You're tasked with protecting sensitive data remotely. How can you ensure its security as a data architect?

Data Security is of utmost importance at every stage of data lifecycle. 1. Data at rest: RBAC, Data masking and encryption for PI/PII, row level filtering, VPN to access data remotely. 2. Data at transit: Network level security, Data should be encrypted over network and should be decrypted at target, API's exposing data should have industry standard authentication and integrated with RBAC. Data stored remotely should comply to regulations of originating geography as well as target geography if required. Data security hub should be in place to monitor each activity of user/system who is accessing the data and realtime alerts should be triggered to mitigate any breach.

To protect sensitive data effectively, a multi-layered security approach is essential. This involves implementing strong authentication and authorization mechanisms, such as MFA and RBAC to ensure only authorized individuals can access data. Encryption should be used for both data at rest and in transit to protect against unauthorized access and interception. Furthermore, isolating sensitive data in dedicated networks and using VPN security controls for remote access can help minimize the risk of unauthorized access or data breaches. By combining these security controls and adhering to principles like the least privilege principle, organizations can significantly enhance their data security posture.

Using encryption keys one can protect sensitive data during rest and transit. One can use keys like AES when data in rest and use TLS, SSL, IPsec during transit

To secure sensitive data remotely, encrypt data at rest and in transit using robust encryption methods. Implement multi-factor authentication and strict access controls following the principle of least privilege. Utilize identity and access management systems, real-time monitoring, and logging to detect suspicious activities. Conduct regular security audits, penetration testing, and vulnerability scanning. Securely backup data and ensure compliance with regulations like GDPR. Follow industry standards, educate users on security best practices, and maintain strong endpoint security measures on all remote devices to mitigate risks.

Sensitive data which are to be remotely accessed needs to be encrypted both at rest as well as in transit. There are various methodologies available today for implementing the encryption at different stages. In the case of at rest, various database vendors support algorithms like SHA16,SHA32 etc. for encryption. Also encryption can be implemented at various levels up to column level. At transit, databases support Transport Layer Security (TLS) to encrypt data that is transmitted across a network between the database instance and a client application. The combination of above methodologies can be used to ensure data is encrypted at all stages whilst trying to transfer across networks for remote access from an application.

Access control is again very important, however, using a PBAC gives a better control on this compared to RBAC. It is better to provide the access control using views over the data, rather than directly giving access to the data

As a data architect, I prioritize robust access controls to protect sensitive data. I design systems based on the principle of least privilege, ensuring users only access data essential to their roles. We implement multi-factor authentication (MFA) to add an extra security layer and use role-based access control (RBAC) for effective permission management. Regular reviews and updates of access rights help prevent unauthorized access and data breaches, ensuring a secure and compliant environment.

A robust semantic layer can enhance the security of sensitive data beyond the security reviews(which most managers rubber stamp) by allowing the filtering and masking of sensitive data.

While the principles of least privilege, multi-factor authentication, and role-based access control are indeed crucial, they fall short in addressing the complexities of modern, distributed data ecosystems. The lack of a comprehensive, universally adopted standard for access control in big data environments is a significant oversight. This gap is particularly evident when we consider the concept of Data Mesh. Moreover, the current approaches often fail to address the dynamic nature of data access needs in big data environments. As Jarmul emphasizes, we need "dynamic data masking and access controls" that can adapt to changing contexts and user needs in real-time.

I wholeheartedly agree with the importance of robust data encryption. Implementing strong encryption standards like AES-256 is undoubtedly essential for safeguarding sensitive information. However, it's crucial to go beyond just encrypting data at rest and in transit. Effective key management, including regular rotation and secure storage, is paramount. Additionally, considering encryption of data in use can provide an extra layer of protection. A comprehensive data protection strategy also involves conducting thorough risk assessments to identify and prioritize sensitive data. By understanding the potential threats, organizations can allocate resources effectively to mitigate risks. Moreover, staying compliant with industry regulations.

Apart from the firewalls, the services related to endpoints and private links can be utilized if the data is hosted on cloud. This will ensure any transmission of data taking place within the cloud service provided backbone network.

In addition deploy firewalls and Data loss prevention systems to prevent leakage of sensitive data across organizational boundaries. Develop a DLP policy and chose the appropriate tools to protect data at rest, transit and in use.

The traditional approach of putting all your data behind a firewall and trusting everyone on the inside is no longer viable. Modern data architectures like Data Mesh which advocates for decentralized, domain-oriented data ownership, further complicates network security. I am currently working on a paper for a data-layer security controls that travel with the data.

Protecting sensitive information is paramount for organizations of all sizes, and encryption is a cornerstone of this protection. Your focus on securing data both at rest and in transit is essential for mitigating risks. Its crucial to consider other advanced encryption algorithms like the Galois/Counter Mode (GCM) for its efficiency and authentication capabilities. Effective key management is equally vital. Regular key rotation, secure storage, and restricted access are fundamental to preventing unauthorized access. Additionally, exploring options like hardware security modules (HSMs) for key management can significantly enhance security. A comprehensive approach to data protection also involves conducting thorough risk assessments.

Network is very important to secure and protect data by use multi firewall if the network had online or offline , protect servers by use certificate. SSL use multi levels (database, servers, network) as all of them must use encryption data, certificate on db and server,SSL ,TLS

Regular Automated Backups: Schedule automated backups to ensure data is consistently saved without manual intervention. Multiple Secure Locations: Store backups in multiple secure locations to protect against localized failures. Consider using cloud services for redundancy, ensuring they comply with your organization's security standards. Regular Testing: Regularly test backup systems to verify their reliability in an emergency and ensure data recovery processes are effective. By following these practices, you can significantly reduce the risk of data loss and ensure business continuity.

Data storytelling can revolutionize stakeholder engagement in decision-making. Start by identifying the core message from your data. Use visuals like charts and graphs to highlight trends and insights clearly. Craft a narrative that connects data points to business goals, showing the impact of decisions. Simplify complex data into relatable stories, making it easier for stakeholders to understand and remember. Incorporate real-world examples to add context. Finally, encourage interactive sessions where stakeholders can explore data, ask questions, and see potential outcomes. This approach turns raw data into actionable insights, fostering informed decision-making.

There is no room for uncertainty when it comes to protecting sensitive information. With cyber threats and data breaches on the rise, it is vital to have confidence in the security and backup measures in place. A comprehensive data backup system serves as a crucial safeguard against unforeseen disasters, whether caused by hardware failures, natural disasters, or malicious attacks. Regular and secure backups ensure that valuable information remains intact, even in the face of adversity. But the benefits go beyond mere protection. A well-executed backup strategy allows for efficient data restoration in the event of loss or corruption, minimizing downtime and disruption to business operations. This saves valuable time and resources.

Implementei um plano de backup de dados que realiza backups automatizados e regulares em locais geograficamente dispersos, incluindo nuvens híbridas para maior redundancia. Além disso, utilizei armazenamento imutável para garantir que os backups n?o possam ser alterados ou excluídos, protegendo contra ransomware. Testes periódicos garantem a eficácia dos processos de recupera??o, assegurando que os dados possam ser restaurados rapidamente em caso de emergência.

I think before starting to work with sensitive data the first point is to make a thorough study if we do need the sensitive data. I have witnessed cases where we directly consider transferring all the data to another system, including PII without taking the time to check if this is really needed. So my first point here will be to really ensure we needed, and if the answer is Yes, then consider options like anonymization or pseudoanonymization, as sometimes we might just need some insights or statistics that does not require all the data fields. Then, if still we need to use that data, then we need to have strict policies in place to ensure the data is encrypted, the access is protected, and all the needed protection measures are in place.

Políticas de governan?a de dados s?o imprescindíveis em toda organiza??o que deseja ser data driven, isso implica na cria??o de normas e procedimentos para garantir a seguran?a de todos os dados disponíveis em sua organiza??o, a escolha de uma plataforma de inteligência de dados também tornaram seus processos mais seguros e escalaveis.

Developing and implementing data policies is crucial for any organisation. Data policy development involves a systematic and analytical approach to establishing guidelines and best practices that promote data privacy, integrity, and accessibility. To begin, a thorough understanding of the organisation's specific data needs and objectives is essential. This includes identifying the types of data collected, the purposes for which it is used, and the legal and regulatory requirements that must be complied with. Furthermore, data policy development should consider emerging trends and technologies, such as cloud computing and big data analytics.

Develop sensitive data protection policies and integrate to your organisation's information security policy. Ensure that the various levels of previleged access is properly documented and revised periodically as per business needs. Monitor all previlege accesses using automated tools and prevent previlege escalations.

Categorize data based on sensitivity levels (public, internal, confidential, highly confidential). Implement role-based access control (RBAC) based on these classifications. Employ robust encryption algorithms for data at rest and in transit. Use strong key management practices. Enforce strong password policies and multi-factor authentication (MFA). Regularly review and audit access privileges.

I could not find a relevant section to add modelling/ design considerations for data security, hence adding here. 1. Ensure that the data classification is widely understood at the time of design. 2. Columns required for access controls must be included in the design , correctly and consistently defined with right data types, optionality/ cardinality . 3. Follow the principles of single source of truth for highly secure data.. Avoid creating redundant copies of secure data that may result in increased points of vulnerability. 4. Design and Deploy mechanisms(processes and tools included) in place that would enable the security policies to be implemented across the lifecycle of data consistently irrespective of its medium of storage.

In addition to the crucial measures mentioned, I would also add: Conducting regular security audits to identify & address vulnerabilities. These audits help ensure that our security measures are up-to-date and effective. Implementing continuous monitoring & logging to detect and respond to suspicious activities in real-time. This proactive approach enables us to quickly identify & mitigate potential threats. And most importantly we must ensure the human element is not overlooked. Providing regular training to employees on data security strategies, best practices, phishing awareness, & recognizing social engineering attacks is vital. This training helps create a security-aware culture & reduces the risk of human error.

If/when you suffer a breach, there is a high likelihood of an aggressive and rapid kneejerk reaction. This is partially warranted, and changes may need to be made, but remember that security and useability are incompatible design goals - a perfectly secure system is a totally inaccessible one. Remember that systems and data exist to server the business. Be careful of overcorrections leading to frustration and real disruption of BAU operations and efficiency loss. Accept that most breaches come through carelessness or social engineering, and you are unlikely to be able to correct for these by making your systems more frustrating to access legitimately.

Participei do desenvolvimento de um plano robusto de resposta a incidentes, detalhando procedimentos claros para identificar, conter e erradicar amea?as, além de recuperar dados comprometidos. Para refor?ar a prontid?o, implementei exercícios de simula??o de ataques cibernéticos que testam a eficácia do plano e garantem que todos saibam suas fun??es em caso de incidente. Essa abordagem proativa minimiza o impacto das viola??es e fortalece a resiliência da equipe.

To engage stakeholders in decision-making through data storytelling, follow these steps: Understand Audience: Know their interests and decision-making criteria. Set Context: Start with the problem or opportunity. Data Selection: Choose relevant, compelling data. Narrative Structure: Build a clear beginning, middle, and end. Visualization: Use charts and visuals to simplify complex data. Actionable Insights: Highlight key insights and implications. Engage Emotion: Connect data to stakeholder values and goals. Feedback Loop: Encourage questions and feedback for continuous engagement. By making data relatable and actionable, you foster informed decision-making.