How can data privacy tools help you label and classify data?
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Data privacy is a key aspect of data governance, as it ensures that sensitive and personal data is protected from unauthorized access, use, and disclosure. However, data privacy is not only a legal and ethical obligation, but also a business opportunity, as it can enhance customer trust, reputation, and value. To achieve data privacy, you need to label and classify your data according to its sensitivity, purpose, and retention. This can help you apply the right policies, controls, and measures to safeguard your data and comply with regulations. In this article, we will explore how data privacy tools can help you label and classify your data effectively and efficiently.
Data privacy tools are software applications or platforms that help you manage and protect your data in accordance with privacy laws and standards, such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), or the ISO 27001. Data privacy tools can help you perform various tasks, such as data discovery, data mapping, data inventory, data masking, data encryption, data anonymization, data deletion, data breach notification, and data subject access requests. Data privacy tools can also help you monitor and audit your data activities, generate reports and dashboards, and demonstrate compliance.
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Alberto Vicente
Senior Director of Data & Analytics Strategist & Business Development | Data Architecture | Data Governance | Data Engineering | MBA Candidate
Data privacy tools offer essential features for labeling and classifying data effectively. They utilize automated classification, data discovery, tagging, and metadata capabilities to categorize and protect data. These tools enable policy enforcement, data masking, and anonymization, while also providing reporting and auditing functions. Integration with data loss prevention solutions enhances data protection, ensuring compliance with data privacy regulations and safeguarding sensitive information throughout an organization.
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Arun Somasundaram Sp
Data Governance | Data Management | Data Engineering | Data Visualization
I have earlier worked in Data privacy and Protection projects for various sectors like Insurance, Retail and Pharma from various geography. The tools I worked to achieve the data privacy and protection includes BigID, Microsoft Compliance manager/ Microsoft Purview, Informatica DPM, Onetrust and Immuta. Sensitivity labeling and classification depends on various parameters like Location of the data storage, Geography of the customer/organization data, file type/storage type. Further the classification is done based on reg-ex, NLP and pattern matching based on the requirement.
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Mir Ayaz Kanth
Information Security Troubleshooter
Data privacy tools can play a significant role in helping you label and classify data while maintaining the privacy and security of sensitive information. These tools can facilitate the labeling and classification of data while protecting sensitive information through various privacy-enhancing techniques like Data Masking, Data categorization, Data Retention and Data Minimization including other techniques those will go in parallel like Access Control, Encryption, Audit & Monitoring and Compliance.
Labeling and classifying data is the process of identifying and categorizing your data based on its characteristics, such as its type, source, owner, content, context, and risk. This can bring a number of benefits, including improved data quality and accuracy, enhanced security and privacy, streamlined governance and compliance, optimized storage and retention, and facilitated analysis and reporting. For example, reducing duplication and errors can improve data quality; applying appropriate access rights and encryption methods can boost security; defining roles, responsibilities, and rules for data handling can facilitate compliance; allocating resources for storage and deleting obsolete or unnecessary data can optimize retention; and enabling data filtering based on relevant criteria can make analysis easier.
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Joe Perez ("Dr. Joe")
✔6X LinkedIn Top Voice ✔International Keynote Speaker ✔Published Author⠀✔Senior Systems Analyst ✔Data Guru⠀ ⠀ ⠀⠀⠀ ✔2021 Thought Leader of the Year ✔CTO (fractional)
In the realm of data privacy, labeling and classifying data is your first line of defense. It's not just a legal obligation; it's a business opportunity waiting to be harnessed. By categorizing data based on its characteristics, such as type, source, and sensitivity, you pave the way for enhanced data quality, security, governance, compliance, and efficiency. Think of it as unlocking the full potential of your data while fortifying your data fortress.
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Bart Vandekerckhove
Co-founder @ Raito | multicloud data security
Labeling data is also a great way to help you with data loss prevention (DLP) and data security. The InfoSec Team can use the labels to manage access to data using attribute based access controls (ABAC), or set up DLP rules in communication systems such as email to prevent accidental data loss of confidential information. As this concerns security decisions it will be important to have high quality labels.
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Renjith K P
TOGAF, CDMC V1 Certified, LSS Black Belt, Alteryx Designer Core Certified, Neo4j Certified Professional, Certified Lean Project Manager
Labeling and classifying data is like organizing ingredients in a professional kitchen. Chefs (data analysts) need to quickly find the right ingredients (data) to create a dish (business insight). By properly labeling and storing these ingredients, chefs can efficiently prepare meals, ensuring dishes meet customer expectations (business objectives). Similarly, secure storage of delicate ingredients (data encryption) prevents spoilage or theft (data breaches), while discarding expired items (obsolete data) keeps the kitchen (data system) efficient and compliant with health standards (data regulations).
Data privacy tools can be used to label and classify data in various ways, based on their features and functionalities. For example, they can scan and discover data sources and repositories across your organization, mapping and documenting the data flows and relationships. They can also inventory and catalog your data assets and metadata, tagging and labeling them with predefined or custom attributes. Additionally, data privacy tools can classify and group data into different levels or categories, as well as assign and enforce policies and controls to your data based on its labels and classifications.
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Abdulaziz Alarfaj, CISSP, CISM, CRISC, PMP
Information Security GRC Manager @ TAHAKOM | Cybersecurity | GRC | Security Architecture | Data Privacy & Protection
Such tools help in classifying and labeling by applying automated policies to identify sensitive information within datasets. These tools can recognize patterns, keywords, or data formats that indicate privacy-related content, allowing organizations to categorize and label data accordingly. This labeling helps enforce data protection and compliance measures, ensuring sensitive data is handled appropriately
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Muath AlHomoud
Director of Cybersecurity | Keynote Speaker | CISO Awards | Board Member | FinTech | vCISO | Enterprise Architect | Digital Transformation
My experience, I believe the Cybersecurity capability i.e. Data Loss Preventation with Data Classification tool will play big part in Data Privacy for "Record of Processing Activities" AKA RoPA.
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Lindsay Pettai, CKM, CSM, CSPO
| Certified Project Manager | Certified Knowledge Manager | Certified ScrumMaster® | Taxonomist | Ontologist | #OpenToWork
In a healthcare organization's I've seen the implementation of data privacy tools like Symantec Data Loss Prevention (DLP). This tool scans incoming and outgoing data, including emails and documents, to identify sensitive patient information, such as medical records. It uses predefined policies and machine learning algorithms to classify this data as "Sensitive Healthcare Data."The tool then applies labels and metadata to these documents, indicating their classification. For example, a medical record might be tagged with metadata such as "Patient Name," "Diagnosis," and "Date of Birth." Additionally, it categorizes the data based on predefined taxonomy, like "Medical Records," "Billing Information," or "Prescriptions."
There are many data privacy tools available in the market, each with its own strengths and weaknesses. Popular and reputable tools include OneTrust, a comprehensive and integrated platform that covers data discovery, data mapping, data inventory, data classification, data subject rights management, data breach response, data protection impact assessment, and privacy compliance management. BigID is a specialized and innovative platform that focuses on data discovery, data mapping, data inventory, data classification, data minimization, data anonymization, data deletion, and data subject access requests. Spirion is a dedicated and powerful platform that concentrates on data discovery, data classification, data labeling, data encryption, data masking, data deletion, and data breach prevention. Additionally, Securiti is a versatile and modular platform that offers features such as data discovery, data mapping, inventorying of information, classifying it according to sensitivity levels, anonymizing or masking it to protect privacy rights of individuals or organizations involved in the process. It also provides for the management of subject rights in accordance with applicable laws and regulations.
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Peggy Tsai
Chief Data Officer at BigID | Global Top 100 Innovator in Data & Analytics | Adjunct Faculty at Carnegie Mellon | Podcast co-host of Data Transformers | Co-author of The AI Book
Working for BigID, it is one of the leading data privacy tools in the market. While there are many solutions available, I would consider the scalability of any solution to ensure it fits into your enterprise architecture. In addition, reusability of the tool is important. Many organizations purchase a niche product but cataloguing, classification and discovery tools can be leveraged for other use cases in data governance and security. Privacy teams work inside governance teams so it is important if tools can fit multiple purposes for an organization.
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Diego Carvallo
Data | Enterprise Architecture | Strategy -- I help companies leverage their data assets to drive business growth
There's several great tools out there including Collibra and Data.World that support data privacy - however none of the tools matter if the organization does not use them effectively. Having a robust data governance and enablement organization is the greatest tool to support data privacy.
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Andre Quintanilha
Co-founder & Chief Compliance Officer @ Palqee [we're hiring!!]
One of the leading and most comprehensive data privacy tools available is Palqee. It offers a customizable birds eye view dashboard with key metrics and insights that every GRC team needs to manage privacy efficiently both locally and globally. The Palqee enterprise-grade suite adapts to any business requirements for workflow management, subject requests fulfilment, data mapping, third party risk management, tasks management, automated audits, and many other areas, creating a collaborative environment for internal teams to engage. The platform is updated on a bi-weekly basis, giving users always the latest frameworks available.
Choosing the right data privacy tool for your needs involves considering several factors such as your business goals, data environment, privacy requirements, budget, and preferences. Before selecting a data privacy tool, ask yourself what the main objectives and challenges of your data privacy program are, what types, volumes, and sources of data you have, and what privacy laws and standards you need to comply with. Additionally, consider the features and functionalities that you need from a data privacy tool, the costs and benefits of different tools, and the reviews and ratings of different tools. After answering these questions, you can narrow down your options and compare the pros and cons of different tools. You can also request a demo or trial to test out the tools in your own data environment. Ultimately, choose the tool that best suits your needs and expectations.
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Umberto Annino
Information Technology Specialist and Evangelist
for me one important aspect is to find a tool that can handle the data and formats a organisation uses and processes. Eventually, a data inventory is only useful the more comprehensive it is, so any gaps should be closed asap.
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Tejasvi Addagada
Data Leader | Privacy Officer| Best selling Author | Global Keynote Speaker & Thought Leader
Data privacy tools are an important asset in an enterprise toolset. Some of the capabilities required to manage data privacy can be 1. classifying data automatically by scanning through metadata & data 2. Curating entitlements and translating them into security controls like masking, anonymization etc 3. Managing consents & preferences from customers 4. Providing intelligence around privacy threats in the consumption zones 5. Location-based intelligent tagging of personal data basis local regulations 6. Assessing data for completeness, consistency, accuracy More about this in my latest book https://datariskmanagement.org/
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Jason Wilson
🧙🏻 Privacy Warlock | Former Microsoft, Twilio, DoD
It’s important not to conflate privacy tools with data governance tools. Both can be used together for enhanced coverage, but each serve different purposes.
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Tahir Latif, FIP
Global Practice Lead - Data Privacy & Responsible AI | Fractional Chief Privacy & Trust Officer | Board Advisor | Global Keynote Speaker | Identifying and mitigating risks for multinational companies
Data privacy tools offer innovative ways to manage dataset labelling and classification, going beyond just compliance. Privacy software can tag sensitive personal data to segment it for stronger access controls and permissible use cases. Automated scanning helps accurately identify health, financial, or confidential data at scale. User interfaces can mark data with meaningful classification schemas, like categories for sales, marketing, R&D. Tracking data lineage across systems provides visibility into flows and transformation points. Ultimately, integrated and automated data privacy tools empower more nuanced, ethical data management beyond binary public/private designation.
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Hemant Gupta
Data Practitioner with specialization in Data Management |Data Architecture| Data Governance| CDMP,CSM
Data privacy platform are gaining more maturity these days to auto classify and tag PII data or any other sensitive data if you apply right classifiers. Two key components: 1. Auto- Tagging PII Data 2. Ability to manually curate the tagged data
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Arpit Mudgal
Data Governance | Data Privacy | Data Quality | Metadata Management
Data classification is one of the most talked-about features in discussions about data privacy, and several vendors provide this capability with different merits and demerits. One of the most critical features to consider when selecting a tool is its self-learning capability. If the tool can learn from manually curated and approved data and incorporate this knowledge into the next set of runs, it could significantly save organizations time. Such an advancement would undoubtedly be a game-changer for both vendors and users.