The $12$ risks identified in the NIST Generative AI Profile are:

CBRN Information: Lowered barriers to entry or eased access to materially nefarious information related to chemical, biological, radiological, or nuclear $($CBRN$)$ weapons, or other dangerous biological materials.

Confabulation: The production of confidently stated but erroneous or false content $($known colloquially as $$hallucinations$$ or $$fabrications$).$

Dangerous or Violent Recommendations: Eased production of and access to violent, inciting, radicalizing, or threatening content as well as recommendations to carry out self$-$harm or conduct criminal or otherwise illegal activities.

Data Privacy: Leakage and unauthorized disclosure or de$-$anonymization of biometric, health, location, personally identifiable, or other sensitive data.

Environmental: Impacts due to high resource utilization in training GAI models, and related outcomes that may result in damage to ecosystems.

Human$-$AI Configuration: Arrangement or interaction of humans and AI systems which can result in algorithmic aversion, automation bias or over$-$reliance, misalignment or mis$-$specification of goals and$/$or desired outcomes, deceptive or obfuscating behaviors by AI systems based on programming or anticipated human validation, anthropomorphization, or emotional entanglement between humans and GAI systems; or abuse, misuse, and unsafe repurposing by humans.

Information Integrity: Lowered barrier to entry to generate and support the exchange and consumption of content which may not be vetted, may not distinguish fact from opinion or acknowledge uncertainties, or could be leveraged for large$-$scale dis$-$ and mis$-$information campaigns.

Information Security: Lowered barriers for offensive cyber capabilities, including ease of security attacks, hacking, malware, phishing, and offensive cyber operations through accelerated automated discovery and exploitation of vulnerabilities; increased available attack surface for targeted cyber attacks, which may compromise the confidentiality and integrity of model weights, code, training data, and outputs.

Intellectual Property: Eased production of alleged copyrighted, trademarked, or licensed content used without authorization and$/$or in an infringing manner; eased exposure to trade secrets; or plagiarism or replication with related economic or ethical impacts.

Obscene, Degrading, and$/$or Abusive Content: Eased production of and access to obscene, degrading, and$/$or abusive imagery, including synthetic child sexual abuse material $($CSAM$),$ and nonconsensual intimate images $($NCII$)$ of adults.

Toxicity, Bias, and Homogenization: Difficulty controlling public exposure to toxic or hate speech, disparaging or stereotyping content; reduced performance for certain sub$-$groups or languages other than English due to non$-$representative inputs; undesired homogeneity in data inputs and outputs resulting in degraded quality of outputs.

Value Chain and Component Integration: Non$-$transparent or untraceable integration of upstream third$-$party components, including data that has been improperly obtained or not cleaned due to increased automation from GAI; improper supplier vetting across the AI lifecycle; or other issues that diminish transparency or accountability for downstream us$.$ Detailed Heatmap $($Example$)$:

Within each risk category, specific risks could be further mapped to each phase.

Here's an example for the "Technical Risks" category:

$|$ Technical Risk $|$R&D $|$ Deployment $|$ Operation $|$ Monitoring & Evaluation $|$ DO HEATMAP FOR $12$ RISKS FOR EACH RISK LIKE THIS AND DO GROUP BY OF RISKS FOR EACH CATEGORY $,$ WHAT DO YOU OBSERVE ALSO?