How We at Redsand Ventures Enable Private AI with Decentralized Edge Solutions

As large language models (LLMs) continue to evolve, our team at Redsand Ventures is deeply committed to addressing the challenges surrounding data security and the handling of sensitive personal and business information. Our focus lies in enabling Private AI through a decentralized approach that assures physical capacity in the most flexible way.

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A recent AI Security & Governance Report released by Immuta highlights key concerns held by data experts about Artificial Intelligence (AI). Over half of the respondents cited the inadvertent exposure of sensitive information by LLMs as their greatest worry. We understand these concerns and are equally vigilant about the risks posed by user prompts and AI attacks initiated by threat actors.

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Similarly, the Open Worldwide Application Security Project, a not-for-profit foundation improving cybersecurity, has identified data leakage as one of the most significant threats to the LLMs underpinning generative AI technologies.

Data Leaks and Bad Actors

A major risk we observe is the potential for LLMs to leak details about the data on which they were trained. While LLMs aren't inherently insecure, their reliance on vast resources and immense size can sometimes lead to vulnerabilities. Sensitive or private data may inadvertently be used as a source, and if the model's architecture is not robust, this information could become publicly accessible.

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For instance, in 2023, researchers at Robust Intelligence uncovered vulnerabilities in Nvidia’s NeMo Framework that allowed private data from a database to be extracted. This framework, which powers generative AI applications like chatbots, highlighted the risks of certain features being exploited.

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We also recognize that AI models can be targeted through adversarial attacks or data poisoning. In these scenarios, malicious actors manipulate inputs to produce incorrect outputs or corrupt training datasets through unauthorized access. Strong data encryption and a secure, decentralized infrastructure at the edge provide a powerful solution to these challenges.

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As Christopher Budd from Sophos X-Ops noted earlier this year: "As vulnerabilities and malware targeting AI tools emerge, attackers are finding ways to exploit them. Over the next year, we'll see a clearer picture of these risks. Users and security professionals must work together to address these vulnerabilities and safeguard against the inevitable attacks that follow."

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Furthermore, reliance on third-party providers for AI infrastructure, such as cloud services, increases the risk of security breaches. A notable example is the AT&T breach in 2024, where hackers exploited vulnerabilities in third-party systems, compromising over 50 billion records from 70 million customers. Such incidents underscore the importance of a decentralized approach to reduce reliance on external systems.

Redsand Ventures' Private AI Edge Solutions

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At Redsand Ventures, we have developed solutions that empower organizations to deploy Private AI effectively while ensuring the highest levels of data security. Our decentralized, edge-focused infrastructure allows us to deliver dedicated, containerized compute capacity, removing dependencies on external cloud providers and minimizing exposure to third-party risks like outages, breaches, or vendor lock-in.

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Our colleague Nicole Anderson encapsulates our approach perfectly: "Private AI compute ensures that sensitive data used to train and fine-tune LLMs remains within national or organizational boundaries, fully aligning with data sovereignty laws and regulatory requirements. Hosting them at the edge provides unparalleled flexibility, ensuring that organizations have full control over their proprietary models and IP while meeting their operational needs."

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This edge-based model allows us to allocate and assure physical capacity dynamically, where it is most needed, providing our clients with an agile and resilient solution.

Navigating Compliance and Regulation

The regulatory landscape presents significant challenges for AI deployment, with stringent data protection laws such as GDPR and CCPA adding complexity. As the International Association of Privacy Professionals (IAPP) has noted, there is no universal approach to regulating AI, and nations continue to develop their own standards.

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Many authorities prohibit storing confidential personal or corporate information on servers outside their legal jurisdictions. Ensuring compliance requires an in-depth understanding of regulatory requirements across multiple regions.

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Sandeep Dama, another esteemed member of our team, highlights how Redsand Ventures meets these challenges: "Our solutions tackle regulatory compliance head-on. By strategically deploying compute nodes across multiple regions, we ensure compliance with local data sovereignty laws, such as GDPR in Europe, enabling seamless entry into regulated markets."

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"Our decentralized edge approach simplifies adherence to regulatory requirements by keeping data localized and compliant with regional or industry-specific standards, all while maintaining the flexibility to adapt to changing needs."

A Use Case - Supporting Governments, Academia, and Research

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Governments, academic institutions, and researchers stand to benefit significantly from our Private AI solutions. For authorities, our decentralized, edge-based infrastructure ensures sovereignty over sensitive data and critical operations, aligning with national security and regulatory compliance needs.

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Academic institutions gain equitable access to cutting-edge Graphics Processing Units (GPUs), fostering innovation and reducing costs for resource-intensive research projects. Researchers benefit from localized, low-latency infrastructure that supports collaboration and accelerates breakthroughs across disciplines.

Our Vision for Private AI

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As technology evolves, so do the challenges and opportunities in the AI landscape. At Redsand Ventures, we are committed to enabling Private AI through a decentralized, edge-focused approach that assures capacity dynamically while meeting the most stringent security and compliance requirements.

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We provide a full AI Compute as a Service which means no capex outlay, making us a powerful agent to your AI Compute journey.

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We invite you to connect with our team to explore how our solutions can support your organization in securely harnessing the power of AI while staying ahead of the curve.

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Get in touch with our team to find out more.

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