# Cryptographic Runtime Governance for Autonomous AI Systems: The Aegis Architecture for Verifiable Policy Enforcement

arXiv: https://arxiv.org/abs/2603.16938 · HTML: https://mlsanity.com/paper/2603.16938/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 15 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [RvLLM: LLM Runtime Verification with Domain Knowledge](https://mlsanity.com/paper/2505.18585/) (2025)
- [Beyond principlism: Practical strategies for ethical AI use in research practices](https://mlsanity.com/paper/2401.15284/) (2024)

## Built on by (most cited first, 5 of 5)

- [When the Agent Is the Adversary: Architectural Requirements for Agentic AI Containment After the April 2026 Frontier Model Escape](https://mlsanity.com/paper/2604.23425/) (2026)
- [Runtime Governance for Agentic AI: Action-Boundary Control with Trusted Provenance and Fail-Closed Execution](https://mlsanity.com/paper/2608.16891/) (2026)
- [Ethical Hyper-Velocity (EHV): A Hardware-Rooted Zero-Trust Runtime Enforcement Architecture for Agentic AI Systems](https://mlsanity.com/paper/2605.17909/) (2026)
- [KYA: A Framework-Agnostic Trust Layer for Autonomous Systems with Verifiable Provenance and Hierarchical Policy Composition](https://mlsanity.com/paper/2605.25376/) (2026)
- [The Acknowledgment Point Is the System: Durable Policy-Decision Receipts for AI Audit Evidence](https://mlsanity.com/paper/2608.17176/) (2026)

## Abstract (arXiv)

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 (IEPL) at system genesis and enforces external emissions through an Ethics Verification Agent (EVA), an Enforcement Kernel Module (EKM), and an Immutable Logging Kernel (ILK). Amendments to the governing policy layer require quorum approval and redeclaration of the system trust root; verified violations trigger autonomous shutdown and generation of auditable proof artifacts. We evaluate the architecture within the Civitas runtime using three operational measures: proof verification latency under tamper conditions, publication overhead, and alignment retention performance relative to an ungoverned baseline. In controlled trials, Aegis demonstrates median proof verification latency of 238 ms, median publication overhead of approximately 9.4 ms, and higher alignment retention than the baseline condition across matched tasks. We argue that these results support a shift in AI governance from discretionary oversight toward verifiable runtime constraint. Rather than claiming to resolve machine ethics in the abstract, the proposed architecture seeks to show that policy violating behavior can be rendered operationally non executable within a controlled runtime governance framework. The paper concludes by discussing methodological limits, evidentiary implications, and the role of proof oriented governance in high assurance AI deployment.