Constructing Safety Cases for AI Systems: A Reusable Template Framework

2026-01-30

AI Summary

Existing safety assurance approaches struggle to address the challenges posed by generative and agent-based AI systems, whose boundaries are often ambiguous, behaviors dynamic, and risks continuously evolving. This work proposes the first structured safety assurance framework tailored to such systems, introducing an integrated taxonomy of claim types, argument patterns, and evidence families. The framework supports composable assurance in scenarios involving truth-agnostic evaluation, dynamic updates, and threshold-based decision-making. By synthesizing multi-source evidence—including empirical, mechanistic, formal, expert-driven, and model-driven forms—and integrating causal, normative, and risk-oriented logics, it enables a trustworthy, auditable, and adaptive assurance mechanism that co-evolves with the system. This significantly enhances the systematicity and adaptability of safety governance for cutting-edge AI systems.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Multiagent Systems: Agent/AI Theories and Architectures
• Natural Language Processing: Safety and Robustness

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Data transparency and provenance

Abstract

Safety cases, structured arguments that a system is acceptably safe, are becoming central to the governance of AI systems. Yet, traditional safety-case practices from aviation or nuclear engineering rely on well-specified system boundaries, stable architectures, and known failure modes. Modern AI systems such as generative and agentic AI are the opposite. Their capabilities emerge unpredictably from low-level training objectives, their behaviour varies with prompts, and their risk profiles shift through fine-tuning, scaffolding, or deployment context. This study examines how safety cases are currently constructed for AI systems and why classical approaches fail to capture these dynamics. It then proposes a framework of reusable safety-case templates, each following a predefined structure of claims, arguments, and evidence tailored for AI systems. The framework introduces comprehensive taxonomies for AI-specific claim types (assertion-based, constrained-based, capability-based), argument types (demonstrative, comparative, causal/explanatory, risk-based, and normative), and evidence families (empirical, mechanistic, comparative, expert-driven, formal methods, operational/field data, and model-based). Each template is illustrated through end-to-end patterns addressing distinctive challenges such as evaluation without ground truth, dynamic model updates, and threshold-based risk decisions. The result is a systematic, composable, and reusable approach to constructing and maintaining safety cases that are credible, auditable, and adaptive to the evolving behaviour of generative and frontier AI systems.

Problem

• safety cases
• AI systems
• generative AI
• agentic AI
• dynamic risk

Innovation

• safety cases
• reusable templates
• AI governance
• structured argumentation
• dynamic AI systems