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Cryptographic protections, access controls, and hardware security.
Also in Non-Model
Hardware-enforced bandwidth limitations on data center network connections can protect AI model weights from unauthorized access or exfiltration, by limiting the speed of model weight access on the connections between data centers and the outside world. Such limitations can be put in place in multiple ways, for example by only constructing connections with a specific bandwidth. The output rate on all data channels can be set low enough that copying the weights is possible in principle (e.g., to enable regular backups), but would take so long that an unauthorized exfiltration of the weights could be detected and prevented. Such rate-limiting is only effective if it applies to all output connections for all storage locations on which the weights of the model are stored [139].
Reasoning
Hardware network isolation restricts data center connectivity, containing model execution environment.
Cybersecurity
Model development
2.4 Engineering & DevelopmentModel development > Data-related
1.1 ModelModel evaluations
2.2.2 Testing & EvaluationModel evaluations > General evaluations
2.2.2 Testing & EvaluationModel evaluations > Benchmarking
3.2.1 Benchmarks & EvaluationModel evaluations > Red teaming
2.2.2 Testing & EvaluationRisk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems
Gipiškis, Rokas; San Joaquin, Ayrton; Chin, Ze Shen; Regenfuß, Adrian; Gil, Ariel; Holtman, Koen (2024)
Organizations and governments that develop, deploy, use, and govern AI must coordinate on effective risk mitigation. However, the landscape of AI risk mitigation frameworks is fragmented, uses inconsistent terminology, and has gaps in coverage. This paper introduces a preliminary AI Risk Mitigation Taxonomy to organize AI risk mitigations and provide a common frame of reference. The Taxonomy was developed through a rapid evidence scan of 13 AI risk mitigation frameworks published between 2023-2025, which were extracted into a living database of 831 distinct AI risk mitigations.
Other (stage not listed)
Applies to a lifecycle stage not captured by the standard categories
Infrastructure Provider
Entity providing compute, platforms, or tooling for AI systems
Manage
Prioritising, responding to, and mitigating AI risks
