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Companies like OpenAI, Google DeepMind, and Anthropic have the stated goal ofbuilding artificial general intelligence (AGI) - AI systems that perform aswell as or better than humans on a wide variety of cognitive tasks. However,there are increasing concerns that AGI would pose catastrophic risks. In lightof this, AGI companies need to drastically improve their risk managementpractices. To support such efforts, this paper reviews popular risk assessmenttechniques from other safety-critical industries and suggests ways in which AGIcompanies could use them to assess catastrophic risks from AI. The paperdiscusses three risk identification techniques (scenario analysis, fishbonemethod, and risk typologies and taxonomies), five risk analysis techniques(causal mapping, Delphi technique, cross-impact analysis, bow tie analysis, andsystem-theoretic process analysis), and two risk evaluation techniques(checklists and risk matrices). For each of them, the paper explains how theywork, suggests ways in which AGI companies could use them, discusses theirbenefits and limitations, and makes recommendations. Finally, the paperdiscusses when to conduct risk assessments, when to use which technique, andhow to use any of them. The reviewed techniques will be obvious to riskmanagement professionals in other industries. And they will not be sufficientto assess catastrophic risks from AI. However, AGI companies should not skipthe straightforward step of reviewing best practices from other industries.

Risk assessment at AGI companies: A review of popular risk assessment techniques from other safety-critical industries