Responsible AIAI Bias and Fairness Assessment
AI bias and fairness assessment evaluates how data, labels, objectives, design choices, and deployment conditions can create uneven errors or harms across groups.
AI data engineeringAI Data Preparation
AI data preparation collects, cleans, labels, transforms, and partitions information for development and evaluation.
AI operationsAI Feedback Loops
AI feedback loops occur when a system's outputs influence the future data used to evaluate or retrain it.
AI incident responseAI System Incident Response
AI-system incident response adapts preparation, detection, analysis, containment, recovery, and learning to failures and attacks involving models, data, prompts, outputs, pipelines, and automated decisions.
AI auditAI-Assisted Audit Governance
AI-assisted audit governance establishes approved uses, boundaries, validation, confidentiality, accountability, and documentation when auditors use AI tools.
API securityAPI-Based Data Ingestion Security
API-based data ingestion security validates the source, identity, authorization, format, rate, and integrity of data accepted through programmatic interfaces.