{"data":{"id":"5eb7fd01-fe9a-4d76-b086-3064ebab36e4","title":"Toward a Secure Framework for Regulating Artificial Intelligence Systems","summary":"This paper addresses the lack of technical tools for regulating high-risk AI systems by proposing SFAIR (Secure Framework for AI Regulation), a system that automatically tests whether an AI meets regulatory standards. The framework uses a temporal self-replacement test (similar to certification exams for human operators) to measure an AI's operational qualification score, and protects itself using encryption, randomization, and real-time monitoring to prevent tampering.","solution":"The paper proposes SFAIR as a comprehensive framework for securing AI regulation. Key technical safeguards mentioned include: randomization, masking, encryption-based schemes, and real-time monitoring to secure SFAIR operations. Additionally, the framework leverages AMD's Secure Encrypted Virtualization-Encrypted State (SEV-ES, a processor-level security technology that encrypts AI system memory) for enhanced security. The source code of SFAIR is made publicly available.","labels":["policy","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11185308","publishedAt":"2025-10-01T13:19:21.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["integrity","safety"],"aiComponentTargeted":null,"llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}