All of the frontier AI companies have published safety frameworks where they define capability thresholds and risk mitigations that determine how they will safely develop and deploy their models. Adoption of systematic approaches to risk modelling, based on established practices used in safety-critical industries, has been recommended, however frontier AI companies currently do not describe in detail any structured approach to identifying and analysing hazards. STPA (Systems-Theoretic Process Analysis) is a systematic methodology for identifying how complex systems can become unsafe, leading to hazards. It achieves this by mapping out controllers and controlled processes then analysing their interactions and feedback loops to understand how harmful outcomes could occur (Leveson & Thomas, 2018). Weevaluate STPA’s ability to broaden the scope, improve traceability and strengthen the robustness of safety assurance for frontier AI systems. Applying STPA to the threat model and scenario described in ’A Sketch of an AI Control Safety Case’ (Korbak et al., 2025), we derive a list of Unsafe Control Actions. From these we select a subset and explore the Loss Scenarios that lead to them if left unmitigated. We find that STPA is able to identify causal factors that may be missed by unstructured hazard analysis methodologies thereby improving robustness. We suggest STPA could increase the safety assurance of frontier AI when used to complement or check coverage of existing AI governance techniques including capability thresholds, model evaluations and emergency procedures. The application of a systematic methodology supports scalability by increasing the proportion of the analysis that could be conducted by LLMs, reducing the burden on human domain experts.

Powerful new frontier AI technologies are bringing many benefits to society but at the same time bring new risks. AI developers and regulators are therefore seeking ways to assure the safety of such systems, and one promising method under consideration is the use of safety cases. A safety case presents a structured argument in support of a top-level claim about a safety property of the system. Such top-level claims are often presented as a binary statement, for example “Deploying the AI system does not pose unacceptable risk”. However, in practice, it is often not possible to make such statements unequivocally. This raises the question of what level of confidence should be associated with a top-level claim. We adopt the Assurance 2.0 safety assurance methodology, and we ground our work by specific application of this methodology to a frontier AI inability argument that addresses the harm of cyber misuse. We find that numerical quantification of confidence is challenging, though the processes associated with generating such estimates can lead to improvements in the safety case. We introduce a method for better enabling reproducibility and transparency in probabilistic assessment of confidence in argument leaf nodes through a purely LLM-implemented Delphi method.  We propose a method by which AI developers can prioritise, and thereby make their investigation of argument defeaters more efficient.  Proposals are also made on how best to communicate confidence information to executive decision-makers.  

A number of AI incident databases exist, but their data is inconsistently structured. I’ve developed a tool to structure AI incident data and presented the results with a dashboard, to help policymakers make evidence-based decisions. The tool classifies real-world, reported incidents by risk category and harm caused, aiming to reveal information about the impacts of AI on society that is currently opaque.

Using an LLM, the tool processes raw incident reports, providing a scalable methodology that could be applied cost-effectively across much larger datasets as numbers of reported incidents grow.

I classified all incidents in the AI Incident Database according to the MIT Risk Repository’s causal and domain taxonomies. I then assigned scores for harm-severity on 10 different dimensions based on the CSET AI Harm Taxonomy, using a scale I developed to reflect impact from zero to 'worst-case catastrophe'. 

The outputs are a structured dataset and a dashboard that you can explore through a graphical web interface to identify trends and insights.

This article identifies and addresses some shortcomings and potential objections to the original Root Cause Analysis method including reliability and epistemology of responses.
It then describes a cluster analysis of 50 incident reports aimed at identifying correlations between clusters of causes and different types of harm.