Wie können Sie den Datenschutz bei der Arbeit mit BI wahren?

Consider data anonymization techniques in Business Intelligence to protect individual identities while still deriving valuable insights. Implementing differential privacy measures can add a layer of noise to aggregate data, preserving privacy without compromising overall analytics accuracy. Additionally, educate BI users on ethical data practices, fostering a collective responsibility for maintaining privacy throughout the analytics lifecycle.

Data privacy can be maintained by implementing the below strategies : 1. Anonymization and Masking of PII data to protect individual identities. 2. Encryption of data at rest and in transit. 3. Classifying the data based on sensitivity, by defining data into categories like sensitive data, confidential data, public information etc. 4. By establishing a Data Governance framework. 5. Complying with regulations like FedRamp, HIPAA, SOC, ISO etc. 6. Implementing access control, audit trails, and conducting periodic security audits.

Quero um selo top voice , irei postar aqui algo repetitivo porém gerado pelo CH@T GPÊT , me ajude deixando seu like para eu conseguir o selo top voice. Navigating the complex landscape of data regulations is crucial for businesses aiming to uphold data privacy standards. Beyond GDPR, CCPA, and PIPEDA, understanding regional and industry-specific laws is imperative. Conduct a comprehensive assessment to identify applicable regulations and implement a robust compliance strategy. Regularly update your knowledge base, as regulations evolve, to ensure sustained adherence. Engaging legal counsel and leveraging compliance management tools can further streamline the process, safeguarding both your business and the privacy rights of data subjects.

My perspective is that these expertise-seeking questions are preposterous. First, they suggest that short form answers are sufficient. Second, the questions they pose contain such nuance and ambiguity that they are unanswerable by anyone with real expertise. Third, anyone capable of answering would be giving away their value for clout by answering How about we talk about the failures of Product Managers who read case studies with buzz words like "crowd sourcing" to develop features like these?

The responsibility for data protection is shared between the data controller and the data processor. It's crucial to review Microsoft's terms of service and ensure that your use of Business Intelligence aligns with your organization's data protection policies. Keep in mind that the specifics may vary depending on your location, the nature of the data you're handling, and your organization's industry. It's advisable to consult legal and privacy professionals to ensure compliance with applicable laws and regulations.

Eu diria que os pontos a seguir são cruciais para garantirmos um alto nível de privacidade em projetos de BI, veja: - Controle de acesso - Anonimização de Dados - Criptografia - Políticas de Privacidade e Conformidade (LGPD) - Monitoramento e Auditoria - Treinamento de Funcionários sobre as práticas adotas

Compliance with data privacy regulations like GDPR, CCPA, and PIPEDA is essential. These laws govern data collection, use, storage, and sharing, and grant rights to data subjects. Key steps include: Identifying applicable regulations based on data type and location. Adapting data handling processes to meet legal standards, including consent mechanisms and data protection protocols. Educating your team about these regulations and their roles in compliance. Regularly reviewing and updating practices to stay current with evolving laws. Preparing for data subject requests under these regulations. Staying compliant mitigates legal risks and builds trust with users and customers.

In my experience below is the checklist -end to end encryption of the data -role based hierarchical access -secure infra -data accountability -security protocols in terms of inflow and outflow of the data . -masked with some antivirus and antiphising solutions .

Tão importantes quanto os produtos, serviços, colaboradores e clientes, os dados são imprescindíveis aos negócios. Sendo assim, é importante para um projeto de BI profissionais com conhecimento das leis e regulamentos relacionados à proteção de dados. No Brasil, a Lei Geral de Proteção de Dados Pessoais (LGPD) foi promulgada para proteger os direitos fundamentais de liberdade e de privacidade.

Maintaining data privacy is must while working with BI as we directly play with the real time data, here's few points which we can consider to maintain the privacy and integrity of the data - - allow Role Based Access, define roles and allow only the roles to view and access their respective data points and insights which they are authorised for. - practice masking of the PIIs. Once we were dealing with some review texts which contained Names and that's where masking helped us to maintain the privacy. - ensure data is encrypted wherever needed - make sure the datasets are accessible by only authorised person and can't be used/misused by someone else to create some insights out of it without the proper permission.

Role-Based Access Control (RBAC) Implementation will bring an effective way of utilization of securely! All data transmitted between servers and clients to be encrypted using secure protocols

Always need to take care the disconnection of data that you analyze with personal information. The key approach differentiator is not to hide sensitive data, but to de-personalize. That is you may keep any kind of information you need to analyze as long as the information is not pointing uniquely to a specific individual or a small groups of individuals. This should be also true when combining info.

La anonimización y encriptación de datos cobra especial interés cuando nuestro almacén de datos está en cloud. Debemos tener en cuenta que esta encriptación se deberá realizar en todas las fases del ciclo de vida del dato: en tránsito, en procesamiento y en reposo, para ello las nubes públicas cuentan con capacidades que de salida nos dan estas funcionalidades, pero en ningún caso debemos perder el foco de su importancia. También es necesario anonimizar si hay datos que se pueden conectar con facilidad con personas, cómo son el DNI, número de teléfono, etc. para proteger a estas personas y esos datos existen una gran cantidad de técnicas que podemos utilizar cómo son: supresión, data masking, tokenización, permutación, añadir ruido, etc

Estruturar dados classificados previamente e fonte de consulta deste BI, seja um data lake ou qualquer repositório, estabelecer Controle de Acesso Baseado em Função (RBAC) e controles adicionais como: scramble, anonimização, criptografia.

Personal identifiable data should be anonymised. This should be automated prior to data being available in the analytical environment.

Encrypting data is effective but business needs to be aware that technical teams are often able to read code and identify the encryption key used. Proper training and compliance checks need to be in place to ensure data is not misused.

Some techniques you can use: - Data Masking - Remove Unnecessary Columns: Identify and remove columns that are not essential for your analysis. This reduces the risk of exposing sensitive information. - Break Data Links: After anonymizing data, you can break the link to the original data source by exporting the anonymized data and re-importing it into your BI platform. - Row Level Security (RLS): Implement RLS, if your data platform supports it, to control access to data based on user roles. This ensures that only authorized users can view sensitive information

Encryption and decryption of banking related is very crucial mainly in ERP systems.if anybody changed profile options the data may expose in Bi reports. In these cases need extra protection

Having recently helped teach as well as leverage several encryption techniques in my recent past, it is important to understand the methods and approaches. However, with systems such as RBAC (Role Based Access Controls) it is possible to apply policies that leverage obfuscation and masking. Making the data filtered to the role the user is assigned. Last but certainly not least, the ability to leverage audit history will allow access that do nut fit within the defined RBAC.

Safeguarding privacy requires diligently anonymizing datasets by removing identifying details and employing aggregation, pseudonymization, and randomization. Encrypting data provides additional protection by transforming information into coded form, only decipherable with a decryption key. Together, proactive anonymization and encryption allow harnessing data's value for business insights without compromising individual rights and consent. Integrating these privacy-preserving practices throughout analytics workflows demonstrates an organization's commitment to ethical data stewardship.

What is almost as important as implementing access control and audit trails is to implement this once only! Because of moder data estate which evolve to distributed architectures multi-cloud and hybrid-cloud with different data services accessing the data. The ability to federate the access control and manage the associated audit trail efficiently is key. Solutions like Immuta bring a very strong value proposal on that specific area.

El registro de actividades o eventos, es una parte fundamental de la seguridad y trazabilidad de los datos. Este registro debe proporcionar un historial detallado de las acciones CRUD, los accesos, quien los ha realizado, tipo, etc, con el objetivo de realizar una detección temprana de posibles amenazas, la respuesta a incidentes y la demostración de cumplimiento normativo.

Conducting quarterly audits on end user group access becomes vital in mid to large size organisations with new and old employees joining or changing departments. A healthy audit reporting, often available through the tools currently in place allows to safeguard sensitive information on business and customers.

Qual é o ativo mais valioso da empresa hoje? Se a resposta não for os dados, é importante entender que há riscos para os quais deve-se estar preparado. Sistemas, softwares e aplicativos precisam ser aliados importantesna luta em prol da privacidade dos dados. Mecanismos de controle de acesso e auditoria interna fazem parte de um sistema preparado para tratar questões de proteção da informação.

When talking about audit trails, it’s important to define Data retention / archiving strategy for the logs, timestamps ..etc.

To easily explain how to implement access controls, I will take example of Power BI. Viewer As is self-explanatory by the title, these kinds of users can view and interact with reports and dashboards without changing their contents. Contributor Apart from being able to view and interact with reports and dashboards, this user type goes a step further by being able to create, publish, edit, copy, and delete items in workspace Member This user type can do all that the preceding user types can do. In addition, the member user type can also feature dashboards on the service, share items, allow others to re-share items, and publish or republish.

In case of critical data any searches done on certain attributes / parameters should be notified to the IT security team for further investigation.

One potential threat is the duplication of data. Since regulations empower individuals to revoke their consent of saving and using their data, it's duplication, for example into desktop versions of BI Tools, is creating a massive headache. It's important, to either simply directly query the source or implement a policy that tracks every download of data to desktop versions and ensure proper deletion when necessary.

I have found the things below very helpful during my tenure at managing BI reports over servers: 1. Create different Access groups as per requirements and assign specific privileges to each group 2. Even if access groups are not present, certain BI tools provide multiple access control options at server individually for e.g. you can provide download for some users and not for others. 3. Ensure the final link for the report is sent across only for intended downstream users and they too have only the required access as mentioned above. 4. Finally we can hide or mask personal or sensitive information in the report itself which is a different activity under dashboard and data cleanup

In the context of patient data in the EU, discussions related the location of data. Patient data should be located in the EU/ETA area. Challenges arise concerning data management, such as the location of backups and log files. Considerations also include whether support activities for the hosting service are held outside the EU region.

Treinamento é fundamental, treinem todos de sua organização e mostrem sempre a importância de cada um em relação ao todo! Vejo que tão importante quanto treinar, é reciclar! Ou seja, de tempos em tempos ter ações que levante o assunto e refresque a memória. Lembrem-se, o lado mais fraco da segurança da informação são os usuários! De nada adianta a tecnologia mais atual, milhões de investimento... se o usuário não tem essa visão! Fica a dica!

Não adianta a porta blindada de um cofre se uma pessoa mal-intencionada souber a senha para abri-la. Nesse sentido, o uso de aplicativos de segurança se faz necessário, contando com uma equipe de tecnologia da informação antenada e competente. Porém, é a conscientização e a construção de uma cultura forte sobre a proteção de dados, nesse caso, que em grande medida vão determinar se o ambiente corporativo se tornará cada vez mais seguro ou não.

Entender que toda a empresa faz parte do processo é importante. Não é porque uma pessoa realiza uma parte do processo que ela não se torna responsável por todo o projeto. Por isso é importante treinar e educar todos os funcionários, reciclar o conhecimento, desenvolver novas aptidões, demonstrar onde cada atividade está empregada, seus riscos e contribuições. Os dados utilizados pela empresa são de responsabilidade de todos, seja na proteção ou no manuseio.

Una de las recomendaciones principales es mantener capacitaciones constantes sobre la importancia de la calidad, veracidad e integridad de la informacion. Abarcando todos estos temas bajo la premisa de la seguridad y privacidad de los datos.

There are several possible anonymization techniques, but before adopting a technique or tool, the most appropriate approach, understanding the business need, is to apply minimization and a fair and necessary purpose for processing personal or sensitive data, thus, whenever possible, avoid capturing , extract or process PII or data that together may lead to the identification of one or a group of people, which may result in discriminatory or harmful processing to data subjects.

O importante da privacidade de dados em Business inteligência é adotar ferramentas e práticas que garantam o anonimato dos dados e a qualificação da equipe trata. Com relação à equipe que vai tratar os dados a conscientização do treinamento é vital além de um termo de confidencialidade e responsabilização pelo vazamento de dados. Procedimentos de Double Check controle de log sistemas de transferência de carregamento de dados são vitais para garantir a privacidade base de dados. Um sistema de criptografia é crucial e o sistema de análise via cubo de dados são práticas mais avançadas que garantem segurança

Capacitar al equipo es siempre importante, no sólo cuando se trata de mantener la privacidad de los datos pero sin lugar a dudas en el área de BI es una tarea a la que los líderes deben dedicar una parte de su tiempo, en este aspecto quiero destacar : Actualización: BI es un área en el que los avances y cambios son constantes. La formación continua permite a los equipos técnicos mantenerse al día con las últimas tendencias en analítica lo que es esencial para garantizar la eficiencia y la competitividad de la organización. Reducción de errores: los equipos bien formados cometen menos errores, esto redunda en más velocidad, menor coste, menos riesgos. Otros factores son: productividad, adaptabilidad, innovación, motivación, cumplimiento.

Trabalhando com Business Intelligence, sempre priorizo a privacidade dos dados. Comprometido com ética e inovação responsável.

Educating the team on business intelligence (BI) data protection is crucial to ensuring that your organization complies with data privacy regulations and best practices. Here's a few steps on how to educate your team on BI data protection: 1. Understand Data Protection Laws 2. Highlight the Importance of Data Protection 3. Define Roles and Responsibilities 4. Access Controls and Permissions 5. Data Classification 6. Regular Audits and Monitoring

What's missing is an important prerequisite for any type of organization, SME, NPO or multi-national: knowing your data. Where is the "personal information" data in the organization? What processes, data flows and people use it? What are their classification levels? It's hard to control access or anonymize data if we can't recognize it. Any person or organization wishing to better manage its data should take the time to conduct an initial census of personal information (80/20), in order to properly manage the risk while balancing the effort involved.

What I haven’t seen mentioned is understanding your organization’s data classification; what’s sensitive, personal, confidential ..etc.

En mi experiencia, al usar metadatos relacionados a voz, he logrado entender algunas características particulares de los datos como información, que dependiendo de cómo la manejes puedes convertir un patrón biométrico como la voz (tonalidad, decibeles) en una llave más segura, añadiendo datos de cómo mencionas tu número de documento, si te pido decir tu número de documento, dirás este uno por uno, asociado en pares y cada uno de estos en un tiempo determinado, siendo para cada persona un patrón distinto, entonces el uso que se le puede dar a la información no tiene límites, tan solo es investigar un poco y pensar fuera de la caja.

Requisitos y Objetivo A la hora de implementar soluciones de BI, es muy importante captar el objetivo principal del proyecto y los requisitos que se deben cumplir, es decir, realizar un análisis lo más completo posible. De esta manera, se podrá realizar el modelado y diseño exacto y necesario, donde se podrán tener en cuenta los puntos mencionados en el artículo y nos ayudará a gestionar correctamente la información. Posteriormente, podremos mantener los correctos niveles de privacidad en base a los roles de los usuarios que la exploten o evitar las consideraciones legales de cada región recopilando los datos únicamente necesarios para nuestra finalidad, que en muchas ocasiones son identificadores mínimos sin necesidad de enmascarar.

Data management is a critical factor. Get rid of old data. Use data consolidation whenever it is appropriate. These approaches help reduce data risk over the long term.

Para proteger y mantener la privacidad de los datos hay que implementar procedimientos y herramientas técnicas que lo permitan, pero además siempre debemos tener en cuenta a las personas. Es importante contemplar la posibilidad de sufrir "Hackeo social". En muchas ocasiones esas herramientas y protocolos se ven afectadas por los descuidos y errores humanos. Personas malintencionadas manipulan a los empleados y obtienen información confidencial o acceso a los sistemas informáticos para realizar acciones no autorizadas. A diferencia de la piratería informática tradicional, que se centra en vulnerabilidades tecnológicas, el hackeo social se enfoca en explotar las debilidades humanas para lograr sus objetivos.

When diving into Business Intelligence, safeguarding data privacy is non-negotiable. Start with robust access controls, limit data access to essentials. Regularly audit user permissions and ensure data encryption both in transit and at rest. Prioritize data anonymization when possible, and instill a culture of data ethics among your team. Stay vigilant with updates on security best practices and always comply with data protection regulations.

In the financial sector, banks use BI and AI for customer analyses and credit risk assessments. They implement data protection through anonymisation, pseudonymisation and strict access controls. Audit trails and monitoring systems ensure compliance with data protection regulations such as the GDPR. AI models, such as for credit risk, utilise differential privacy to avoid identifying individuals while enabling accurate predictions. These practices show how BI and AI can be used in sensitive areas in compliance with data protection regulations.