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Cryptographic protections, access controls, and hardware security.
Also in Non-Model
The developers of AI models can invest in cybersecurity to prevent compute resources, training source code, model weights, and other critical resources from being accessed and copied by unauthorized third parties (e.g., through insider threats or supply chain attacks). Access to model source code and weights can be restricted through an access control scheme, such as role-based access control. If access to model outputs by third parties is required, it can be provided through an API. Air gaps can block unauthorized remote access. In the case of necessary interaction with an external network, network bandwidth limitations can also be enforced to increase the detection window of potential breaches [108].
Reasoning
Cryptographic and access control protections safeguard proprietary model weights from unauthorized extraction.
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.
Build and Use Model
Training, fine-tuning, and integrating the AI model
Developer
Entity that creates, trains, or modifies the AI system
Manage
Prioritising, responding to, and mitigating AI risks
Other
