{
  "id": 7229,
  "url": "https://arxiv.org/abs/2603.16938v1",
  "title": "Cryptographic Runtime Governance for Autonomous AI Systems: The Aegis Architecture for Verifiable Policy Enforcement",
  "summary": "Contemporary AI governance frameworks rely heavily on post hoc oversight, policy guidance, and behavioral alignment techniques, yet these mechanisms become fragile as systems gain autonomy, speed, and operational opacity. This paper presents Aegis, a runtime governance architecture for autonomous AI systems that treats policy and legal constraints as execution conditions rather than advisory principles. Aegis binds each governed agent to a cryptographically sealed Immutable Ethics Policy Layer (",
  "authors": "Adam Massimo Mazzocchetti",
  "category": "research",
  "topics": "regulation,safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-15T04:04:57.000Z",
  "fetched_at": "2026-07-14T16:33:03.574Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7229",
  "original_url": "https://arxiv.org/abs/2603.16938v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}