AI Risk Starts with Data Risk: DBTA Data Summit Keynote Summary

What the first wave of AI security efforts are missing, and how the Data Layer is where new and critical security and privacy concerns need to be addressed.

July 3, 2024
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Dhruv Jain, Chief Product Officer and Co-Founder

I had the pleasure of introducing my co-founder, Abhishek Das, at DBTA’s annual Data Summit in Boston. Abhishek was invited to present one of the keynote sessions, and we thought it would be a good opportunity to discuss the emerging security challenges as organizations look to harness their proprietary data as part of their artificial intelligence (AI) strategy. 

AI has become a C-level mandate, and every organization is looking to gain a competitive advantage. However, despite this incredible momentum, less than twenty percent of AI projects have reached the production stage. Security of AI applications,  and specifically confidentiality and privacy concerns about the data we feed into them are a major reason for these roadblocks.

AI teams looking to leverage their proprietary data and eliminate hallucination issues, go beyond basic prompt engineering approaches to use Retrieval Augmented Generation (RAG) and Fine-tuning architectures. In fact, 75% of enterprises have already started using these approaches. These architectures were the focus of Abhishek’s presentation, and specifically the data security challenges that come to the fore here. Abhishek, who has decades of experience working with AI and machine learning (ML) applications, outlined a 3-layer stack of such AI applications. Starting from the Inference Layer with the user/application interfaces, to the Model Layer and finally the Data Layer at the bottom. 

Most AI security efforts to date have focused on the Inference layer analyzing prompts/responses, and some on the Model Layer looking at model supply chain issues. However, it is at the Data Layer, where new and critical security and privacy concerns need to be addressed to successfully productionalize RAG & fine-tuning architectures.  

Abhishek detailed 6 primary data security risks. These are  (i) data privacy, (ii) training data poisoning, (iii) prompt manipulation, (iv) unauthorized access, (v) sensitive data exfiltration and (vi) data supply-chain poisoning. These risks can be clearly mapped to industry standard AI security frameworks such as the OWASP LLM Top 10 and the Databricks AI Security Framework

He wrapped up the talk with a brief glimpse of how Acante is squarely addressing these security risks at the Data Layer of the AI stack. Every single bit of organizational data will be at risk of exposure through these AI systems in ways we never fathomed before and ways that aren’t easily comprehensible to humans. Talk to us about how Acante is empowering enterprises with practical ways to unlock the value of their data safely and confidently

Watch the whole keynote above or checkout the slides on our LinkedIn page.

Unveiling the Challenge

As our digital footprint expands, so do the challenges of securing our data assets. Acante.ai recognizes the exponential proliferation and constant change in data access patterns, creating blind spots for traditional security approaches.

The Acante.ai Difference

At Acante.ai, our approach to data security marks a paradigm shift in the industry. Unlike traditional security models that often succumb to the static nature of data threats, Acante.ai thrives on dynamism. We believe that true security evolves with the challenges, and that's precisely what sets us apart. The Acante.ai difference lies in our commitment to providing security teams with more than just a shield; we offer a strategic ally that anticipates, adapts, and fortifies against the unpredictable proliferation of data access patterns. Our solution doesn't just keep pace with the digital transformation journey; it propels it forward. But what truly defines the Acante.ai difference goes beyond technology; it's ingrained in our culture. We are a collective of thoughtful, compassionate, and collaborative individuals on a shared mission to disrupt the security industry. With deep expertise from major brands and startups, we've collectively built over 10 startups, resulting in category-creating businesses, acquisitions, and IPOs. Our success is a testament to the collaborative spirit within our team, where every member contributes to shaping our culture and the future of data security. Join Acante.ai, and experience the difference that drives us to redefine the limits of protection in the digital age.

Dynamic Data Security

Explore the cutting-edge realm of dynamic data security with Acante.ai. In an era where the digital landscape is in a perpetual state of flux, Acante.ai's comprehensive approach to data security becomes not just a solution but a strategic imperative. Imagine a security system that not only reacts to the ever-changing data access patterns but anticipates and adapts in real-time. This level of sophistication is what sets Acante.ai apart. Our solution not only seamlessly integrates with the native controls of your data lakes and warehouse ecosystems but also evolves with them. It's not just about protecting your data; it's about empowering it. Acante.ai's dynamic data security solution is not confined by static parameters; it's a living, breathing shield that moves in harmony with the pulse of your data. As businesses navigate the complexities of the modern data landscape, Acante.ai provides not just a safeguard but a strategic ally, ensuring that security is not a hindrance but an enabler of progress.

Conclusion

In a world where data is both a valuable asset and a potential liability, Acante.ai emerges as a beacon of innovation. Join us on this exploration of the future of data security and discover how Acante.ai is empowering organizations to navigate the evolving landscape with confidence.
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AI Risk Starts with Data Risk: DBTA Data Summit Keynote Summary

I had the pleasure of introducing my co-founder, Abhishek Das, at DBTA’s annual Data Summit in Boston. Abhishek was invited to present one of the keynote sessions, and we thought it would be a good opportunity to discuss the emerging security challenges as organizations look to harness their proprietary data as part of their artificial intelligence (AI) strategy. 

AI has become a C-level mandate, and every organization is looking to gain a competitive advantage. However, despite this incredible momentum, less than twenty percent of AI projects have reached the production stage. Security of AI applications,  and specifically confidentiality and privacy concerns about the data we feed into them are a major reason for these roadblocks.

AI teams looking to leverage their proprietary data and eliminate hallucination issues, go beyond basic prompt engineering approaches to use Retrieval Augmented Generation (RAG) and Fine-tuning architectures. In fact, 75% of enterprises have already started using these approaches. These architectures were the focus of Abhishek’s presentation, and specifically the data security challenges that come to the fore here. Abhishek, who has decades of experience working with AI and machine learning (ML) applications, outlined a 3-layer stack of such AI applications. Starting from the Inference Layer with the user/application interfaces, to the Model Layer and finally the Data Layer at the bottom. 

Most AI security efforts to date have focused on the Inference layer analyzing prompts/responses, and some on the Model Layer looking at model supply chain issues. However, it is at the Data Layer, where new and critical security and privacy concerns need to be addressed to successfully productionalize RAG & fine-tuning architectures.  

Abhishek detailed 6 primary data security risks. These are  (i) data privacy, (ii) training data poisoning, (iii) prompt manipulation, (iv) unauthorized access, (v) sensitive data exfiltration and (vi) data supply-chain poisoning. These risks can be clearly mapped to industry standard AI security frameworks such as the OWASP LLM Top 10 and the Databricks AI Security Framework

He wrapped up the talk with a brief glimpse of how Acante is squarely addressing these security risks at the Data Layer of the AI stack. Every single bit of organizational data will be at risk of exposure through these AI systems in ways we never fathomed before and ways that aren’t easily comprehensible to humans. Talk to us about how Acante is empowering enterprises with practical ways to unlock the value of their data safely and confidently

Watch the whole keynote above or checkout the slides on our LinkedIn page.

The Next Wave of AI Safety Needs to Focus on Data Governanceimage
The Next Wave of AI Safety Needs to Focus on Data Governance

The path to AI success requires organizations to unlock the value of their proprietary data, but in order to do that, they need to ensure that the data they feed into these AI systems, including LLMs, is secure.

Acante Announces Partnership with Commvault to Bring Together the Best of Data Access Governance and Protection for Enterprise Cloud Dataimage
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Seamless integration with Commvault Cloud provides unparalleled cyber resilience in the face of growing ransomware attacks and breaches

AI Risk Starts with Data Risk: DBTA Data Summit Keynote Summaryimage
AI Risk Starts with Data Risk: DBTA Data Summit Keynote Summary

What the first wave of AI security efforts are missing, and how the Data Layer is where new and critical security and privacy concerns need to be addressed.

Databricks has open sourced Unity Catalog: What that means for the ecosystemimage
Databricks has open sourced Unity Catalog: What that means for the ecosystem

Our point of view on why we need unified governance for Data and AI and why we are excited about Databricks releasing Unity Catalog as open source.

Nam quis nulla. Integer malesuada. In in enim a arcu imperdiet malesuada. Sed vel lectus. Donec odio urna, tempus molestie, porttitor ut, iaculis quis
Read now
Nam quis nulla. Integer malesuada. In in enim a arcu imperdiet malesuada. Sed vel lectus. Donec odio urna, tempus molestie, porttitor ut, iaculis quis
Read now
Nam quis nulla. Integer malesuada. In in enim a arcu imperdiet malesuada. Sed vel lectus. Donec odio urna, tempus molestie, porttitor ut, iaculis quis
Read now
Nam quis nulla. Integer malesuada. In in enim a arcu imperdiet malesuada. Sed vel lectus. Donec odio urna, tempus molestie, porttitor ut, iaculis quis
Read now