auditing

This paper introduces TSFMAudit, the first method for auditing pretraining data contamination in time series foundation models, using probe adaptation dynamics to detect unusually efficient fine-tuning that indicates prior exposure.

This paper proposes techniques that combine formal methods (Linear Temporal Logic) with LLMs for auditing, monitoring, and intervening in AI systems to ensure compliance with behavioral constraints, showing that even small-model labelers can match frontier LLM judges in detecting violations.

The article discusses the need for runtime governance in AI agents to balance autonomy with compliance, introducing SAFi, an open-source framework that enforces policies in real-time and audits actions.

The author surveyed 20 agentic AI founders and found that 17 rely on temporary workarounds for agent access control due to a lack of verifiable authorization layers. This highlights a significant security and auditing gap in production AI agents handling sensitive data.