Glossary

The practice of implementing safeguards in AI systems to protect user data, prevent misuse, and comply with data privacy regulations.

Techniques and tools that automatically locate, identify, and classify datasets to improve data governance and streamline operations.

An international regulatory framework for banks to improve risk management and strengthen financial systems’ stability post-2008 crisis.

Ensuring adherence to the California Consumer Privacy Act, which grants consumers rights over their personal data and imposes obligations on businesses.

A system for automating software development stages, including Continuous Integration and Continuous Delivery, to improve efficiency and quality.

An enhancement of the CCPA, providing stricter rules for data handling, additional consumer rights, and the creation of a privacy protection agency.

The process of moving data, applications, and IT resources from on-premises infrastructure to cloud-based platforms for scalability and efficiency.

An extension of Continuous Integration where code changes are automatically prepared for deployment to production environments.

The practice of merging code changes into a shared repository frequently, with automated testing to catch errors early in the development cycle.

The process of collecting data from various sources, often involving extraction, transformation, and integration into usable formats.

The transformation of data to prevent identification of individuals, balancing privacy needs with data utility for analysis or sharing.

Discover the best data anonymization tools, how they protect privacy, ensure compliance, and enable safe data analysis. Learn which one is the best for you.

Methods to enhance dataset size and diversity by creating modified copies of existing data, often used to improve machine learning models.

The framework of policies, processes, and standards that ensure data quality, security, and proper usage across an organization.

A technique for concealing sensitive data by altering its values, ensuring privacy while maintaining usability for testing or training.

Data masking irreversibly obscures data for non-production use, while tokenization replaces sensitive data with reversible, secure tokens.

The practice of protecting personal data from unauthorized access or misuse, ensuring compliance with regulations and user trust.

Data redaction is like a sophisticated eraser that selectively removes sensitive information while keeping documents and files structurally intact. This security measure goes beyond simple deletion by carefully identifying and eliminating confidential elements.

Generating artificial data to mimic real-world scenarios for testing, analysis, or model training without using sensitive real data.

Extracting a smaller, representative portion of a dataset to improve efficiency and reduce complexity in analysis or testing tasks.

A data management technique that provides real-time access to data from multiple sources without requiring physical replication.

A technology that allows users to interact with multiple databases as a single entity without moving or replicating the underlying data.

De-identification involves removing or altering PII, while anonymization ensures that the data cannot be re-identified, even with external sources.

A collaborative approach that integrates development and operations teams to accelerate software delivery, improve quality, and enhance scalability.

A mathematical framework that adds random noise to data analyses to protect individual privacy while preserving overall dataset accuracy.

Temporary data that is created and used for short-term tasks, such as caching or intermediate processing, and deleted after use.

An encryption technique that secures data while maintaining its original format, allowing compatibility with existing systems and processes.

Adhering to the General Data Protection Regulation, which governs data protection and privacy for individuals within the European Union.

Adherence to U.S. healthcare privacy and security regulations, ensuring protection of patient information and data handling standards.

Large Language Models are AI systems trained on vast text datasets capable of understanding and generating human-like language outputs.

The strategic use of multiple cloud service providers to achieve flexibility, avoid vendor lock-in, and enhance reliability.

The process of identifying and categorizing personally identifiable information (PII) based on its sensitivity to ensure proper handling and protection.

An AI method that retrieves external knowledge to improve the quality and relevance of generated text or responses.

Data generated through artificial means that simulates the characteristics of real datasets, commonly used for AI training and testing.

The creation of artificial data that closely mimics real-world data, used in testing, training, or research without compromising privacy.

Structured data represented in rows and columns, commonly found in spreadsheets, relational databases, and analytical reports.

The process of creating realistic and relevant data for testing applications, ensuring coverage of different use cases and scenarios.

Organizing, securing, and provisioning data used for software testing to ensure it is accurate, reliable, and compliant with regulations.

The process of configuring, maintaining, and controlling environments for software testing to ensure consistency and reliability of tests.