OpenAI’s Post

Security is sometimes forgotten as the focus goes towards the main goal a product was conceived for. But this article from OpenAI reminds all of us that in these times, and even more for state-of-the-art technology, security plays a crucial role in a world that every second is more digital. P.S.: The thing that amazed me more is the weights' encryption even when arriving at the GPU, and then decrypted. That is serious data transport security 😉

Super interesting topic! If you want to unlock the powerful of Human-AI interaction, follow us! Visit our website https://www.superhum.ai

Security vs sovereign data: that is a question to ask ourselves while using ai services. All sovereignity levels have various levels of security. All have pros and cons as long as you understand them. https://www.altkimya.com/2024/02/21/navigating-the-future-unveiling-the-power-of-sovereign-ai-and-data-management/

OpenAI's proposed security evolution for advanced AI infrastructure is not just necessary, but a strategic imperative. We admire their focus on trusted computing for AI accelerators and innovations in data center security. These measures are vital for protecting AI systems from advanced threats while maintaining their essential availability for innovation. Security is indeed a team sport, and OpenAI’s commitment to collaboration and transparency sets a strong example. We eagerly anticipate the development of these security standards and their global impact.

Thanks for being in the forefront navigating such an important complex challenge yet vital for achieving AGI! In my opinion, setting up AI infrastructure is a lot like running a top-notch amusement park where safety meets cutting-edge fun. Use trusted computing for AI accelerators and ensure tight network isolation to keep things running smoothly and securely. Stick to rigorous audit and compliance standards and always aim to be resilient, ready for anything! What do you think of managing AI models, especially when comparing open source to non-open source approaches? Could open collaboration impact the security and innovation of AI tech differently than more closed, proprietary models?

OpenAI The expensive electricity bills 💰🤑💸 may turn down AI. AI is based on human-made math models. Is it safe to buy? A practical list of high-risk AI applications has been identified by European Parliament's AI Act. Yes, AI can do many things, but NOT everything; at least, NOT what we are doing in data analytics. Let's try a go/no-go test to see if AI works. Is there any today's AI data solution you know, that can interpret and answer the following Chinese-English multilingual questions? With our intellectual property (IP), a copyrighted multilingual metadata, we can provide real time answers, by census geographical locations, as evidence for decision/policy making. "Who, in the Ontario province of Canada, has new US patents granted on the nearest Tuesday (Eastern Time), when the USPTO releases the newly granted US patents on a weekly basis?" "Who, in the "江蘇‘’ province of China, has new US patents granted on the nearest Tuesday (Eastern Time), when the USPTO releases the newly granted US patents on a weekly basis?" Metadata is an enabler. Without metadata, NO data can be found/retrieved, even by the most advanced technologies, like AI, NVIDIA chips, supercomputers, etc. https://lnkd.in/g-aJFnX

To complement today's security controls effectively, it's crucial to incorporate a layered security strategy that not only fortifies the perimeter but also ensures robust internal safeguards. Among the six measures, emphasizing real-time threat detection and automated response mechanisms can significantly elevate AI security. Integrating these systems with advanced encryption methods and biometric verification ensures that access is tightly controlled while maintaining a transparent audit trail enhances accountability. Additionally, adopting a zero-trust architecture will ensure that no entity within the network is trusted by default, thus minimizing the potential for insider threats. It's also vital to engage in regular security audits and update protocols in accordance with emerging threats to keep the security measures ahead of potential attackers.

How Blockchain and Decentralization Could Help: Decentralized Model Distribution: Blockchains offer immutable and transparent storage for AI models. A decentralized network can manage model versions and access, preventing a single point of failure and potentially limiting unauthorized distribution. Secure Data Exchange: Blockchain-based ledgers can facilitate the secure exchange of inference data, ensuring that only authorized parties with the right cryptographic keys can access it. Zero-Trust Verification: Blockchain-based smart contracts and decentralized identity mechanisms can enforce multi-party authorization and continuously verify the integrity of hardware involved in computation. Implementation: Collaborative Model Marketplaces: Establish decentralized AI model marketplaces on a blockchain, enabling secure model sharing, version control, and usage tracking. Access controls based on smart contracts would manage authorized model use and payments. Federated Learning + Blockchain: Combine privacy-preserving techniques like Federated Learning (where models are trained on decentralized data) with blockchain technology to coordinate the process. Blockchain ensures the integrity of models and data updates among participants.

The protection of private and copyrighted data is a major problem right now. The difficulty is to strike a compromise between protecting sensitive information's and allowing for the deployment of such models. The privacy concern is twofold: 1. For AI users, this entails protecting data against unauthorised access and exploitation. 2. Developers that train generative models must discover a mechanism to adequately compensate copyright holders, as the traditional legal basis of fair usage is no longer viable.

An insightful article shedding light on a critical yet often overlooked topic. Indeed, securing advanced AI infrastructure is paramount in today's ever-evolving threat landscape. What's particularly concerning is not just safeguarding our own use of AI, but also the proliferation of AI offerings from vendors who have amassed years' worth of our data. It's essential that they're prioritizing security measures, as highlighted in this piece, until security frameworks catch up. Let's stay vigilant and proactive in ensuring the integrity and security of AI technologies.