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Rishabh Poddar
Co-Founder and CEO at Opaque Systems (We are hiring!) UC Berkeley PhD
Professional Background
Rishabh Poddar is a distinguished professional and entrepreneur in the field of computer science. With an impressive academic background, including a Doctor of Philosophy (PhD) in Computer Science from the prestigious University of California, Berkeley, Rishabh brings a wealth of knowledge and expertise to his endeavors. He is currently the Co-Founder and CEO of Opaque Systems, where he leads innovative solutions that bridge the gap between technology and everyday applications.
In addition to his current role, Rishabh has accumulated a wealth of experience through his various positions in leading tech organizations. He began his professional journey as a Research Software Engineer at IBM, where he was involved in cutting-edge software solutions and contributed to significant projects that influenced the tech landscape. His analytical skills and technical acumen led him to The Boston Consulting Group, where he served as an Associate, gaining valuable insights into business strategy and operational efficiency.
Rishabh also spent time as a Research Intern at Microsoft, where he had the opportunity to work on advanced research projects that harnessed the power of technology to drive innovation. This experience further solidified his commitment to pushing the boundaries of what is possible in computer science and technology.
Education and Achievements
Rishabh Poddar's academic journey is marked by a rigorous pursuit of knowledge and excellence. He completed his Bachelor of Technology (B.Tech.) in Computer Science & Engineering at the Indian Institute of Technology, Kharagpur (IIT Kharagpur), renowned for its top-tier engineering programs and distinguished alumni. His time at IIT Kharagpur laid a solid foundation in core computer science concepts, algorithms, and engineering practices, setting the stage for his advanced studies.
At the University of California, Berkeley, Rishabh's PhD studies further enhanced his understanding of computer science and established him as an expert in his field. Berkeley's emphasis on research and innovation provided him with the platform to explore complex problems and develop pioneering solutions that address real-world challenges. Rishabh's dissertation work and contributions to the academic community reflect his commitment to advancing knowledge in computer science.
Achievements
Rishabh's accomplishments extend beyond his formal education; his career is a testament to his innovative thinking and leadership. As Co-Founder and CEO of Opaque Systems, Rishabh is at the forefront of developing technology that focuses on data privacy, security, and usability. Under his leadership, Opaque Systems has gained recognition for its innovative solutions, making a significant impact on how organizations handle sensitive information.
Throughout his career, Rishabh has demonstrated a remarkable ability to connect complex technical concepts with practical applications, which makes him a sought-after expert in the tech industry. He has contributed to numerous projects that enhance software development and data management practices across diverse sectors, including government, healthcare, and financial services.
In addition to his business ventures, Rishabh is also involved in mentorship and thought leadership within the tech community. He actively participates in conferences and workshops, sharing insights on technology trends, data privacy, and software engineering best practices. His dedication to the field and passion for fostering innovation reinforces his status as a leader and influencer in the tech landscape.
Highlights
With great data comes great responsibility.
The responsibility is becoming even greater as data increases at exponential rates and businesses race to implement AI.
For IT leaders, this rapid accumulation of data means keeping compliant with regulations, ensuring customer privacy, and protecting proprietary information are becoming challenging. Businesses today are often forced to rely on large (and expensive!) teams of data scientists to manually redact, encrypt, or add noise to information so it remains confidential and compliant. As data continues to pour in, these time-consuming processes for anonymization won’t hold up much longer. And they are less safe with emerging AI technologies.
With AI in the mix, the data privacy problem gets more complicated. AI introduces unprecedented security risks that many companies still aren’t equipped to deal with. Leaders may want to leverage AI in their workflows, but are concerned about how these models may use—and potentially disclose—sensitive information.
If your company is thinking about data privacy—especially as it relates to AI implementation, give this post a read. Opaque offers a hardware root of trust with confidential computing to tackle the challenges above. Read on to understand how.
While it might not be surprising anymore to learn that large language models (LLMs) are capable of memorizing training data, a recent study reveals another critical issue: the possibility of training data extraction attacks on AI models that are designed to avoid such vulnerabilities. Despite efforts to protect data by aligning models to human values and objectives, models like ChatGPT (which is designed to avoid regurgitating large amounts of training data) can still inadvertently memorize and expose the data they were trained on. This is a major concern, especially when the data is sensitive.
The implications of training data extraction attacks are significant, affecting data privacy and security. It's crucial for businesses to be aware of these potential threats and take proactive steps to mitigate them. One approach is to leverage retrieval-augmented generation (RAG) in combination with Opaque’s confidential computing solution, which enables data to be processed without exposing its sensitive contents. In addition to protecting sensitive data in prompts, this approach circumvents the need to train models on confidential data, mitigating potential threats like data extraction attacks.
As we navigate the exciting future of AI, it's our responsibility to ensure that AI systems are not only efficient and effective but also secure and trustworthy. Prioritizing data safety and privacy is as crucial as leveraging the benefits of AI. Reach out to us to learn how you can harness the power of AI for your business without compromising privacy.
To learn more about data extraction attacks, their implications, and how to mitigate their risks: 📖 Read the research team’s article: https://lnkd.in/g8BEswjq 🚀 Book a demo: https://lnkd.in/gR_h234r
#ArtificialIntelligence #DataPrivacy #DataSecurity #ConfidentialComputing #ConfidentialAI
