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What are the best practices for securing GIS data during acquisition and storage?

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GIS data is essential for environmental engineering projects, as it provides spatial information and analysis for various environmental issues, such as pollution, climate change, natural hazards, and resource management. However, GIS data also poses security risks, as it may contain sensitive or confidential information that could be compromised by unauthorized access, theft, or manipulation. Therefore, it is important to follow some best practices for securing GIS data during acquisition and storage, which we will discuss in this article.

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Ashish kumar Yadav

Assistant Professor at Amity University, Greater Noida
Youssef DAAFI

1 Data acquisition

Data acquisition is the process of collecting and importing GIS data from various sources, such as sensors, satellites, drones, surveys, or databases. During this process, you should ensure that the data sources are reliable and trustworthy, and that the data quality is verified and validated. You should also use encryption and authentication methods to protect the data transmission and prevent interception or tampering. For example, you can use secure protocols, such as HTTPS or SFTP, or encryption tools, such as SSL or VPN, to transfer the data securely.

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Ashish kumar Yadav

Assistant Professor at Amity University, Greater Noida
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To secure GIS data during acquisition and storage, Implement strict access controls, employ encryption for transmission and storage, classify data based on sensitivity, and establish backup procedures while staying current with software updates and monitoring vendor security.

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Javier Jerez Fernández - Energías Renovables

(Nuevas tecnologías renovables y Crecimiento personal)
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Para proteger los datos SIG durante la adquisición y almacenamiento, sugiero identificar y cifrar los datos sensibles, aplicar controles de seguridad, seguir procedimientos de adquisición adecuados y almacenar los datos en entornos seguros. Además, es muy importante el cifrado, la auditoría de acceso y las consideraciones específicas para datos SIG, como el riesgo de información geográfica precisa. Recomiendo utilizar soluciones de seguridad especializadas para datos SIG, ayudando a prevenir el acceso no autorizado, pérdida o da?o.

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2 Data storage

Data storage is the process of saving and organizing GIS data in a physical or cloud-based location, such as a hard drive, a server, or a cloud service. During this process, you should ensure that the data storage location is secure and accessible, and that the data backup and recovery is available and reliable. You should also use encryption and authorization methods to protect the data access and prevent leakage or loss. For example, you can use password protection, access control, firewalls, or encryption software, such as AES or BitLocker, to secure the data storage.

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Youssef DAAFI
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From my experience as a GIS user, I recommend: - Implement a robust storage infrastructure with access controls. - Use role-based permissions to restrict data access based on user roles. - Regularly update and patch storage systems to address vulnerabilities. - Employ geolocation data backup strategies to ensure data availability, especially during mergers and acquisitions

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3 Data classification

Data classification is the process of assigning different levels of sensitivity and confidentiality to GIS data, based on the potential impact of disclosure or misuse. For example, you can use a four-level classification scheme, such as public, internal, confidential, or secret, to categorize the data according to the degree of protection required. Data classification helps you to determine the appropriate security measures and policies for each data type, and to comply with the relevant legal and ethical standards.

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Youssef DAAFI
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I add the following recommendations to the linkedin answer: - Classify GIS data based on sensitivity levels. - Prioritize critical data with stricter access controls. - Regularly review and update data classifications to adapt to evolving security needs. - Clearly define and communicate data classification policies across the organization

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4 Data anonymization

Data anonymization is the process of removing or modifying personal or identifiable information from GIS data, such as names, addresses, phone numbers, or email addresses. This process helps to protect the privacy and anonymity of the data subjects, and to reduce the risk of identity theft or fraud. Data anonymization can be done by using various techniques, such as masking, hashing, aggregation, or generalization, to alter or replace the original data with fictitious or random values.

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S. Andrés Ochoa-Chávez

Jefe de Medio Ambiente y Sistema de Gestión | Auditor Líder ISO | Consultor Empresarial | Especialista de Procesos | Investigador PAOs
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Como parte de la gestión de datos es importante asegurar los distintos niveles de seguridad. Un buen sistema de seguridad de la información, seguido de la clasificación de datos, escalamiento de los niveles de la información y tratamiento de datos, darán como resultado una gestión óptima de los datos SIG.

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5 Data auditing

Data auditing is the process of monitoring and recording the activities and events related to GIS data, such as creation, modification, deletion, access, or transfer. This process helps to track and verify the data integrity and security, and to detect and respond to any potential breaches or incidents. Data auditing can be done by using various tools, such as logs, alerts, reports, or dashboards, to capture and analyze the data history and usage.

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Dr (Er).Suresh Sadhasivam

Deputy General Manager(DGM)-Plant ( Ex- Parry Nutraceuticals (EID Parry (I) Ltd., Arjuna Natural Extracts Ltd.,& Indfrag Bioscience Ltd.,)
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Data auditing is heart for GIS, to have trend of data ,we have to audit at frequent intervals which is to ensure the data is being collected as per predetermined mechanism otherwise we may not be having any control over data collection, it has to give alert whenever trend is in positive and negative , so that we can corelate the cause behind the trend.

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Youssef DAAFI
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- Establish a comprehensive auditing process to monitor data access and modifications. - Regularly review audit logs to detect and respond to suspicious activities promptly. - Conduct periodic security audits to assess the effectiveness of existing security

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6 Data disposal

Data disposal is the process of deleting or destroying GIS data that is no longer needed or relevant, or that has reached its retention period. This process helps to free up storage space and resources, and to prevent the accumulation of obsolete or redundant data. Data disposal can be done by using various methods, such as erasing, overwriting, shredding, or degaussing, to ensure that the data is completely and irreversibly removed from the storage media.

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Marcello Couto
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N?o há uma fórmula ou receita única, tendo que ser avaliado o tipo de mídia utilizado e o método de armazenamento e indexa??o. Dados sensíveis n?o podem ser apenas simplesmente apagados, pois podem ser recuperados. Devem passar por um processo de reescrita na mesma mídia com novas informa??es. Há softwares que prometem apagar definitivamente os dados, assim como há pessoas especializadas em recuperá-los. Nunca, em hipótese alguma, descarte ou ceda para outro fim a mídia onde estes dados foram armazenados. Quer ter seguran?a? Destrua esta mídia. Isso sem contar os backups, que s?o um capítulo à parte, ainda mais com a cloud computing...

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best practices for securing GIS data during acquisition and storage?

1

2

3

4

5

6

7

1 Data acquisition

2 Data storage

3 Data classification

4 Data anonymization

5 Data auditing

6 Data disposal

7 Here’s what else to consider

Environmental Engineering

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What are the best practices for securing GIS data during acquisition and storage?

1

2

3

4

5

6

7

1 Data acquisition

2 Data storage

3 Data classification

4 Data anonymization

5 Data auditing

6 Data disposal

7 Here’s what else to consider

Environmental Engineering

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