With National Data Privacy Week upon us, we reflect on the shifting data environment. From concerns surrounding artificial intelligence (AI) to an evolving threat environment, there is as great a need as ever to be mindful in our approach to protecting data. Our Karsun Innovation Center experts have met this challenge producing new solutions to address future requirements.
Our teams have a long history of incorporating emerging technologies into our data solutions. We build data platforms that produce meaningful insight while protecting sensitive data. Whether that occurs through machine learning (ML) for business intelligence or incorporating well-architected practices into our data-led migration. Moreover, many of these novel solutions incorporate machine learning and AI to further enhance data privacy.
Utilizing Synthetic Data
Synthetic data enables enterprise data teams to innovate securely. This data is produced through machine learning using models that learn the patterns, structures, and relationships within the real dataset. Next, artificial, or synthetic, data is produced with similar statistical properties to the original data set. Thus, this process masks sensitive data, such as personally identifiable information (PII).
By providing high-quality datasets that mirror real-world data without exposing this data, synthetic data minimizes privacy risks, supports compliance with regulations like HIPAA, and reduces the impact of data breaches. Meanwhile, by allowing safe data sharing and model training, synthetic data accelerates AI and analytics development while ensuring ethical data practices, making it a powerful tool for balancing privacy and innovation. Introducing strong synthetic data practices can be one of the steps organizations can take to prepare their data for an AI-enhanced future.
Security Automation
We can apply both predictive AI and generative AI (GenAI) and enhance organizations’ security posture by strengthening their security automation. While predictive AI identifies threats, GenAI creates new pathways to addressing security concerns. GenAI is particularly well suited to automating security. When incorporated into an AI-powered platform, it enhances safety culture by applying guardrails, best practice policies, templates, and automations that proactively address security concerns. GenAI further enhances security through its self-healing mechanisms that assess threats and then incorporate those assessments into its policy recommendations.
GenAI to Meet Regulatory Requirements
GenAI also offers significant potential for meeting regulatory requirements as modernization teams migrate legacy systems. We have written about this extensively on the ReDuX website. ReDuX is our AI-powered platform that accelerates mainframe modernization. One component of that platform is a mapping feature that builds a blueprint of the legacy system. With an enhanced understanding of the legacy system, the team avoids security pitfalls, identifying functional code while removing dangerous dead code and reducing the risks and errors. Moreover, using a platform with built resources allows teams to introduce guardrails like those used to improve security automation.
Bringing It Together
Consider then a data project where each of these methodologies is included as part of a robust data practice. First, synthetic data is produced using machine learning. When combined with AI-assisted development, like that used by ReDuX, the security automation guardrails enforced by the AI-powered platform ensure proper security tools and practices, including those applied to synthetic data, are used properly every time. Then, as AI practices evolve, they are refined further.
AI/ML helps technology teams navigate complex regulatory landscapes, including compliance with standards like HIPAA, FISMA, and GDPR, to ensure data privacy and system security. By adopting an AI-enhanced approach, agencies can protect privacy, overcome regulatory challenges, and maintain secure and resilient applications to align with their data goals. To learn more about emerging technology from the Innovation Center, visit our Projects Page or take the first step on your data modernization journey and connect with us on our Data Solutions Page.
Every summer Karsun embeds interns in our Innovation Center to work alongside our technology experts, prototyping solutions to support our customers. 2022 Intern Akhilesh Varanasi used synthetic data to address a common privacy concern, personally identifiable information (PII.) Using synthetic data, an artificial set of data is created to perform ML/AI work preventing exposure of sensitive PII. In the interview below, Akhilesh describes his experience in the Karsun Innovation Center and his synthetic data internship project.
First, please tell us about yourself. Where are you going to school? What are you studying? What do you like to do in your free time?
Hi all! My name is Akhilesh Varanasi. I’m currently a rising junior at the University of Washington in Seattle, where I’m a double major in Computer Science and Astronomy. In my free time, I like reading and playing basketball.
Could you share a little bit about the project you worked on as part of this internship? What challenges does it solve? What technologies and tools are you using?
For most of my internship, I worked on the Synthetic Data project. The purpose of this project was to create PII anonymized ‘fake’ data for Machine Learning/Artificial Intelligence use cases. I mostly worked with Python, the Synthetic Data Vault libraries, and graphing frameworks like matplotlib. My main tasks were to create accurate Synthetic Data models and to find generic ways to graphically represent all forms of tabular data. I also worked with AWS Lambda and the AWS CLI to run tests.
What is your favorite part about working with the Karsun Innovation Center? Is there a weekly meeting or ritual you enjoy? The opportunity to learn more or get a new certification?
My favorite parts of working at the Karsun Innovation Center were the input I had in the development process and the team I worked with. I always felt like my opinion was respected at meetings, even in a room full of people that were far more experienced than me. I also had a great time working with the team, everyone was so willing to help each other and it felt like a comfortable, collaborative environment.
What is the biggest takeaway from your experience as an intern at Karsun?
My biggest takeaway from my experience at Karsun is that taking initiative is important. To be a valuable part of a team I have to research topics by myself and come up with goals to structure my approach to a problem.
Akhilesh was mentored by Srikanth Devarajan, Director, Karsun Innovation Center Data Practice.
Every summer Karsun embeds interns in our Innovation Center to work alongside our technology experts, prototyping solutions to support our customers. 2022 Intern Akhilesh Varanasi used synthetic data to address a common privacy concern, personally identifiable information (PII.) Using synthetic data, an artificial set of data is created to perform ML/AI work preventing exposure of sensitive PII. In the interview below, Akhilesh describes his experience in the Karsun Innovation Center and his synthetic data internship project.
First, please tell us about yourself. Where are you going to school? What are you studying? What do you like to do in your free time?
Hi all! My name is Akhilesh Varanasi. I’m currently a rising junior at the University of Washington in Seattle, where I’m a double major in Computer Science and Astronomy. In my free time, I like reading and playing basketball.
Could you share a little bit about the project you worked on as part of this internship? What challenges does it solve? What technologies and tools are you using?
For most of my internship, I worked on the Synthetic Data project. The purpose of this project was to create PII anonymized ‘fake’ data for Machine Learning/Artificial Intelligence use cases. I mostly worked with Python, the Synthetic Data Vault libraries, and graphing frameworks like matplotlib. My main tasks were to create accurate Synthetic Data models and to find generic ways to graphically represent all forms of tabular data. I also worked with AWS Lambda and the AWS CLI to run tests.
What is your favorite part about working with the Karsun Innovation Center? Is there a weekly meeting or ritual you enjoy? The opportunity to learn more or get a new certification?
My favorite parts of working at the Karsun Innovation Center were the input I had in the development process and the team I worked with. I always felt like my opinion was respected at meetings, even in a room full of people that were far more experienced than me. I also had a great time working with the team, everyone was so willing to help each other and it felt like a comfortable, collaborative environment.
What is the biggest takeaway from your experience as an intern at Karsun?
My biggest takeaway from my experience at Karsun is that taking initiative is important. To be a valuable part of a team I have to research topics by myself and come up with goals to structure my approach to a problem.
Akhilesh was mentored by Srikanth Devarajan, Director, Karsun Innovation Center Data Practice.
Every summer Karsun embeds interns in our Innovation Center to work alongside our technology experts, prototyping solutions to support our customers. 2022 Intern Akhilesh Varanasi used synthetic data to address a common privacy concern, personally identifiable information (PII.) Using synthetic data, an artificial set of data is created to perform ML/AI work preventing exposure of sensitive PII. In the interview below, Akhilesh describes his experience in the Karsun Innovation Center and his synthetic data internship project.
First, please tell us about yourself. Where are you going to school? What are you studying? What do you like to do in your free time?
Hi all! My name is Akhilesh Varanasi. I’m currently a rising junior at the University of Washington in Seattle, where I’m a double major in Computer Science and Astronomy. In my free time, I like reading and playing basketball.
Could you share a little bit about the project you worked on as part of this internship? What challenges does it solve? What technologies and tools are you using?
For most of my internship, I worked on the Synthetic Data project. The purpose of this project was to create PII anonymized ‘fake’ data for Machine Learning/Artificial Intelligence use cases. I mostly worked with Python, the Synthetic Data Vault libraries, and graphing frameworks like matplotlib. My main tasks were to create accurate Synthetic Data models and to find generic ways to graphically represent all forms of tabular data. I also worked with AWS Lambda and the AWS CLI to run tests.
What is your favorite part about working with the Karsun Innovation Center? Is there a weekly meeting or ritual you enjoy? The opportunity to learn more or get a new certification?
My favorite parts of working at the Karsun Innovation Center were the input I had in the development process and the team I worked with. I always felt like my opinion was respected at meetings, even in a room full of people that were far more experienced than me. I also had a great time working with the team, everyone was so willing to help each other and it felt like a comfortable, collaborative environment.
What is the biggest takeaway from your experience as an intern at Karsun?
My biggest takeaway from my experience at Karsun is that taking initiative is important. To be a valuable part of a team I have to research topics by myself and come up with goals to structure my approach to a problem.
Akhilesh was mentored by Srikanth Devarajan, Director, Karsun Innovation Center Data Practice.