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User vetting, access restrictions, encryption, and infrastructure security for deployed systems.
Also in Operations & Security
AI systems are expected to increase the volume and impact of cyberattacks in the next 2 years. They’re also expected to improve the capability available to cyber crime and state actors in 2025 and beyond. Open-weights models are likely to increase this threat because their safeguards can be cheaply removed, they can be finetuned to help cyberattackers, and they cannot be recalled. Given many powerful open-weights models have been released, it’s infeasible to ‘put the genie back in the bottle’ that would prevent the use of AI systems for cyberattacks.15 This means significant work is likely necessary to defend against the upcoming wave of cyberattacks caused by AI systems.
Reasoning
Mitigation identifies threat requiring defensive work but lacks specific mechanism, mechanism location, or implementation approach.
Compute goverance
Regulate companies in the highly concentrated AI chip supply chain, given AI chips are key inputs to developing frontier AI models.
3.1.1 Legislation & PolicyData input controls
Filter data used to train AI models, e.g. don’t train your model with instructions to launch cyberattacks.
1.1.1 Training DataLicensing
Require organisations or specific training runs to be licensed by a regulatory body, similar to licensing regimes in other high-risk industries.
3.1.4 Compliance RequirementsOn-chip governance mechanisms
Make alterations to AI hardware (primarily AI chips), that enable verifying or controlling the usage of this hardware.
1.2.4 Security InfrastructureSafety cases
Develop structured arguments demonstrating that an AI system is unlikely to cause catastrophic harm, to inform decisions about training and deployment.
2.2.4 Assurance DocumentationEvaluations (aka “evals”)
Give AI systems standardised tests to assess their capabilities, which can inform the risks they might pose.
2.2.2 Testing & EvaluationThe AI regulator’s toolbox: A list of concrete AI governance practices
Jones, Adam (2024)
This article explains concrete AI governance practices people are exploring as of August 2024. Prior summaries have mapped out high-level areas of work, but rarely dive into concrete practice details. This summary explores specific practices addressing risks from advanced AI systems. Practices are grouped into categories based on where in the AI lifecycle they best fit. The primary goal of this article is to help newcomers contribute to the field of AI governance by providing a comprehensive overview of available practices.
Other (outside lifecycle)
Outside the standard AI system lifecycle
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Manage
Prioritising, responding to, and mitigating AI risks
Other
