Data access governance (DAG) solutions manage user, application, and machine data access privileges. They employ the principle of least privilege to ensure only the right identities have access to the organization's sensitive data, enabling innovation and growth while maintaining a strong security posture. DAG tools also continuously monitor who and...
A data asset is a broader term that encompasses any object or set of objects that contain data. It could refer to a data store (such as Amazon S3 bucket), a data object within that store (such as Apache Parquet file), or a data record (such as a single row...
A data breach means that an unauthorized person has successfully infiltrated an organization’s data stores and viewed, taken or shared data. Contributing factors that may lead to a breach include shadow data, misconfigurations, cyber attacks, social engineering, human error, or physical theft of devices containing data. If a data breach...
Data breach prevention is a set of best practices for keeping sensitive data safe from unauthorized personnel. A few data breach prevention tactics include: discovering and classifying your sensitive data, automating data policy management, following the principle of least privilege access, continuously monitoring your environment for active breaches, and aligning...
A structured inventory of a company's data assets, typically across various clouds and technologies, which helps in identifying, classifying, and organizing sensitive data like PII, PHI, and PCI transaction data, thereby aiding in data security and governance through enhanced visibility and control over the data landscape.
Data classification is the practice of categorizing data based on specific characteristics such as its sensitivity, value, volume and criticality to an organization. By classifying their data assets, teams can better understand the owners and uses of data, define and enforce policies appropriate to the level of data sensitivity, and...
Data democratization is the process of enabling many users across a business to easily access and use data. Previously, only data experts could handle data, making it difficult for other departments to make data-driven decisions. With the rise of data democratization, various users — including non-experts — can use tools...
Data detection and response alerts organizations in real time when suspicious activity or data breaches occur, allowing security teams to respond rapidly and mitigate active threats. With DDR, businesses can swiftly contain any data security incidents and minimize potential damage.
A process that involves identifying and understanding where data resides within an organization's environment, including public clouds, data warehouses, SaaS applications, cloud file shares, and on-premise storage. Data discovery aims to achieve comprehensive visibility into all data an organization creates and utilizes, providing crucial information about the data's owner, access,...
A data governance framework establishes which people, processes, and technologies are responsible for managing and protecting data assets. It sets policies for ensuring data is usable and executing successful data security, as well defining the daily operations for meeting compliance standards.
A data leak occurs when sensitive data is accidentally or maliciously exposed to unauthorized parties. Misconfigurations, cyberattacks, insider threats, security vulnerabilities, and other factors can cause leaks. A comprehensive data security posture management (DSPM) solution can help prevent leaks.
DLP is a technology that monitors sensitive data as it gets used, moved, and stored across the organization. It prevents data leakage and alerts team members about potential data losses or breaches. DLP works well for on-premise environments but is not conducive to a fast-paced, ephemeral cloud environment.
Data management encompasses the practices of collecting, keeping, and using data securely, efficiently, and cost-effectively. It involves a range of tasks such as data governance, storage, data quality assurance, and data policy enforcement, ensuring that data is accessible, reliable, and handled in compliance with policies and regulations.
Data mapping enables teams to understand the location, source and destination of their data, including previously unknown shadow data, its format and type, and which transformations it undergoes. It is used both to ensure compliance with data protection regulations and standards, and governance standards that often require organizations to map...
Data masking is a security process that protects confidential information by hiding it behind modified, fake data. This technique is often used when sharing data with external parties or within different parts of an organization, ensuring that sensitive details remain inaccessible while the overall structure and utility of the data...
Data security is a discipline concerned with protecting digital assets such as customer data, employee data, and company secrets. It safeguards these assets from unauthorized actions and access. Today, organizations must think about securing the data within their cloud environments, as well as their traditional, on-premise environments.
Data security in the cloud, also referred to as cloud data security, protects the data stored and processed in cloud environments. The discipline is focused on empowering organizations to leverage that data to meet business goals while still protecting cloud data from exposure risks, breaches and compromises. To make this...
Data Security Posture Management (DSPM) is the set of processes, policies, and technologies used to protect sensitive data and ensure compliance in cloud environments at scale and with automation. This rapidly evolving security solution category grew out of the need to protect the “innovation attack surface” created by the unintentional...
DFIR is the process of investigating and responding to cybersecurity incidents. The process includes finding the event's root cause, gathering evidence, and determining the scope of the breach. Response strategies are also critical to stopping and containing a threat, minimizing its impact on the organization.
Data loss prevention (DLP) monitoring is the practice of scanning data continuously in search of potential risks. DLP solutions flag unauthorized or unusual activities involving data at rest, in motion, or in use. Today, many organizations use a data detection and response (DDR) solution to fulfill the role of DLP...