Machine Learning
What is Machine Learning?
A branch of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. Machine learning algorithms build models from training data to make predictions or decisions.
Terms that appear alongside machine learning
Each of these is named in at least one of the same controls as machine learning. The number is how many controls name both.
- governance 8 shared controls
- cybersecurity 7 shared controls
- compliance 5 shared controls
- ransomware 5 shared controls
- transparency 5 shared controls
- resilience 5 shared controls
- generative ai 4 shared controls
- policy 4 shared controls
Frameworks that govern machine learning
What the standards actually require on machine learning
Requirements naming machine learning across 6 standards, quoted from the control text.
Machine learning sub-systems embedded in wider IT systems follow a risk-based life cycle framework aligned with the GAMP phases, with data management starting in the concept phase because data is the key resource, performance metrics acting as the technical sp...
gamp-5-good-automated-manufacturing-practice::D11 · Appendix D11 Artificial Intelligence and Machine Learning →AI-related risk source: Learning from data brings risks of its own: data quality and bias, overfitting, concept drift, behaviour that changes with retraining, and outputs that cannot be fully specified in advance.
iso-iec-23894-2023::B.5 · Machine learning →Where artificial intelligence or machine learning is used for fraud and misconduct detection, cybersecurity, automated trading, loan origination and credit scoring, risk management, security controls or compliance, management addresses the risks: reliance on l...
ffiec-it-examination-handbook::AIO-VII.D · VII.D Artificial Intelligence and Machine Learning (Architecture, Infrastructure, and Operations) →Automated functionality identifies processes that move data (ETL, ELT, intentional duplication, data delivery, streaming) and records lineage metadata for them;
edm-council-cdmc-cloud-data-management-capability-framework::6.2.1 · 6.2.1 Multi-environment lineage discovery is automated →For planning algorithms the analysis looks at where the vehicle is and its surroundings, the road, built-up, countryside or motorway settings, how the user or driver behaves (misuse included), what other road users may do, situations such as roadworks, crashes...
iso-21448-2022::7.3.2 · 7.3.2 Insufficiencies and triggering conditions related to planning algorithms →To keep the AV effective, extra sensor coverage ought to be considered so that losing or degrading one sensor does not force the vehicle to stop; sensor fusion such as Dempster-Shafer methods or machine learning may help, with Annex C on AI.
bsi-pas-1880-2020::7.13 · 7.13 Sensor fault tolerance →Questions people ask about machine learning
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