DFIR (digital forensics and incident response)
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.
DLP monitoring
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 monitoring in a cloud-native environment.
Data Backup
The ability to restore data to a specific point in time. Data backups run at periodic intervals, creating “save points” of all the data on your production servers. These save points can be restored in the event of file corruption, system failure, outages, or any event that causes data loss.
Data Backup and Recovery
Without proper protection, data disaster events can be incredibly damaging to business. Data backup and recovery solutions can mean less trouble in the event of an operational failure, ransomware attack, or data corruption.
Data Management as a Service
Data management as a service (DMaaS) is the comprehensive approach to storing, organizing, securing, and maintaining all the data that an organization possesses using a vendor that operates on a pay-as-you-go model. Fees are based on the amout of data to be stored and additional needs for security, backup and recovery, data discovery, and analysis.
Data Replication
The process of making copies of business data, synchronizing it, and distributing it among multiple servers and data centers replicated in multiple databases. Data replication maintains consistent access to critical data and core business applications remotely in the event of an outage or emergency.
Data access governance
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 what is accessing data, highlighting unauthorized access or suspicious behavior.
Data access governance
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 what is accessing data, highlighting unauthorized access or suspicious behavior.
Data asset
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 in a MySQL table).
Data breach
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 involves sensitive, confidential or regulated data, an organization may face adverse business impacts including increased regulatory scrutiny, financial losses, and loss of customer trust. There is also a possibility that the threat actor will use stolen data for fraudulent activities in the future.
Data breach prevention
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 data practices with regulations and standards.
Data catalog
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 & 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
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 reduce risks such as unauthorized access, data loss, or breaches.
Data democratization
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 and resources to analyze, interpret, and leverage data. Widely-available data enables business innovation, especially as it migrates to the cloud, which also increases data security risk.
Data detection and response (DDR)
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.
Data discovery
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, usage, type, and sensitivity. & 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, usage, type, and sensitivity.
Data governance framework
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.
Data leak
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.
Data loss prevention/Data leak prevention (DLP)
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
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 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
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 because it provides visibility into potential risk.
Data masking
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 are preserved for legitimate use. & 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 are preserved for legitimate use.
Data privacy compliance
Data Privacy Compliance entails adhering to regulations and standards that govern the collection, processing, and protection of personal information. It involves implementing policies, procedures, and technologies to ensure that organizations handle data in accordance with legal requirements. Achieving and maintaining data privacy compliance builds trust with stakeholders and safeguards individuals' rights to control their personal information.& Data Privacy Compliance entails adhering to regulations and standards that govern the collection, processing, and protection of personal information. It involves implementing policies, procedures, and technologies to ensure that organizations handle data in accordance with legal requirements. Achieving and maintaining data privacy compliance builds trust with stakeholders and safeguards individuals' rights to control their personal information.
Data security
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 (cloud data security)
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 approach work, it’s imperative for security teams to understand where the sensitive data is and who has access to it, the overall security posture of that data, and how it is being accessed on an ongoing basis. Learn more about data security in the cloud.
Data security posture management (DSPM)
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 risk cloud data users, such as developers and data scientists, create when using data to drive innovation. It provides organizations with a practical approach to securing cloud data by discovery of structured and unstructured data, analyzing access, usage patterns, and security posture, and providing actionable, guided remediation for data security risk. Learn more about data security posture management (DSPM).
Data tokenization
Data tokenization is a security technique that involves replacing sensitive data with unique tokens. These tokens are generated through an algorithm and hold no intrinsic value, making it difficult for unauthorized users to decipher the original information. This process enhances data security, especially in payment transactions and sensitive information storage, as the tokens can be securely processed without revealing the underlying sensitive data. Data tokenization plays a crucial role in protecting information from potential breaches and unauthorized access, contributing to overall data privacy and security measures.
Database management
Database management involves the systematic organization, storage, and retrieval of data within a structured database system. It includes tasks such as data modeling, database design, implementation, and optimization for efficient data storage and retrieval. Database management systems (DBMS) play a crucial role in controlling and managing access to the data, ensuring data integrity, and supporting various data-related operations within an organization. Effective database management is crucial for businesses to efficiently handle and leverage their data assets.
Disaster Recovery
Disaster recovery is a data backup and system restoration plan that can help a business survive an unexpected event, such as a cyber attack, natural disaster, or data loss due to human error.
Disaster Recovery as a Service (DRaaS)
Data Recovery as a Service (DRaaS) solutions enable the replication of backup data to a third-party service provider or public cloud infrastructure while providing the orchestration and resources necessary for rapid recovery in the event of a disaster.
Distributed File System
A distributed file system “distributes” a companies data across multiple servers using replicated and partitioned file systems to break data into smaller blocks and keep multiple copies of the data in multiple locations.