BWRCI Launches Open-Source Quantum-Secured AI Fail-Safe Protocol on GitHub

The Better World Regulatory Coalition Inc. (BWRCI) has taken a significant step towards ensuring the safety of advanced AI systems with the launch of the Quantum-Secured AI Fail-Safe Protocol (QSAFP) open-source repository on GitHub. This initiative represents a critical advancement in the global effort to establish provable AI safety mechanisms, leveraging quantum boundaries and controlled runtimes to prevent irreversible misuse.

QSAFP introduces a fail-safe operating framework for AI systems, incorporating runtime expiration, command authorization limits, and quantum-sealed checkpoints. These features are designed to maintain human oversight over AI, regardless of its power or complexity. Max Davis, founder of BWRCI and principal co-author of QSAFP, likened the protocol to a seatbelt for artificial general intelligence (AGI), emphasizing the need for foundational infrastructure that safeguards humanity without hindering innovation.

The open-core launch on GitHub invites developers, researchers, and security architects to contribute to the public layers of QSAFP. The repository includes protocol specifications for quantum-boundary enforcement, SDK tools for integration testing, and API documentation. It also outlines the scope of contributions, distinguishing between open and restricted areas, particularly in high-security domains such as tamper detection and quantum key generation.

BWRCI is extending an invitation to founding implementation partners to collaborate on the next phase of QSAFP. This includes work on commercial SDK integrations, runtime oversight for frontier models, and the development of quantum-secured fail-safe chipsets. The organization has already engaged with Meta’s AI leadership and submitted a proposal to DARPA, demonstrating its commitment to aligning with both industry leaders and national security entities.

QSAFP, born from years of cross-domain research and development, is positioned as a potential global standard for AI safety. Its design is lightweight, provable, and compatible with both open and closed systems, aiming to function alongside existing national security and AI governance frameworks. The protocol’s launch on GitHub marks a pivotal moment in the quest to secure AI before it becomes an unmanageable force.

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