According to a 2023 survey of Chief Information Security Officers (CISO) worldwide, 70 percent of sensitive data loss at organizations happens because of carless users, A further 48.1 percent of the respondents said Compromised systems caused data loss. Additionally, around 20 percent of respondents, malicious employee or contractor was the cause behind their incidents.

According to a 2024 survey of Chief Information Security Officers (CISO) worldwide, nearly eight in ten respondents in South Korea said their organization had encountered a loss of sensitive information in the past 12 months. Canada ranked second, as ** percent of the CISOs stated the same. Overall, around ** percent of organizations across the researched markets said they had dealt with material loss of sensitive information in the previous year.

Sensitive Data Discovery Market size was valued at USD 7.50 Billion in 2023 and is projected to reach USD 20.03 Billion by 2030, growing at a CAGR of 15.5% during the forecast period 2024-2030.

Global Sensitive Data Discovery Market Drivers

The market drivers for the Sensitive Data Discovery Market can be influenced by various factors. These may include:

Growing Concerns about Cybersecurity and Data Breaches: Organisations now realise how critical it is to secure sensitive data due to the increasing frequency and severity of data breaches. Solutions for sensitive data discovery become essential for locating and safeguarding sensitive data, which helps to solve cybersecurity issues.

Regulatory Compliance Requirements: Strict privacy and data protection laws are being enforced by governments and regulatory organisations across the globe. Organisations must put in place methods for locating and handling sensitive data in order to comply with regulations like the General Data Protection Regulation, the Health Insurance Portability and Accountability Act, and others.

Proliferation of Data Across Organisations: It is becoming more difficult to manually track and secure sensitive information due to the ever-growing volume of data collected and processed by organisations. Organisations can manage the massive volumes of data they handle with the aid of automated sensitive data discovery solutions.

Adoption of Cloud Services: Switching to cloud services creates new difficulties for data security maintenance. Ensuring that data stored in the cloud is appropriately recognised, classified, and safeguarded becomes dependent on sensitive data discovery.

Growing Awareness and Education: Organisations are placing a greater emphasis on training and education as they become more conscious of the possible risks connected to the exposure of sensitive data. Sensitive data discovery is growing because businesses are investing in technology that make it easier to find and safeguard sensitive data.

Integration with Security Ecosystem: Data Loss Prevention (DLP), Endpoint Protection, and Security Information and Event Management (SIEM) systems are just a few examples of the larger cybersecurity ecosystems into which sensitive data discovery solutions are frequently integrated. The adoption of these integrated solutions is motivated by the requirement for a thorough security approach.

Growing Insider Threats: Sensitive data is at serious danger from insider threats, whether deliberate or inadvertent. Organisations can identify and reduce the risks related to insider threats with the use of sensitive data discovery technologies.

Globalisation and Cross-Border Data Flow: Companies that conduct business internationally must abide by various data protection regulations in various countries. Sensitive data discovery ensures the safe transfer of data across borders by helping to manage compliance with various regulations.

Purpose and Features

🌍 World's largest open dataset for privacy masking 🌎 The dataset is useful to train and evaluate models to remove personally identifiable and sensitive information from text, especially in the context of AI assistants and LLMs. Key facts:

OpenPII-220k text entries have 27 PII classes (types of sensitive data), targeting 749 discussion subjects / use cases split across education, health, and psychology. FinPII contains an additional ~20 types tailored to… See the full description on the dataset page: https://huggingface.co/datasets/ai4privacy/pii-masking-300k.

ATTENTION: THIS DATASET DOES NOT HOST ANY SOURCE VIDEOS. WE PROVIDE ONLY HIDDEN FEATURES GENERATED BY PRE-TRAINED DEEP MODELS AS DATA

Environmentally Sensitive Areas as declared in Environmental Protection (Environmentally Sensitive Areas) Notice 2005, Government Gazette No. 55. This dataset is provided to assist landowners and managers in determining the location of environmentally sensitive areas under the Environmental Protection Act 1986. It is not a substitute for any requirement of the legislation. Those seeking further information should contact the data custodian. The ESA data was originally produced by Department of Water (D0W), completed at 30/05/2005. Department of Environment and Conservation (DEC) produced a new ESA data set to amend errors in the ESA data set. The custodian of the Environmentally Sensitive Areas dataset is now the Department of Water and Environmental Regulation. For further information contact Clearing Regulation Branch (08) 9333 7510. This dataset was formerly known as Clearing Regulations - Environmentally Sensitive Areas (DER-016)

Sensitive Regulated Data: Permitted and Restricted UsesPurposeScope and AuthorityStandardViolation of the Standard - Misuse of InformationDefinitionsReferencesAppendix A: Personally Identifiable Information (PII)Appendix B: Security of Personally Owned Devices that Access or Maintain Sensitive Restricted DataAppendix C: Sensitive Security Information (SSI)

This project investigates the perceived characteristics of the data collected by the Google Voice Assistant (i.e. speech records). Speech records were collected through data donation, analysed, represented visually, and used during semi-structured interviews to interrogate people's perceptions of sensitivity and intimacy. The dataset includes the analysis and interview protocol, the visual representation of the data, and the thematic structure of the results.

The global sensitive data discovery market size reached USD 4.87 Billion in 2020 and is expected to reach a market size of USD 15.75 Billion in 2028 and register a CAGR of 15.7%. Sensitive data discovery industry report classifies global market by share, trend, and on the basis of component, organiz...

The European Values Study is a large-scale, cross-national and longitudinal survey research program on how Europeans think about family, work, religion, politics, and society. Repeated every nine years in an increasing number of countries, the survey provides insights into the ideas, beliefs, preferences, attitudes, values, and opinions of citizens all over Europe.

The EVS Trend File 1981-2017 is constructed from the five EVS waves and covers almost 40 years. In altogether 160 surveys, more than 224.000 respondents from 48 countries/regions were interviewed. It is based on the updated data of the EVS Longitudinal Data File 1981-2008 (v.3.1.0) and the current EVS 2017 Integrated Dataset (v.5.0.0).

For the EVS Trend File, a Restricted-Use File (ZA7504) is available in addition to the (factually anonymised) Scientific-Use File (ZA7503). The EVS Trend File – Sensitive Dataset (ZA7504) is provided as an add-on file. In addition to a small set of admin and protocol variables needed to merge with the SUF data, the Sensitive Dataset contains the following variables that could not be included in the scientific-use file due to their sensitive nature:

W005_3 Job profession/industry (3-digit ISCO88) - spouse/partner EVS 2008 W005_3_01 Job profession/industry (3-digit ISCO08) - spouse/partner EVS 2017 W005_4 Job profession/industry (4-digit ISCO88) - spouse/partner EVS 2008 X035_3 Job profession/industry (3-digit ISCO88) – respondent EVS 1999, EVS 2008 X035_3_01 Job profession/industry (3-digit ISCO08) - respondent EVS 2017 X035_4 Job profession/industry (4-digit ISCO88) – respondent EVS 1999, EVS 2008 x048c_n3 Region where the interview was conducted (NUTS-3): NUTS version 2006 EVS 2008 X048J_N3 Region where the interview was conducted (NUTS-3): NUTS version 2016 EVS 2017 X049 Size of town (8 categories) EVS 2008, EVS 2017

Detailed information on the anonymization process in the EVS Trend File is provided in the EVS Trend File Variable Report.
Study number; version; Digital Object Identifier, EVS-wave; country (ISO 3166-1 Numeric code); original respondent number; unified respondent number; country (ISO 3166-1 Alpha-2 code); country - wave; job profession/industry (3-digit ISCO88) - spouse/partner; job profession/industry (3-digit ISCO08) - spouse/partner; job profession/industry (4-digit ISCO88) - spouse/partner; job profession/industry (3-digit ISCO88) - respondent; job profession/industry (3-digit ISCO08) - respondent; job profession/industry (4-digit ISCO88) - respondent; region where the interview was conducted (NUTS-3): NUTS version 2006; region where the interview was conducted (NUTS-3): NUTS version 2016; size of town (8 categories).

In 2020, 81 percent of respondents state that sensitive data is stored in cloud storage, such as Google Drive. In general, the adoption of software as a service (SaaS) applications within organizations increases the need for a more flexible approach to data security, as sensitive data is now stored across many different applications.

CCP Sensitive Prompts

These prompts cover sensitive topics in China, and are likely to be censored by Chinese models.

The Biologically Sensitive Area sets out the criteria and procedures for a system relating to the management of fishing effort in waters around Ireland that are considered biologically sensitive. A specific effort regime shall apply to the area enclosed by the coast of Ireland to the south of 53° 30' N and to the west of 07° 00' W and straight lines sequentially joining the following geographical coordinates: - a point on the coast of Ireland at latitude 53° 30; N, latitude 53° 30 N, longitude 12° 00 W, latitude 53° 00 N, longitude 12° 00 W, latitude 51° 00 N, longitude 11° 00 W, latitude 49° 30 N, longitude 11° 00 W and latitude 49° 30 N, longitude 07° 00 W. A point on the coast of Ireland at longitude 07° 00 W. An area to the South and West of Ireland has been identified as an area of high concentration of juvenile hake. This area has been made subject to special restrictions on the use of demersal gear. For the same conservation purpose it should also be subject to specific effort limitation requirements within the general system described above. A review of these requirements by December 2008 would allow the Council to reassess the situation. Council Regulation (EC) No 1954/2003 of 4 November 2003 on the management of the fishing effort relating to certain Community fishing areas and resources and modifying Regulation (EC) No 2847/93 and repealing Regulations (EC) No 685/95 and (EC) No 2027/95. The area covered by this designation includes the south coast of Ireland in the Celtic Sea from Waterford to Mizen Head and the south-west coast of Ireland in the North Atlantic Ocean from Mizen Head to Galway Bay. In 2003 the EU Commission established a Biologically Sensitive Area (BSA); off the south west of Ireland. Area designated via fisheries science research information. Area has been designated as a protected site for fishing effort monitoring. The Biologically Sensitive Area has been created by the European Council directive. Area 100% designated a protected site for biological sensitivity.

According to a 2023 survey, 56.6 percent of sensitive data loss incidents at worldwide organizations caused business disruption and revenue loss. Almost 40 percent experienced a damaged reputation. Other repercussions of such incidents were weakened competitive positioning, regulatory violations, and fines.

The global sensitive data discovery software market is projected to reach a value of USD 1,484.2 million by 2033, expanding at a CAGR of 15.4% over the forecast period of 2025-2033. The market's growth is driven by the increasing need for data security and compliance, rising data volumes, and the growing adoption of cloud-based services. Key market trends include the adoption of artificial intelligence (AI) and machine learning (ML) technologies for automating data discovery and classification, increasing demand for data privacy and protection regulations globally, and the emergence of new technologies such as tokenization and encryption for protecting sensitive data. North America holds the largest market share, followed by Europe and Asia Pacific. The demand for sensitive data discovery software is expected to rise as organizations focus on protecting their sensitive data from unauthorized access or misuse, ensuring regulatory compliance, and mitigating data breaches.

Between the 9th and 15th of April 2023, per 100,000 employees, *** cases of sensitive data leaking on ChatGPT were spotted in worldwide companies. Compared to an observation between February and March 2023, the figure had increased by around ** percent. The second-most common type of confidential data shared on ChatGPT was source code, with *** cases per 100,000 employees.

Im Datensatz GLES Sensitive Regionaldaten werden die in den Scientific Use Files (SUF) anonymisierten Angaben, die sich auf den Wohnort der Befragungspersonen beziehen, für Forschungszwecke zur Verfügung gestellt. Über eine Identifizierungsnummer der Befragungspersonen können die benötigten Regionalvariablen dieses Datensatzes Datensatzes den Befragungsdaten des Vor- und Nachwahl-Querschnitt (GLES 2013) und den Befragungsdaten des Vor- und Nachwahl-Querschnitt (GLES 2017), sowie deren Kumulationen zugespielt werden. Dieser Datensatz enthält folgende sensitive Regionalvariablen (sowohl Kennziffern als auch Namen): • Regierungsbezirk (2013-2017) • Raumordnungsregion (2013-2017) • Landkreis, Kreis und kreisfreie Stadt (2013-2017) • Gemeinde (2013-2017) • Postleitzahl (2013 & 2017) • Wahlkreise (2013-2017) • NUTS-3-Code (2013-2017) • INSPIRE ID (1km) • Deanonymisierte Angaben zur politischen Gemeindegrößenklasse (gkpol) (nur 2017) und zum BIK-Typ der Gemeinde (bik) (2013-2017)

Die sensitiven Daten unterliegen einer besonderen Zugangsbeschränkung und eine Nutzung ist ausschließlich im Rahmen einer On-Site Nutzung im Secure Data Center (SDC) vor Ort in Köln möglich. Nähere Hinweise und Ansprechpartner finden Sie auf unseren Internetseiten: http://www.gesis.org/sdc.

Um Änderungen in den Gebietsständen der regionalen Einheiten (z. B. Kreisreformen, Eingemeindungen) zu berücksichtigen, werden die Regionalvariablen neben dem Stand des 1.1. des Jahres der Erhebung auch als zeitharmonisierte Variablen auf dem Stand des 31.12.2015 angeboten.

Sollten Sie über die Regionalvariablen zusätzliche Kontextmerkmale (regionale Attribute wie z. B. Arbeitslosenquote oder Wahlbeteiligung) hinzuspielen wollen, müssen Sie uns diese Daten vor Ihrem Besuch zukommen lassen. Daneben benötigen wir eine Quellenangabe und Dokumentation (Variablenerklärung) der Daten. Beachten Sie, dass diese Kontextdaten ggf. ebenso sensitiv sein können wie die Regionalvariablen, wenn darüber eine direkte Zuordnung möglich ist. Es ist datenschutzrechtlich problematisch, wenn einzelne Ausprägungen mithilfe einer Korrespondenztabelle auch ohne den GLES-Regionaldatensatz auf bestimmte regionale Einheiten und darüber letztlich auf die Befragungspersonen schließen lassen. Dementsprechend ist die Herausgabe (deskriptiver) Analyseergebnisse, die auf solchen Kontextdaten basieren, nur in vergröberter Form möglich.

Bitte setzen Sie sich zunächst mit dem GLES-Nutzerservice in Verbindung und senden Sie uns das ausgefüllte GLES-Regionaldatenformular (unter ´Daten & Dokumente´) zu, in dem Sie genau spezifizieren, welche GLES-Datensätze und welche Regionalvariablen Sie benötigen. Kontakt: gles@gesis.org

Sobald Sie mit dem GLES-Nutzerservice geklärt haben, welche genauen regionalen Merkmale für die On-Site-Nutzung zur Verfügung gestellt werden sollen, wird Ihnen der Datennutzungsvertrag für die Nutzung der Daten an einem SDC-Gastarbeitsplatz (Safe Room) in Köln zugeschickt. Geben Sie darin bitte alle Datensätze an, die Sie benötigen, also sowohl den „GLES Sensitive Regionaldaten (ZA6828)“ als auch die GLES-Datensätze, an die die Regionalvariablen herangespielt werden sollen. Des Weiteren nennen Sie bitte unter ´Spezifische Variablen´ alle Regionalvariablen, die Sie benötigen (siehe GLES-Regionaldatenformular).

These nutrient sensitive areas are those waterbodies listed in accordance with the Urban Waste Water Treatment (UWWT) Directive 91/271/EEC on Urban Waste Water Treatment and S.I. 254 / 2001, S.I. 440/2004 and S.I. 48/2010. The waterbody containing the sensitive area is used to represent the nutrient sensitive area.

Environmental Sensitivity Index (ESI) data characterize the marine and coastal environments and wildlife based on sensitivity to spilled oil. Coastal species that are listed as threatened, endangered, or as a species of concern, by either federal or state governments, are a primary focus. A subset of the ESI data, the ESI Threatened and Endangered Species (T&E) databases focus strictly on these species. Species are mapped individually. In addition to showing spatial extent, each species polygon, point, or line has attributes describing abundance, seasonality, threatened/endangered status, and life history. Both the state and federal status is provided, along with the year the ESI data were published. This is important, as the status of a species can vary over time. As always, the ESI data are a snapshot in time. The biology layers focus on threatened/endangered status, areas of high concentration, and areas where sensitive life stages may occur. Supporting data tables provide species-/location-specific abundance, seasonality, status, life history, and source information. Human-use resources mapped include managed areas (parks, refuges, critical habitats, etc.) and resources that may be impacted by oiling and/or cleanup, such as beaches, archaeological sites, marinas, etc. ESIs are available for the majority of the US coastline, as well as the US territories. ESI data are available as PDF maps, as well as in a variety of GIS formats. For more information, go to http://response.restoration.noaa.gov/esi . To download complete ESI data sets, go to http://response.restoration.noaa.gov/esi_download .

The polygon dataset represents areas with acid-sensitive waters in the contiguous United States. Summary data in this indicator were provided by EPA’s Office of Atmospheric Programs and are taken from a publication documenting how surface waters have responded to reduced air emissions of acid rain precursors (U.S. EPA, 2003) and from more recent unpublished results (U.S. EPA, 2014). Trends are based on data collected in two networks: the TIME project and the LTM project. Because both networks are operated by numerous collaborators in state agencies, academic institutions, and other federal agencies, the monitoring data are not available in a single publication or database. The trend data in this indicator are based on observations documented in several publications (see pages 15-17 of U.S. EPA, 2003).