Business and Data Understanding AI Mitigations
ML Lifecycle StageBusiness and Data Understanding groups 12 AI defenses across the ML lifecycle.
Overview
A group of defenses with the same label.
Lifecycle stage
How ATLAS labels this defense group.
- ML lifecycle stage
- Business and Data Understanding
- Mitigation count
- 12
Related defenses
Defenses included in this group.
AI Bill of Materials
An AI Bill of Materials (AI BOM) contains a full listing of artifacts and resources that were used in building the AI. The AI BOM can help mitigate supply chain risks and enable rapid response to reported vulnerabilities.
This can include maintaining dataset provenance, i.e. a detailed history of datasets used for AI applications. The history can include information about the dataset source as well as well as a complete record of any modifications.
Control Access to AI Models and Data at Rest
Establish access controls on internal model registries and limit internal access to production models. Limit access to training data only to approved users.
Input and Output Validation for AI Agent Components
Implement validation on inputs and outputs for the tools and data sources used by AI agents. Validation includes enforcing a common data format, schema validation, checks for sensitive or prohibited information leakage, and data sanitization to remove potential injections or unsafe code. Input and output validation can help prevent compromises from spreading in AI-enabled systems and can help secure the workflow when multiple components are chained together. Validation should be performed external to the AI agent.
Limit Model Artifact Release
Limit public release of technical project details including data, algorithms, model architectures, and model checkpoints that are used in production, or that are representative of those used in production.
Showing 4 of 10
Source evidence
Original public records and references for this page.
Original source
Original source links
Open the public records and source datasets used for this page.
