0 / 0
AI risk atlas

AI risk atlas

Explore this atlas to understand some of the risks of working with generative AI, foundation models, and machine learning models.

Background image for risks associated with input

Risks associated with input

Training and tuning phase

Fairness

Data bias
Amplified

Robustness

Data poisoning
Traditional

Value alignment

Data curation
Amplified

Data laws

Data transfer
Traditional
Data usage
Traditional
Data aquisition
Traditional

Intellectual property

Data usage rights
Amplified

Transparency

Data transparency
Amplified
Data provenance
Amplified

Privacy

Reidentification
Traditional

Inference phase

Intellectual property

Robustness

Evasion attack
Amplified
Extraction attack
Amplified
Prompt leaking
Amplified

Multi-category

Prompt priming
Amplified
Jailbreaking
Amplified
Background image for risks associated with output

Risks associated with output

Fairness

Intellectual property

Harmful code generation

Generative AI search and answer
These answers are generated by a large language model in watsonx.ai based on content from the product documentation. Learn more