You're managing sensitive data. How can you ensure privacy without sacrificing efficiency?

To safeguard sensitive data while maintaining efficiency, implement a tiered access control system. Classify data based on sensitivity and grant access rights accordingly. Utilize encryption for data in transit and at rest. Regularly audit and monitor access logs to detect and respond to anomalies promptly. Train staff on privacy protocols to ensure adherence. Employ anonymization and pseudonymization techniques when possible to balance privacy and usability. Continuously update policies and conduct thorough risk assessments to address emerging threats. Collaborate with legal and compliance teams to stay aligned with privacy regulations, fostering a privacy-conscious culture without hindering operational efficiency.

Begin with efficiency. The sensitive data solution must exist within the larger data governance platform. In 2024 no firm budgets for standalone data privacy/sensitive data products. Position CISO advocacy for your approach - early. Ensure this solution provides an OOTB periodically updated sensitive data dictionary. Sensitive data scanning jobs must auto-adjust to that dictionary and offer results-based obfuscation per scanned results. Avoid manual rework here. Define, test and validate the solution attests to (with updated heatmaps) coverage for GDPR, CCPA, GLBA requirements. Align a published, shared sensitive data responsibility model between your CISO, business partners and data product owners.

A data governance framework establishes policies, processes, and roles for effective data management. It includes defining data ownership, ensuring quality, and enforcing security. With clear guidelines, organizations can enhance data transparency, compliance, and decision-making, fostering a culture of accountability and trust in data practices.

Having a mature Data Governance Framework is a significant move towards achieving Privacy without compromising Efficiency. The DG Framework operates throughout the lifecycle of data entities, mirroring the business value chain and underlying processes. It encompasses Privacy and all related elements (interactions, processes, tasks) that might affect it. Understanding these details accelerates privacy considerations and guarantees nothing is overlooked, especially when coupled with agile change management and impact assessment.

Ter uma estrutura de governan?a de dados eficiente é essencial para garantir uma boa governan?a na privacidade e prote??o de dados. A governan?a de dados dá seguran?a sobre todo o ciclo de vida desses dados, identifica??o de processos e áreas que utilizam, pontos de coletas, armazenamento e descarte. Ela mitiga riscos de pontos cegos sobre os dados da companhia. A estrutura de governan?a de dados e de privacidade andam juntas e a primeira dá suporte para o bom funcionamento da segunda.

Without an mature data catalog, all the data must be treated as private and sensitive, and protected accordingly. This inefficient, it increases the cost of management and limits business benefits and access to data insights to a few people in the organization. Cataloging the data is a fundamental step towards democratizing data and insights.

Indeed, data cataloging and classification are important parts of the data governance. For large organizations that hold hundreds or even thousands of corporate apps this might be quite challenging , so a good start would be to have an inventory of all corporate apps (service/products catalog or portfolio in ITSM), add respective attributes to classify data in it, set up right change management process to keep it up to date. Then it might be helpful to prioritize your data cataloging activities, start with apps that hold sensitive information, use specialized toolsets and automation to search for dangerous patterns. This is where AI might be a real game changer!

Data classification and cataloging are vital for effective data management. Classification involves labeling data based on sensitivity, ensuring appropriate handling. Cataloging employs metadata to organize and index data, aiding in discovery and understanding. Together, they enhance security, compliance, and accessibility, providing a structured approach to manage and derive value from organizational data.

Data classification and cataloging are foundational steps in data governance. Classification involves labeling data based on sensitivity levels like public, internal, confidential, or restricted. This aids in implementing suitable security measures. Cataloging creates a metadata repository detailing data sources, attributes, relationships, lineage, and quality. This process facilitates efficient data tracking and control, ensuring sensitive data is accurately managed. Both classification and cataloging are essential for enabling secure data discovery and analysis, playing a crucial role in the effective governance and protection of organizational data assets.

There are several ways to catalog and classify sensitive data. Using tools that can verify certain patterns is something that can get a lot done with little effort. Having periods along the year to review and validate the classifications is also something that should improve accuracy. Above all that, it has to be common knowledge along the company that sensitive data should be only accessed by specific users so training employees is a great way to have a second line of defense.

This requires detail: > Your earlier steps (proper locating and classification of your data) will enable you to transform data for specific processing purposes. > The exact transformation technique (e.g. masking, hashing, or more hardcore Privacy Enhancing Technology like synthetics) should enable you subsequently to secure for purpose. NIST has great guidelines for this. > Sharing your data for specific purposes -both internally among team or externally- can be done through a variety of techniques. For example, you can use data clean rooms, prepare specific views on data with dedicated retention periods, or use technology such as multi-party computation. >> Ensure that your data governance program is linked to actual data delivery!

This is the most important step in devising protective measures: hope for the best but plan for the worst. You also need to make sure that there are no crumbs left in the process, i.e., you don't leave attributes behind in your feed that can be linked to other related data feeds in case these protective layers are not implemented properly.

Data anonymization and encryption are key to privacy without compromising efficiency: - Anonymization: Removes or alters personal identifiers in data. Techniques like data masking, pseudonymization, or aggregation ensure privacy while maintaining data utility for analysis. - Encryption: Secures data in transit and at rest. Advanced Encryption Standard (AES) and homomorphic encryption allow data processing without decryption, balancing security with efficiency. Implementing these with efficient algorithms and hardware acceleration ensures data remains useful and accessible, while safeguarding privacy

One strategy that can be employed is virtualizing data with two distinct access permissions: one for open data and another for anonymized and encrypted data. This approach facilitates access management to sensitive data without compromising data democratization.

In data governance, anonymization and encryption are critical for protecting sensitive data. Anonymization removes or alters identifiable information, safeguarding individual or entity privacy. Encryption transforms data into a secure code, accessible only to authorized parties with decryption keys. Both processes are essential for preventing unauthorized data access, disclosure, or misuse, and are fundamental for complying with privacy regulations. Implementing these methods enhances data security, maintaining the confidentiality and integrity of sensitive information, a cornerstone of responsible data governance.

Data quality related to sensitive information begins with a clear understanding of characteristics and details associated with that data. Based on the initial analysis of data, one should be able to identify the PII and draw conclusions to the purpose and potential use of that information. If the data are considered primary to the work associated with it, then it may need to stay in its original state. However, if the data is secondary, it can be recoded and using a variety of methods to maintain the integrity and security of the record. When recoding, it is essential to maintain clear records, and process to be able to link the updated data to the original data. This approach will help maintain the integrity of the information.

Ensuring data quality and validation is crucial in data governance. Data quality involves applying specific rules, standards, and metrics to assess and enhance your data's accuracy, completeness, and reliability. Validation checks ensure data meets predefined criteria and formats. These practices help prevent errors, inconsistencies, and anomalies in sensitive data, thereby improving its usability and value. Effective data quality and validation processes are key to maintaining high standards of data integrity, which in turn supports reliable analytics and decision-making within an organization.

I have found prioritizing data quality and validation to be helpful in data governance. Organizations can ensure the accuracy, completeness, and reliability of their sensitive data by applying rules, standards, and metrics. Validation processes can further verify that data meets expected criteria and formats. These practices help prevent errors, inconsistencies, and anomalies, ultimately enhancing data usability and value.

Identification and classification of PII is the inception steps which needs to be bundled up with a strong documentation process which is easy to understand by all levels of employees at an organisation. Once PII is recognised the Privacy Impact assessment based on regional legal requirements should be completed and finally the data sharing assessment with both the legal and regulatory bodies should be undertaken. Above two steps will ensure trust in sensitive data and enable data managers to assess the degree of encryption or acccess controls tech needs to deliver

Data quality of PII is equally, or even more important than regular non-PII data. Accuracy, timeliness, validity and consistency are crucial dimensions which must be monitored in order to keep a good "health" of sensitive data. Ofcourse, there are some limitations and controls which must be considered, such as "visibility" of non-anonymized data to tool which is performing profiling, and to person who is performing this kind of DQ checks.

Managing sensitive data requires ensuring privacy without sacrificing efficiency by implementing robust data access and sharing controls. Establishing strict protocols and access permissions ensures that only authorized personnel have the necessary privileges to handle sensitive information. By carefully managing who can access and share data, organizations can maintain privacy while allowing efficient collaboration among authorized users.

A proper data governance tool allows you to access, share, restrict and modify data governance rules via role-based permissioning. Even better, some tools allow seamless integration to control data flows based on these permissions and contracts.

Striking the balance between data privacy and efficiency in access and sharing hinges on prioritizing user consent and minimizing data access. Data should be collected and used only with explicit, informed consent, limited to the specific purpose for which it was obtained. Implement granular access controls, granting access only to authorized personnel on a need-to-know basis. Leverage pseudonymization and anonymization techniques where possible, and store sensitive data securely using encryption at rest and in transit. Efficiency shouldn't come at the cost of individual privacy. By prioritizing consent and minimizing access, you can ensure sensitive data is both protected and utilized responsibly.

In my experience, managing data access and sharing is crucial for enabling effective collaboration and informed decision-making. Data access involves controlling permissions based on user roles and context to ensure that sensitive information remains safe. At the same time, data sharing involves securely transferring data internally or externally while complying with standards. By navigating these processes thoughtfully, organizations can leverage their sensitive data for innovation and insights, all while upholding privacy and respecting the rights of data subjects.

When handling private information, balance privacy and efficiency. Control data access, allowing only authorized personnel. Implement encryption to secure data in transit and at rest. Use anonymization techniques when sharing insights. Regularly audit and update security measures. This way, you maintain privacy without compromising operational efficiency in managing sensitive data.

In managing sensitive data, ensuring privacy without sacrificing efficiency can be achieved through the use of data governance tools. These tools provide a structured framework for managing and controlling data throughout its lifecycle. Implementing data governance tools allows organizations to set policies, monitor compliance, and automate processes, ensuring that privacy measures are integrated seamlessly into data management practices, minimizing the risk of breaches and optimizing efficiency.

Tools like Collibra come with mature privacy features to safeguard your sensitive data while being efficient in managing huge amounts of data

Employ end-to-end encryption protocols to secure data both in transit and at rest. Establish a granular access control system, utilizing role-based permissions, ensuring that only authorized personnel can access specific data sets. Conduct regular security audits to identify vulnerabilities and promptly address any issues.

For those working in the field of data privacy disciplines, it's important to note that this subject doesn't operate independently. In a project, privacy policies have a broad synergy with more technical aspects related to cybersecurity and other pillars of data governance and quality. The most effective way to mitigate bureaucracy is, first and foremost, to recognize that business interests align with data protection, creating a win-win relationship.

Sharing a snapshot of my experience with the Data Governance platform ecosystem: 1. Complete end-to-end DG platforms: Few options exist, with Collibra standing out. 2. Pure play DG platforms: OnTrust and Data360 Govern. 3. Master Data with DG: Explored Ataccama and SAP MDG 4. Lineage and Cataloging platforms: Atlan, PurView, Unity Catalog, Manta and Octopai. 5. ETL platforms with DG extension: Talend, Informatica and Apache Atlas.

If you use data contracts and tools that can understand the security as they define it (like Raito), you do not have to sacrifice efficiency at all: you will have the same performance with higher security.

Creating value from data and security are not opposites - while often they are perceived to be. This is an expensive fallacy (either because value is left on the table or too much risk is taken). For instance, if you leverage data contracts with access and governance definitions included, you can define data policies in very granular ways without sacrificing utility and velocity (like PACE).

See comment Jean-Georges Perrin ↑↑↑ .

When managing sensitive data, additional considerations to ensure privacy without sacrificing efficiency include regular audits and assessments of data handling practices, employee training on privacy protocols, and staying abreast of evolving data privacy regulations. Continuous monitoring, encryption technologies, and a culture of privacy awareness contribute to a comprehensive approach that safeguards sensitive data while maintaining operational efficiency.

As a custodian of sensitive data, I prioritize privacy without compromising efficiency. Data collection is minimized, and encryption is applied in transit and at rest. Strict access controls ensure authorized usage. Anonymization and pseudonymization techniques protect personal privacy. Sensitive data fields are masked, and secure transfer protocols are utilized. Compliance with governance policies is a priority, backed by a robust breach response plan. Employees are trained on privacy practices, and regular audits bolster security. These measures ensure data integrity while preserving operational effectiveness.