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Sensitive Data Classification Challenges Security Teams Face

March 27, 2024

Min Read

Data Security

Yair Cohen

Co-Founder and VP Product

Ensuring the security of your data involves more than just pinpointing its location. It's a multifaceted process in which knowing where your data resides is just the initial step. Beyond that, accurate classification plays a pivotal role. Picture it like assembling a puzzle – having all the pieces and knowing their locations is essential, but the real mastery comes from classifying them (knowing which belong to the edge, which make up the sky in the picture, and so on…), seamlessly creating the complete picture for your proper data security and privacy programs.

Just last year, the global average cost of a data breach surged to USD 4.45 million, a 15% increase over the previous three years. This highlights the critical need to automatically discover and accurately classify personal and unique identifiers, which can transform into sensitive information when combined with other data points.

This unique capability is what sets Sentra’s approach apart— enabling the detection and proper classification of data that many solutions overlook or mis-classify.

What Is Data Classification and Why Is It Important?

Data classification is the process of organizing and labeling data based on its sensitivity and importance. This involves assigning categories like "confidential," "internal," or "public" to different types of data. It’s further helpful to understand the ‘context’ of data - it’s purpose - such as legal agreements, health information, financial record, source code/IP, etc. With data context you can more precisely understand the data’s sensitivity and accurately classify it (to apply proper policies and related violation alerting, eliminating false positives as well).

Here's why data classification is crucial in the cloud:

Enhanced Security: By understanding the sensitivity of your data, you can implement appropriate security measures. Highly confidential data might require encryption or stricter access controls compared to publicly accessible information.
Improved Compliance: Many data privacy regulations require organizations to classify personally identifying data to ensure its proper handling and protection. Classification helps you comply with regulations like GDPR or HIPAA.
Reduced Risk of Breaches: Data breaches often stem from targeted attacks on specific types of information. Classification helps identify your most valuable data assets, so you can apply proper controls and minimize the impact of a potential breach.
Efficient Management: Knowing what data you have and where it resides allows for better organization and management within the cloud environment. This can streamline processes and optimize storage costs.

Data classification acts as a foundation for effective data security. It helps prioritize your security efforts, ensures compliance, and ultimately protects your valuable data.

Securing your data and mitigating privacy risks begins with a data classification solution that prioritizes privacy and security. Addressing various challenges necessitates a deeper understanding of the data, as many issues require additional context. The end goal is automating processes and making findings actionable - which requires granular, detailed context regarding the data’s usage and purpose, to create confidence in the classification result.

In this article, we will define toxic combinations and explore specific capabilities required from a data classification solution to tackle related data security, compliance, and privacy challenges effectively.

Data Classification Challenges

Challenge 1: Unstructured Data Classification

Unstructured data is information that lacks a predefined format or organization, making it challenging to analyze and extract insights, yet it holds significant value for organizations seeking to leverage diverse data sources for informed decision-making. Examples of unstructured data include customer support chat logs, educational videos, and product photos. Detecting data classes within unstructured data with high accuracy poses a significant challenge, particularly when relying solely on simplistic methods like regular expressions and pattern matching. Unstructured data, by its very nature, lacks a predefined and organized format, making it challenging for conventional classification approaches. Legacy solutions often grapple with the difficulty of accurately discerning data classes, leading to an abundance of false positives and noise.

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This highlights the need for more advanced and nuanced techniques in unstructured data classification to enhance accuracy and reduce its inherent complexities. Addressing this challenge requires leveraging sophisticated algorithms and machine learning models capable of understanding the intricate patterns and relationships within unstructured data, thereby improving the precision of data class detection.

In the search for accurate data classification within unstructured data, incorporating technologies that harness machine learning and artificial intelligence is critical. These advanced technologies possess the capability to comprehend the intricacies of context and natural language, thereby significantly enhancing the accuracy of sensitive information identification and classification.

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For example, detecting a residential address is challenging because it can appear in multiple shapes and forms, and even a phone number or a GPS coordinate can be easily confused with other numbers without fully understanding the context. However, LLMs can use text-based classification techniques (NLP, keyword matching, etc.) to accurately classify this type of unstructured data. Furthermore, understanding the context surrounding each data asset, whether it be a table or a file, becomes paramount. Whether it pertains to a legal agreement, employee contract, e-commerce transaction, intellectual property, or tax documents, discerning the context aids in determining the nature of the data and guides the implementation of appropriate security measures. This approach not only refines the accuracy of data class detection but also ensures that the sensitivity of the unstructured data is appropriately acknowledged and safeguarded in line with its contextual significance.

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Optimal solutions employ machine learning and AI technology that really understand the context and natural language in order to classify and identify sensitive information accurately. Advancements in technologies have expanded beyond text-based classification to image-based classification and audio/speech-based classification, enabling companies and individuals to efficiently and accurately classify sensitive data at scale.

Challenge 2: Customer Data vs Employee Data

Employee data and customer data are the most common data categories stored by companies in the cloud. Identifying customer and employee data is extremely important. For instance, customer data that also contains Personal Identifiable Information (PII) must be stored in compliant production environments and must not travel to lower environments such as data analytics or development.

What is customer data?

Customer data is all the data that we store and collect from our customers and users.

B2C - Customer data in B2C companies, includes a lot of PII about their end users, all the information they transact with our service.
B2B - Customer data in B2B companies includes all the information of the organization itself, such as financial information, technological information, etc., depending on the organization.

This could be very sensitive information about each organization that must remain confidential or otherwise can lead to data breaches, intellectual property theft, reputation damage, etc.

What is employee data?

Employee data includes all the information and knowledge that the employees themselves produce and consume. This could include many types of different information, depending on what team it comes from. For instance:-tech and intellectual property, source code from the engineering team-HR information, from the HR team-legal information from the legal team, source code, and many more.It is crucial to properly classify employee and customer data, and which data falls under which category, as they must be secured differently. A good data classification solution needs to understand and differentiate the different types of data. Access to customer data should be restricted, while access to employee data depends on the organizational structure of the user’s department. This is important to enforce in every organization.

Challenge 3: Understanding Toxic Combinations

What Is a Toxic Combination?

A toxic combination occurs when seemingly innocuous data classes are combined to increase the sensitivity of the information. On their own, these pieces of information are harmless, but when put together, they become “toxic”.

The focus here extends beyond individual data pieces; it's about understanding the heightened sensitivity that emerges when these pieces come together. In essence, securing your data is not just about individual elements but understanding how these combinations create new vulnerabilities.

We can divide data findings into three main categories:

Personal Identifiers: Piece of information that can identify a single person - for example, an email address or social security number (SSN), belongs only to one person.
Personal Quasi Identifiers: A quasi identifier is a piece of information that by itself is not enough to identify just one person. For example, a zip code, address, an age, etc. Let’s say Bob - there are many Bobs in the world, but if we also have Bob’s address - there is most likely just one Bob living in this address.
Sensitive Information: Each piece of information that should remain sensitive/private. Such as medical diseases, history, prescriptions, lab results, etc. automotive industry - GPS location. Sensitive data on its own is not sensitive, but the combination of identifiers with sensitive information is very sensitive.

Finding personal identifiers by themselves, such as an email address, does not necessarily mean that the data is highly sensitive. Same with sensitive data such as medical info or financial transactions, that may not be sensitive if they can not be associated with individuals or other identifiable entities.

However, the combination of these different information types, such as personal identifiers and sensitive data together, does mean that the data requires multiple data security and protection controls and therefore it’s crucial that the classification solution will understand that.

Detecting ‘Toxic Data Combinations’ With a Composite Class Identifier

Sentra has introduced a new ‘Composite’ data class identifier to allow customers to easily build bespoke ‘toxic combinations’ classifiers they wish for Sentra to deploy to identify within their data sets.

Importance of Finding Toxic Combinations

This capability is critical because having sensitive information about individuals can harm the business reputation, or cause them fines, privacy violations, and more.

Under certain data privacy and protection requirements, this is even more crucial to discover and be aware of. For example, HIPAA requires protection of patient healthcare data. So, if an individual’s email is combined with his address, and his medical history (which is now associated with his email and address), this combination of information becomes sensitive data.

‍Challenge 4: Detecting Uncommon Personal Identifiers for Privacy Regulations

‍There are many different compliance regulations, such as Privacy and Data Protection Acts, which require organizations to secure and protect all personally identifiable information. With sensitive cloud data constantly in flux, there are many unknown data risks arising. This is due to a lack of visibility and an inaccurate data classification solution.Classification solutions must be able to detect uncommon or proprietary personal identifiers. For example, a product serial number that belongs to a specific individual, U.S. Vehicle Identification Number (VIN) might belong to a specific car owner, or GPS location that indicates an individual home address can be used to identify this person in other data sets.

These examples highlight the diverse nature of identifiable information. This diversity requires classification solutions to be versatile and capable of recognizing a wide range of personal identifiers beyond the typical ones.

Organizations are urged to implement classification solutions that both comply with general privacy and data protection regulations and also possess the sophistication to identify and protect against a broad spectrum of personal identifiers, including those that are unconventional or proprietary in nature. This ensures a comprehensive approach to safeguarding sensitive information in accordance with legal and privacy requirements.

Challenge 5: Adhering to Data Localization Requirements

‍Data Localization refers to the practice of storing and processing data within a specific geographic region or jurisdiction. It involves restricting the movement and access to data based on geographic boundaries, and can be motivated by a variety of factors, such as regulatory requirements, data privacy concerns, and national security considerations.In adherence to the Data Localization requirements, it becomes imperative for classification solutions to understand the specific jurisdictions associated with each of the data subjects that are found in Personal Identifiable Information (PII) they belong to.For example, if we find a document with PII, we need to know if this PII belongs to Indian residents, California residents or German citizens, to name a few. This will then dictate, for example, in which geography this data must be stored and allow the solution to indicate any violations of data privacy and data protection frameworks, such as GDPR, CCPA or DPDPA.

Below is an example of Sentra’s Monthly Data Security Report: GDPR

Why Data Localization Is Critical

Adhering to local laws and regulations: Ensure data storage and processing within specific jurisdictions is a crucial aspect for organizations. For instance, certain countries mandate the storage and processing of specific data types, such as personal or financial data, within their borders, compelling organizations to meet these requirements and avoid potential fines or penalties.

Protecting data privacy and security: By storing and processing data within a specific jurisdiction, organizations can have more control over who has access to the data, and can take steps to protect it from unauthorized access or breaches. This approach allows organizations to exert greater control over data access, enabling them to implement measures that safeguard it from unauthorized access or potential breaches.
‍Supporting national security and sovereignty: Some countries may want to store and process data within their borders. This decision is driven by the desire to have more control over their own data and protect their citizens' information from foreign governments or entities, emphasizing the role of data localization in supporting these strategic objectives.

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Conclusion: Sentra’s Data Classification Solution

Sentra provides the granular classification capabilities to discern and accurately classify the formerly difficult to classify data types just mentioned. Through a variety of analysis methods, we address those data types and obscure combinations that are crucial to effective data security. These combinations too often lead to false positives and disappointment in traditional classification systems.

In review, Sentra’s data classification solution accurately:

Classifies Unstructured data by applying advanced AI/ML analysis techniques
Discerns Employee from Customer data by analyzing rich business context
Identifies Toxic Combinations of sensitive data via advanced data correlation techniques
Detects Uncommon Personal Identifiers to comply with stringent privacy regulations
Understands PII Jurisdiction to properly map to applicable sovereignty requirements

To learn more, visit Sentra’s data classification use case page or schedule a demo with one of our experts.

Yair Cohen

Co-Founder and VP Product

Yair brings a wealth of experience in cybersecurity and data product management. In his previous role, Yair led product management at Microsoft and Datadog. With a background as a member of the IDF's Unit 8200 for five years, he possesses over 18 years of expertise in enterprise software, security, data, and cloud computing. Yair has held senior product management positions at Datadog, Digital Asset, and Microsoft Azure Protection.

Latest Blog Posts

Team Sentra

December 26, 2024

Min Read

Data Security

Create an Effective RFP for a Data Security Platform & DSPM

This RFP Guide is designed to help organizations create their own RFP for selection of Cloud-native Data Security Platform (DSP) & Data Security Posture Management (DSPM) solutions. The purpose is to identify key essential requirements that will enable effective discovery, classification, and protection of sensitive data across complex environments, including in public cloud infrastructures and in on-premises environments.

Instructions for Vendors

Each section provides essential and recommended requirements to achieve a best practice capability. These have been accumulated over dozens of customer implementations. Customers may also wish to include their own unique requirements specific to their industry or data environment.

1. Data Discovery & Classification

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Requirement	Details
Shadow Data Detection	Can the solution discover and identify shadow data across any data environment (IaaS, PaaS, SaaS, OnPrem)?
Sensitive Data Classification	Can the solution accurately classify sensitive data, including PII, financial data, and healthcare data?
Efficient Scanning	Does the solution support smart sampling of large file shares and data lakes to reduce and optimize the cost of scanning, yet provide full scan coverage in less time and lower cloud compute costs?
AI-based Classification	Does the solution leverage AI/ML to classify data in unstructured documents and stores (Google Drive, OneDrive, SharePoint, etc) and achieve more than 95% accuracy?
Data Context	Can the solution discern and ‘learn’ the business purpose (employee data, customer data, identifiable data subjects, legal data, synthetic data, etc.) of data elements and tag them accordingly?
Data Store Compatibility	Which data stores (e.g., AWS S3, Google Cloud Storage, Azure SQL, Snowflake data warehouse, On Premises file shares, etc.) does the solution support for discovery?
Autonomous Discovery	Can the solution discover sensitive data automatically and continuously, ensuring up to date awareness of data presence?
Data Perimeters Monitoring	Can the solution track data movement between storage solutions and detect risky and non-compliant data transfers and data sprawl?

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2. Data Access Governance

Requirement	Details
Access Controls	Does the solution map access of users and non-human identities to data based on sensitivity and sensitive information types?
Location Independent Control	Does the solution help organizations apply least privilege access regardless of data location or movement?
Identity Activity Monitoring	Does the solution identify over-provisioned, unused or abandoned identities (users, keys, secrets) that create unnecessary exposures?
Data Access Catalog	Does the solution provide an intuitive map of identities, their access entitlements (read/write permissions), and the sensitive data they can access?
Integration with IAM Providers	Does the solution integrate with existing Identity and Access Management (IAM) systems?

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3. Posture, Risk Assessment & Threat Monitoring

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Requirement	Details
Risk Assessment	Can the solution assess data security risks and assign risk scores based on data exposure and data sensitivity?
Compliance Frameworks	Does the solution support compliance with regulatory requirements such as GDPR, CCPA, and HIPAA?
Similar Data Detection	Does the solution identify data that has been copied, moved, transformed or otherwise modified that may disguise its sensitivity or lessen its security posture?
Automated Alerts	Does the solution provide automated alerts for policy violations and potential data breaches?
Data Loss Prevention (DLP)	Does the solution include DLP features to prevent unauthorized data exfiltration?
3rd Party Data Loss Prevention (DLP)	Does the solution integrate with 3rd party DLP solutions?
User Behavior Monitoring	Does the solution track and analyze user behaviors to identify potential insider threats or malicious activity?
Anomaly Detection	Does the solution establish a baseline and use machine learning or AI to detect anomalies in data access or movement?

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4. Incident Response & Remediation

Requirement	Details
Incident Management	Can the solution provide detailed reports, alert details, and activity/change history logs for incident investigation?
Automated Response	Does the solution support automated incident response, such as blocking malicious users or stopping unauthorized data flows (via API integration to native cloud tools or other)?
Forensic Capabilities	Can the solution facilitate forensic investigation, such as data access trails and root cause analysis?
Integration with SIEM	Can the solution integrate with existing Security Information and Event Management (SIEM) or other analysis systems?

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5. Infrastructure & Deployment

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Requirement	Details
Deployment Models	Does the solution support flexible deployment models (on-premise, cloud, hybrid)? Is the solution agentless?
Cloud Native	Does the solution keep all data in the customer’s environment, performing classification via serverless functions? (ie. no data is ever removed from customer environment - only metadata)
Scalability	Can the solution scale to meet the demands of large enterprises with multi-petabyte data volumes?
Performance Impact	Does the solution work asynchronously without performance impact on the data production environment?
Multi-Cloud Support	Does the solution provide unified visibility and management across multiple cloud providers and hybrid environments?

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6. Operations & Support

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Requirement	Details
Onboarding	Does the solution vendor assist customers with onboarding? Does this include assistance with customization of policies, classifiers, or other settings?
24/7 Support	Does the vendor provide 24/7 support for addressing urgent security issues?
Training & Documentation	Does the vendor provide training and detailed documentation for implementation and operation?
Managed Services	Does the vendor (or its partners) offer managed services for organizations without dedicated security teams?
Integration with Security Tools	Can the solution integrate with existing security tools, such as firewalls, DLP systems, and endpoint protection systems?

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7. Pricing & Licensing

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Requirement	Details
Pricing Model	What is the pricing structure (e.g., per user, per GB, per endpoint)?
Licensing	What licensing options are available (e.g., subscription, perpetual)?
Additional Costs	Are there additional costs for support, maintenance, or feature upgrades?

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Conclusion

This RFP template is designed to facilitate a structured and efficient evaluation of DSP and DSPM solutions. Vendors are encouraged to provide comprehensive and transparent responses to ensure an accurate assessment of their solution’s capabilities.

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Sentra’s cloud-native design combines powerful Data Discovery and Classification, DSPM, DAG, and DDR capabilities into a complete Data Security Platform (DSP). With this, Sentra customers achieve enterprise-scale data protection and do so very efficiently - without creating undue burdens on the personnel who must manage it.

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To learn more about Sentra’s DSP, request a demo here and choose a time for a meeting with our data security experts. You can also choose to download the RFP as a pdf.

Gilad Golani

December 16, 2024

Min Read

Data Security

Best Practices: Automatically Tag and Label Sensitive Data

The Importance of Data Labeling and Tagging

In today's fast-paced business environment, data rarely stays in one place. It moves across devices, applications, and services as individuals collaborate with internal teams and external partners. This mobility is essential for productivity but poses a challenge: how can you ensure your data remains secure and compliant with business and regulatory requirements when it's constantly on the move?

Why Labeling and Tagging Data Matters

Data labeling and tagging provide a critical solution to this challenge. By assigning sensitivity labels to your data, you can define its importance and security level within your organization. These labels act as identifiers that abstract the content itself, enabling you to manage and track the data type without directly exposing sensitive information. With the right labeling, organizations can also control access in real-time.

For example, labeling a document containing social security numbers or credit card information as Highly Confidential allows your organization to acknowledge the data's sensitivity and enforce appropriate protections, all without needing to access or expose the actual contents.

Why Sentra’s AI-Based Classification Is a Game-Changer

Sentra’s AI-based classification technology enhances data security by ensuring that the sensitivity labels are applied with exceptional accuracy. Leveraging advanced LLM models, Sentra enhances data classification with context-aware capabilities, such as:

Detecting the geographic residency of data subjects.
Differentiating between Customer Data and Employee Data.
Identifying and treating Synthetic or Mock Data differently from real sensitive data.

This context-based approach eliminates the inefficiencies of manual processes and seamlessly scales to meet the demands of modern, complex data environments. By integrating AI into the classification process, Sentra empowers teams to confidently and consistently protect their data—ensuring sensitive information remains secure, no matter where it resides or how it is accessed.

‍Benefits of Labeling and Tagging in Sentra

Sentra enhances your ability to classify and secure data by automatically applying sensitivity labels to data assets. By automating this process, Sentra removes the manual effort required from each team member—achieving accuracy that’s only possible through a deep understanding of what data is sensitive and its broader context.

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Here are some key benefits of labeling and tagging in Sentra:

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Enhanced Security and Loss Prevention: Sentra’s integration with Data Loss Prevention (DLP) solutions prevents the loss of sensitive and critical data by applying the right sensitivity labels. Sentra’s granular, contextual tags help to provide the detail necessary to action remediation automatically so that operations can scale.‍
Easily Build Your Tagging Rules: Sentra’s Intuitive Rule Builder allows you to automatically apply sensitivity labels to assets based on your pre-existing tagging rules and or define new ones via the builder UI (see screen below). Sentra imports discovered Microsoft Purview Information Protection (MPIP) labels to speed this process.

Labels Move with the Data: Sensitivity labels created in Sentra can be mapped to Microsoft Purview Information Protection (MPIP) labels and applied to various applications like SharePoint, OneDrive, Teams, Amazon S3, and Azure Blob Containers. Once applied, labels are stored as metadata and travel with the file or data wherever it goes, ensuring consistent protection across platforms and services.
Automatic Labeling: Sentra allows for the automatic application of sensitivity labels based on the data's content. Auto-tagging rules, configured for each sensitivity label, determine which label should be applied during scans for sensitive information.
Support for Structured and Unstructured Data: Sentra enables labeling for files stored in cloud environments such as Amazon S3 or EBS volumes and for database columns in structured data environments like Amazon RDS. By implementing these labeling practices, your organization can track, manage, and protect data with ease while maintaining compliance and safeguarding sensitive information. Whether collaborating across services or storing data in diverse cloud environments, Sentra ensures your labels and protection follow the data wherever it goes.

Applying Sensitivity Labels to Data Assets in Sentra

In today’s rapidly evolving data security landscape, ensuring that your data is properly classified and protected is crucial. One effective way to achieve this is by applying sensitivity labels to your data assets. Sensitivity labels help ensure that data is handled according to its level of sensitivity, reducing the risk of accidental exposure and enabling compliance with data protection regulations.

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Below, we’ll walk you through the necessary steps to automatically apply sensitivity labels to your data assets in Sentra. By following these steps, you can enhance your data governance, improve data security, and maintain clear visibility over your organization's sensitive information.

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The process involves three key actions:

Create Sensitivity Labels: The first step in applying sensitivity labels is creating them within Sentra. These labels allow you to categorize data assets according to various rules and classifications. Once set up, these labels will automatically apply to data assets based on predefined criteria, such as the types of classifications detected within the data. Sensitivity labels help ensure that sensitive information is properly identified and protected.
Connect Accounts with Data Assets: The next step is to connect your accounts with the relevant data assets. This integration allows Sentra to automatically discover and continuously scan all your data assets, ensuring that no data goes unnoticed. As new data is created or modified, Sentra will promptly detect and categorize it, keeping your data classification up to date and reducing manual efforts.
Apply Classification Tags: Whenever a data asset is scanned, Sentra will automatically apply classification tags to it, such as data classes, data contexts, and sensitivity labels. These tags are visible in Sentra’s data catalog, giving you a comprehensive overview of your data’s classification status. By applying these tags consistently across all your data assets, you’ll have a clear, automated way to manage sensitive data, ensuring compliance and security.

By following these steps, you can streamline your data classification process, making it easier to protect your sensitive information, improve your data governance practices, and reduce the risk of data breaches.

Applying MPIP Labels

In order to apply Microsoft Purview Information Protection (MPIP) labels based on Sentra sensitivity labels, you are required to follow a few additional steps:

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Set up the Microsoft Purview integration - which will allow Sentra to import and sync MPIP sensitivity labels.
Create tagging rules - which will allow you to map Sentra sensitivity labels to MPIP sensitivity labels (for example “Very Confidential” in Sentra would be mapped to “ACME - Highly Confidential” in MPIP), and choose to which services this rule would apply (for example, Microsoft 365 and Amazon S3).

Using Sensitivity Labels in Microsoft DLP

Microsoft Purview DLP (as well as all other industry-leading DLP solutions) supports MPIP labels in its policies so admins can easily control and prevent data loss of sensitive data across multiple services and applications.For instance, a MPIP ‘highly confidential’ label may instruct Microsoft Purview DLP to restrict transfer of sensitive data outside a certain geography. Likewise, another similar label could instruct that confidential intellectual property (IP) is not allowed to be shared within Teams collaborative workspaces. Labels can be used to help control access to sensitive data as well. Organizations can set a rule with read permission only for specific tags. For example, only production IAM roles can access production files. Further, for use cases where data is stored in a single store, organizations can estimate the storage cost for each specific tag.

Build a Stronger Foundation with Accurate Data Classification

Effectively tagging sensitive data unlocks significant benefits for organizations, driving improvements across accuracy, efficiency, scalability, and risk management. With precise classification exceeding 95% accuracy and minimal false positives, organizations can confidently label both structured and unstructured data. Automated tagging rules reduce the reliance on manual effort, saving valuable time and resources. Granular, contextual tags enable confident and automated remediation, ensuring operations can scale seamlessly. Additionally, robust data tagging strengthens DLP and compliance strategies by fully leveraging Microsoft Purview’s capabilities. By streamlining these processes, organizations can consistently label and secure data across their entire estate, freeing resources to focus on strategic priorities and innovation.

Yair Cohen

December 4, 2024

Min Read

Data Security

PII Compliance Checklist: 2025 Requirements & Best Practices

What is PII Compliance?

In our contemporary digital landscape, where information flows seamlessly through the vast network of the internet, protecting sensitive data has become crucial. Personally Identifiable Information (PII), encompassing data that can be utilized to identify an individual, lies at the core of this concern. PII compliance stands as the vigilant guardian, the fortification that organizations adopt to ensure the secure handling and safeguarding of this invaluable asset.

In recent years, the frequency and sophistication of cyber threats have surged, making the need for robust protective measures more critical than ever. PII compliance is not merely a legal obligation; it is strategically essential for businesses seeking to instill trust, maintain integrity, and protect their customers and stakeholders from the perils of identity theft and data breaches.

Sensitive vs. Non-Sensitive PII Examples

Before delving into the intricacies of PII compliance, one must navigate the nuanced waters that distinguish sensitive from non-sensitive PII. The former comprises information of profound consequence – Social Security numbers, financial account details, and health records. Mishandling such data could have severe repercussions.

On the other hand, non-sensitive PII includes less critical information like names, addresses, and phone numbers. The ability to discern between these two categories is fundamental to tailoring protective measures effectively.

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Type	Examples
Sensitive PII	Social Security Numbers
	Financial Account Details (e.g., credit card info)
	Health Records
	Biometric Information (e.g., fingerprints)
	Personal Identification Numbers (PINs)
Non-Sensitive PII	Names
	Addresses
	Phone Numbers
	Email Addresses
	Usernames

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This table provides a clear visual distinction between sensitive and non-sensitive PII, illustrating the types of information that fall into each category.

The Need for Robust PII Compliance

The need for PII compliance is propelled by the escalating threats of data breaches and identity theft in the digital realm. Cybercriminals, armed with advanced techniques, continuously evolve their strategies, making it crucial for organizations to fortify their defenses. Implementing PII compliance, including robust Data Security Posture Management (DSPM), not only acts as a shield against potential risks but also serves as a foundation for building trust among customers, stakeholders, and regulatory bodies. DSPM reduces data breaches, providing a proactive approach to safeguarding sensitive information and bolstering the overall security posture of an organization.

PII Compliance Checklist

As we delve into the intricacies of safeguarding sensitive data through PII compliance, it becomes imperative to embrace a proactive and comprehensive approach. The PII Compliance Checklist serves as a navigational guide through the complex landscape of data protection, offering a meticulous roadmap for organizations to fortify their digital defenses.

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From the initial steps of discovering, identifying, classifying, and categorizing PII to the formulation of a compliance-based PII policy and the implementation of cutting-edge data security measures - this checklist encapsulates the essence of responsible data stewardship. Each item on the checklist acts as a strategic layer, collectively forming an impenetrable shield against the evolving threats of data breaches and identity theft.

1. Discover, Identify, Classify, and Categorize PII

The cornerstone of PII compliance lies in a thorough understanding of your data landscape. Conducting a comprehensive audit becomes the backbone of this process. The journey begins with a meticulous effort to discover the exact locations where PII resides within your organization's data repositories.

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Identifying the diverse types of information collected is equally important, as is the subsequent classification of data into sensitive and non-sensitive categories. Categorization, based on varying levels of confidentiality, forms the final layer, establishing a robust foundation for effective PII compliance.

2. Create a Compliance-Based PII Policy

In the intricate tapestry of data protection, the formulation of a compliance-based PII policy emerges as a linchpin. This policy serves as the guiding document, articulating the purpose behind the collection of PII, establishing the legal basis for processing, and delineating the measures implemented to safeguard this information.

The clarity and precision of this policy are paramount, ensuring that every employee is not only aware of its existence but also adheres to its principles. It becomes the ethical compass that steers the organization through the complexities of data governance.

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public class PiiPolicy {
    private String purpose;
    private String legalBasis;
    private String protectionMeasures;

    // Constructor and methods for implementing the PII policy
    // ...

    // Example method to enforce the PII policy
    public boolean enforcePolicy(DataRecord data) {
        // Implementation to enforce the PII policy on a data record
        // ...
        return true;  // Compliance achieved
    }
}

The Java code snippet represents a simplified PII policy class. It includes fields for the purpose of collecting PII, legal basis, and protection measures. The enforcePolicy method could be used to validate data against the policy.

3. Implement Data Security With the Right Tools

Arming your organization with cutting-edge data security tools and technologies is the next critical stride in the journey of PII compliance. Encryption, access controls, and secure transmission protocols form the arsenal against potential threats, safeguarding various types of sensitive data.

The emphasis lies not only on adopting these measures but also on the proactive and regular updating and patching of software to address vulnerabilities, ensuring a dynamic defense against evolving cyber threats.


function implementDataSecurity(data) {
    // Example implementation for data encryption
    let encryptedData = encryptData(data);

    // Example implementation for access controls
    grantAccess(user, encryptedData);

    // Example implementation for secure transmission
    sendSecureData(encryptedData);
}

function encryptData(data) {
    // Implementation for data encryption
    // ...
    return encryptedData;
}

function grantAccess(user, data) {
    // Implementation for access controls
    // ...
}

function sendSecureData(data) {
    // Implementation for secure data transmission
    // ...
}

The JavaScript code snippet provides examples of implementing data security measures, including data encryption, access controls, and secure transmission.

4. Practice IAM

Identity and Access Management (IAM) emerges as the sentinel standing guard over sensitive data. The implementation of IAM practices should be designed not only to restrict unauthorized access but also to regularly review and update user access privileges. The alignment of these privileges with job roles and responsibilities becomes the anchor, ensuring that access is not only secure but also purposeful.

5. Monitor and Respond

In the ever-shifting landscape of digital security, continuous monitoring becomes the heartbeat of effective PII compliance. Simultaneously, it advocates for the establishment of an incident response plan, a blueprint for swift and decisive action in the aftermath of a breach. The timely response becomes the bulwark against the cascading impacts of a data breach.

6. Regularly Assess Your Organization’s PII

The journey towards PII compliance is not a one-time endeavor but an ongoing commitment, making periodic assessments of an organization's PII practices a critical task. Internal audits and risk assessments become the instruments of scrutiny, identifying areas for improvement and addressing emerging threats. It is a proactive stance that ensures the adaptive evolution of PII compliance strategies in tandem with the ever-changing threat landscape.

7. Keep Your Privacy Policy Updated

In the dynamic sphere of technology and regulations, the privacy policy becomes the living document that shapes an organization's commitment to data protection. It is of vital importance to regularly review and update the privacy policy. It is not merely a legal requirement but a demonstration of the organization's responsiveness to the evolving landscape, aligning data protection practices with the latest compliance requirements and technological advancements.

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# Example implementation for reviewing and updating the privacy policy
class PrivacyPolicyUpdater
  def self.update_policy
    # Implementation for reviewing and updating the privacy policy
    # ...
  end
end

# Example usage
PrivacyPolicyUpdater.update_policy

The Ruby script provides an example of a script to review and update a privacy policy.

8. Prepare a Data Breach Response Plan

Anticipation and preparedness are the hallmarks of resilient organizations. Despite the most stringent preventive measures, the possibility of a data breach looms. Beyond the blueprint, it emphasizes the necessity of practicing and regularly updating this plan, transforming it from a theoretical document into a well-oiled machine ready to mitigate the impact of a breach through strategic communication, legal considerations, and effective remediation steps.

Key PII Compliance Standards

Understanding the regulatory landscape is crucial for PII compliance. Different regions have distinct compliance standards and data privacy regulations that organizations must adhere to. Here are some key standards:

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United States Data Privacy Regulations: In the United States, organizations need to comply with various federal and state regulations. Examples include the Health Insurance Portability and Accountability Act (HIPAA) for healthcare information and the Gramm-Leach-Bliley Act (GLBA) for financial data.

Europe Data Privacy Regulations: European countries operate under the General Data Protection Regulation (GDPR), a comprehensive framework that sets strict standards for the processing and protection of personal data. GDPR compliance is essential for organizations dealing with European citizens' information.

Conclusion

PII compliance is not just a regulatory requirement; it is a fundamental aspect of responsible and ethical business practices. Protecting sensitive data through a robust compliance framework not only mitigates the risk of data breaches but also fosters trust among customers and stakeholders. By following a comprehensive PII compliance checklist and staying informed about relevant standards, organizations can navigate the complex landscape of data protection successfully. As technology continues to advance, a proactive and adaptive approach to PII compliance is key to securing the future of sensitive data protection.

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If you want to learn more about Sentra's Data Security Platform and how you can use a strong PII compliance framework to protect sensitive data, reduce breach risks, and build trust with customers and stakeholders, request a demo today.