#runtime-enforcement News & Analysis

4 articles tagged with #runtime-enforcement. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBullisharXiv – CS AI · Jun 27/10

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Ethical Hyper-Velocity (EHV): A Hardware-Rooted Zero-Trust Runtime Enforcement Architecture for Agentic AI Systems

Researchers introduce Ethical Hyper-Velocity (EHV), a hardware-enforced governance architecture that embeds real-time policy constraints directly into AI inference pipelines using trusted execution environments and formal verification. The system reduces policy enforcement latency from days to near-instant, addressing critical safety gaps in autonomous agentic systems operating in regulated industries like healthcare and finance.

AIBullisharXiv – CS AI · Feb 277/105

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Agent Behavioral Contracts: Formal Specification and Runtime Enforcement for Reliable Autonomous AI Agents

Researchers introduce Agent Behavioral Contracts (ABC), a formal framework for specifying and enforcing reliable behavior in autonomous AI agents. The system addresses critical issues of drift and governance failures in AI deployments by implementing runtime-enforceable contracts that achieve 88-100% compliance rates and significantly improve violation detection.

AINeutralarXiv – CS AI · Jun 196/10

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Sovereign Execution Brokers: Enforcing Certificate-Bound Authority in Agentic Control Planes

Researchers introduce the Sovereign Execution Broker (SEB), a runtime enforcement layer that separates authorization, certification, and execution in autonomous agent systems. SEB ensures that production mutations can only occur through certificate-bound channels, preventing unauthorized actions by non-deterministic AI reasoning processes accessing cloud and deployment infrastructure.

AINeutralarXiv – CS AI · Jun 116/10

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Runtime Enforcement of Hybrid System Properties

Researchers propose a runtime enforcement framework using Hybrid Automata to actively prevent safety violations in autonomous and cyber-physical systems by monitoring and modifying unsafe behaviors in real time. The approach combines discrete-event editing with continuous monitoring and is validated through an Adaptive Cruise Control case study, demonstrating effective safety compliance with minimal computational overhead.