Terminology Index

Glossary

2953 terms

DNS over HTTPS (DoH)

A protocol that sends DNS queries encrypted inside HTTPS, hiding them from network observers. It improves user privacy but complicates defensive monitoring, since DNS lookups no longer appear in plain network traffic.

Traditionally DNS queries travel in plaintext, visible to anyone on the network. DoH encrypts them within HTTPS so they blend with ordinary web traffic, protecting privacy and resisting tampering. For defenders this is double-edged: it stops eavesdropping and manipulation, but it also blinds DNS-based monitoring and can let malware hide command-and-control or exfiltration inside encrypted DNS, so organizations must adapt their visibility strategy.

Introduced in: Networking Foundations

Examples

A browser sending DNS queries encrypted over HTTPS instead of plaintext.
Defenders losing visibility into DNS lookups that now ride inside HTTPS.
Malware using DoH to hide command-and-control resolution.

DNS Resolutionrelates_to DNS Logsrelates_to DNS Tunnelingrelates_to DNSrelates_to MX Recordrelates_to HTTPSprerequisite_of Disk Encryptionrelates_to DNS Cacherelates_to

DNS-Based Detection

Detection techniques that use DNS query data to find threats, spotting lookups of malicious, newly registered, or algorithmically generated domains, beaconing patterns, and DNS tunneling, often written as SIEM queries over DNS logs.

DPIA Mitigation Tracking

DPIA mitigation tracking assigns each privacy-risk measure an owner, deadline, status, expected effect, test evidence, and residual-risk decision.

DPIA Process

The step-by-step method for conducting a Data Protection Impact Assessment under the GDPR, systematically describing a processing activity, assessing its privacy risks, and identifying measures to reduce them before the processing begins.

DPIA Requirements

The conditions under the GDPR that determine when a Data Protection Impact Assessment is mandatory, namely when a processing activity is likely to result in a high risk to individuals' rights and freedoms.

DR Plan Components

The technical and operational elements of a disaster-recovery plan, including scope, roles, dependencies, restoration sequence, credentials, validation, communications, and failback.

DSAR Operations

The operational work of handling Data Subject Access Requests, the process and tooling to receive, verify, fulfill, and respond to individuals exercising their privacy rights within legal deadlines.

Data Access Alert

An alert about unusual or prohibited reading, querying, copying, exporting, modifying, or deleting of data, requiring resource- and operation-level validation.

Data Access Alerts

SOC alerts triggered when data is accessed in unusual or sensitive ways, such as bulk downloads, access to restricted records, or an account reaching data it never normally touches, which can indicate theft or insider misuse.

Data Classification

Labeling data based on sensitivity, value, or handling requirements.

Data Classification (Cloud)

Categorizing cloud-stored data by sensitivity, such as public, internal, confidential, or regulated, so the right protections, encryption, access control, and DLP, can be applied proportionately to how sensitive each dataset is.

Data Collection

The attacker activity of gathering targeted data within a compromised environment in preparation for theft, identifying, accessing, and staging files, records, or communications before exfiltrating them.

Data Component Mapping

Data Component mapping connects an ATT&CK information need to the organization's actual sensors, native events, normalized fields, collection policies, and covered platforms.

Data Exfiltration

Unauthorized transfer of data from a system, account, tenant, device, or controlled environment to a destination or party outside the approved boundary.

Data Exfiltration Response

The response to an incident where data is being or has been stolen, focused on stopping the outflow, scoping exactly what left, preserving evidence, and meeting notification obligations triggered by the loss of data.

Data Flow Analysis

Data flow analysis traces information through sources, processes, stores, recipients, trust boundaries, transformations, backups, and deletion paths.

Data Flow Inventory

A data flow inventory records personal-data sources, destinations, transformations, interfaces, recipients, jurisdictions, security boundaries, and deletion paths.

Data Flow Modeling

Data flow modeling records external entities, processes, stores, flows, protocols, direction, data classes, identity context, and protections to show how information moves and changes.

Data Flow Validation

Data flow validation traces representative information from source to destination and confirms actual protocols, fields, transformations, storage, logs, queues, errors, retention, and third parties.

Data Lakes

Large, scalable repositories that store vast amounts of raw log and telemetry data in one place for security analytics, letting defenders retain and query more data over longer periods than a traditional SIEM alone.

Data Loss Prevention

Controls that try to detect or prevent sensitive data from leaving approved places.

Data Map Change Control

Data map change control requires product, system, vendor, field, retention, migration, and retirement changes to update privacy maps and records before closure.

Data Mapping Foundation

The basic concept and purpose of data mapping in privacy practice: documenting what personal data an organization holds, where it comes from, where it flows, and where it is stored, as the groundwork for all privacy compliance.

Data Mapping Process

The step-by-step method for building and maintaining a data map, discovering personal data, documenting its sources, flows, purposes, and storage, validating with stakeholders, and keeping it current as systems change.

Data Mapping at Scale

Performing data mapping across large, complex organizations with many systems and data flows, using automation, discovery tooling, and governance to keep the map accurate when manual methods alone cannot keep up.

Data Minimization

Collecting and keeping only the data needed for a clear purpose.

Data Protection

The set of controls and practices that keep data safe across its lifecycle and all three states, at rest, in transit, and in use, through encryption, access control, classification, and loss prevention, preserving its confidentiality and integrity.

Data Protection Officer

A designated role, required by laws like the GDPR for certain organizations, responsible for overseeing data-protection strategy and compliance, advising on privacy obligations, and serving as a contact point for regulators and data subjects.

Data Purpose and Minimization

Data purpose and minimization link processing to an authorized need and limit collected data, fields, precision, access, copies, use, and retention accordingly.

Data Residency

The requirement or practice of keeping data physically located in a specific country or region, often to satisfy legal, regulatory, or contractual obligations. In the cloud, it is controlled mainly through region selection.

Data Residency and Sovereignty

Data residency concerns where data is stored or processed, while data sovereignty concerns the legal authority and rules governing that data and its custodians.

Data Sovereignty

The principle that data is subject to the laws of the country in which it is located, meaning where data resides determines which government's legal authority and access rights apply to it. It is a key driver of cloud region and provider choices.