent samples at any volume.
Synthesized data points are entirely new and did not previously exist, but fully preserve the quality and performance of the original set. The same cannot be said of other approaches. Sensitive data sharing is secured with Synthesized's advanced privacy measures.
In addition to preserving data quality, we have tested our approach using a variety of attacks, including statistical linkage attacks, and with many different datasets to ensure privacy is fully preserved and no confidentiality is broken.
Enabling Collaboration with Synthesized Data Clean Rooms
Data access and sharing has never been easier, faster, or more secure. Synthesized Data Clean Rooms are secure, isolated environments created by the platform to streamline data collaboration with Synthesized data between internal teams and with external parties. They are tightly integrated with your enterprise logging and monitoring tools, providing a full audit of all data access and data movement. This innovative approach to sensitive data sharing enhances collaboration while maintaining strict privacy and utility standards.
FAQs
What is sensitive data sharing and why is it important?
Sensitive data sharing refers to the process of allowing authorized parties to access and utilize sensitive information, such as personal, financial, or proprietary data, while ensuring its privacy and security. It's important because it enables organizations to collaborate, innovate, and make informed decisions without compromising the confidentiality and integrity of the data. Effective sensitive data sharing can lead to better business outcomes, such as enhanced product development, improved customer service, and more accurate analytics.
What are the primary risks associated with sensitive data sharing?
The primary risks of sensitive data sharing include data breaches, unauthorized access, data leaks, and non-compliance with regulatory requirements. These risks can lead to significant financial losses, legal penalties, and damage to an organization's reputation. To mitigate these risks, it's crucial to implement robust security measures, such as encryption, access controls, and regular audits, as well as to comply with relevant data protection regulations.
How can organizations balance the need for data access with the need for security in sensitive data sharing?
Organizations can balance data access and security in sensitive data sharing by adopting a data governance framework that defines clear policies and procedures for data access, usage, and sharing. This includes implementing role-based access controls, ensuring that only authorized personnel can access sensitive data, and using advanced technologies like data masking and synthetic data generation to protect data privacy. Additionally, fostering a culture of security awareness and providing regular training to employees can help maintain this balance.
What technologies are available to facilitate secure sensitive data sharing?
There are several technologies available to facilitate secure sensitive data sharing, including data encryption, data masking, and synthetic data generation. Data encryption ensures that data is unreadable to unauthorized users, while data masking replaces sensitive information with fictitious but realistic data. Synthetic data generation creates artificial data sets that mimic the properties of real data without exposing actual sensitive information. These technologies, when combined with secure data collaboration platforms and data clean rooms, can significantly enhance the security of sensitive data sharing.
What are some best practices for ensuring compliance with regulations during sensitive data sharing?
To ensure compliance with regulations during sensitive data sharing, organizations should:
- Conduct regular data privacy assessments and impact analyses to identify potential risks and vulnerabilities.
- Implement comprehensive data protection policies and procedures that align with relevant regulations, such as GDPR, HIPAA, and CCPA.
- Use secure methods for data transmission and storage, such as end-to-end encryption and secure cloud services.
- Maintain detailed records of data access and sharing activities to provide an audit trail and demonstrate compliance.
- Educate employees about data privacy laws and the importance of following best practices for sensitive data sharing. By adhering to these best practices, organizations can mitigate the risks associated with sensitive data sharing and ensure they remain compliant with legal requirements.
