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What is Sensitive Data Exposure and How to Prevent It

January 1, 2024
6
 Min Read
Data Security

What is Sensitive Data Exposure?

Sensitive data exposure occurs when security measures fail to protect sensitive information from external and internal threats. This leads to unauthorized disclosure of private and confidential data. Attackers often target personal data, such as financial information and healthcare records, as it is valuable and exploitable.

Security teams play a critical role in mitigating sensitive data exposures. They do this by implementing robust security measures. This includes eliminating malicious software, enforcing strong encryption standards, and enhancing access controls. Yet, even with the most sophisticated security measures in place, data breaches can still occur. They often happen through the weakest links in the system.

Organizations must focus on proactive measures to prevent data exposures. They should also put in place responsive strategies to effectively address breaches. By combining proactive and responsive measures, as stated below, organizations can protect sensitive data exposure. They can also maintain the trust of their customers.

Proactive Measures Responsive Strategies
Implementation of appropriate security posture controls for sensitive data, such as encryption, data masking, de-identification, etc. Security audits with patch management ensure the masking of affected data to minimize the attack surface and eradicate threats.
Sensitive data access restrictions through least privilege principles enforcement. Promptly identifying and reacting through incident response systems with adequate alerting.
Enablement of comprehensive logging mechanisms to capture and monitor activities on sensitive data. Investigating the root cause of the breach to prevent similar incidents from occurring in the future.
Alignment with cyber protection regulations and compliance requirements through adherence to strict cyber policies. Implementing additional custom security measures to strengthen the overall security posture.

Difference Between Data Exposure and Data Breach

Both data exposure and data breaches involve unauthorized access or disclosure of sensitive information. However, they differ in their intent and the underlying circumstances.

Data Exposure

Data exposure occurs when sensitive information is inadvertently disclosed or made accessible to unauthorized individuals or entities. This exposure can happen due to various factors. These include misconfigured systems, human error, or inadequate security measures. Data exposure is typically unintentional. The exposed data may not be actively targeted or exploited.

Data Breach

A data breach, on the other hand, is a deliberate act of unauthorized access to sensitive information with the intent to steal, manipulate, or exploit it. Data breaches are often carried out by cybercriminals or malicious actors seeking financial gain, identity theft, or to disrupt an organization's operations.

Key Differences

The table below summarizes the key differences between sensitive data exposure and data breaches:

Features Data Exposure Data Breach
Intent Unintentional Intentional
Underlying Factor Human error, misconfigured systems, inadequate security Deliberate attacks by cybercriminals or malicious actors
Impact Can still lead to privacy violations and reputational damage Often more severe impacts, including fraud and financial losses, identity theft, and disruption of operations
Solutions Following security best practices, continuous monitoring and SecOps literacy Robust security measures with discrete monitoring and alerting for anomaly detection and remediation

Types of Sensitive Data Exposure

Attackers relentlessly pursue sensitive data. They create increasingly sophisticated and inventive methods to breach security systems and compromise valuable information. Their motives range from financial gain to disruption of operations. Ultimately, this causes harm to individuals and organizations alike. There are three main types of data breaches that can compromise sensitive information:

Availability Breach

An availability breach occurs when authorized users are temporarily or permanently denied access to sensitive data. Ransomware commonly uses this method to extort organizations. Such disruptions can impede business operations and hinder essential services. They can also result in financial losses. Addressing and mitigating these breaches is essential to ensure uninterrupted access and business continuity.

Confidentiality Breach

A confidentiality breach occurs when unauthorized entities access sensitive data, infringing upon its privacy and confidentiality. The consequences can be severe. They can include financial fraud, identity theft, reputational harm, and legal repercussions. It's crucial to maintain strong security measures. Doing so prevents breaches and preserves sensitive information's integrity.

Integrity Breach

An integrity breach occurs when unauthorized individuals or entities alter or modify sensitive data. AI LLM training is particularly vulnerable to this breach form. This compromises the data's accuracy and reliability. This manipulation of data can result in misinformation, financial losses, and diminished trust in data quality. Vigilant measures are essential to protect data integrity. They also help reduce the impact of breaches.

How Sensitive Data Gets Exposed

Sensitive data, including vital information like Personally Identifiable Information (PII), financial records, and healthcare data, forms the backbone of contemporary organizations. Unfortunately, weak encryption, unreliable application programming interfaces, and insufficient security practices from development and security teams can jeopardize this invaluable data. Such lapses lead to critical vulnerabilities, exposing sensitive data at three crucial points:

Data in Transit

Data in transit refers to the transfer of data between locations, such as from a user's device to a server or between servers. This data is a prime target for attackers due to its often unencrypted state, making it vulnerable to interception. Key factors contributing to data exposure in transit include weak encryption, insecure protocols, and the risk of man-in-the-middle attacks. It is crucial to address these vulnerabilities to enhance the security of data during transit.

Data at Rest

While data at rest is less susceptible to interception than data in transit, it remains vulnerable to attacks. Enterprises commonly face internal exposure to sensitive data when they have misconfigurations or insufficient access controls on data at rest. Oversharing and insufficient access restrictions heighten the risk in data lakes and warehouses that house Personally Identifiable Information (PII). To mitigate this risk, it is important to implement robust access controls and monitoring measures. This ensures restricted access and vigilant tracking of data access patterns.

Data in Use

Data in use is the most vulnerable to attack, as it is often unencrypted and can be accessed by multiple users and applications. When working in cloud computing environments, dev teams usually gather the data and cache it within the mounts or in-memory to boost performance and reduce I/O. Such data causes sensitive data exposure vulnerabilities as other teams or cloud providers can access the data. The security teams need to adopt standard data handling practices. For example, they should clean the data from third-party or cloud mounts after use and disable caching.

What Causes Sensitive Data Exposure?

Sensitive data exposure results from a combination of internal and external factors. Internally, DevSecOps and Business Analytics teams play a significant role in unintentional data exposures. External threats usually come from hackers and malicious actors. Mitigating these risks requires a comprehensive approach to safeguarding data integrity and maintaining a resilient security posture.

Internal Causes of Sensitive Data Exposure

  • No or Weak Encryption: Encryption and decryption algorithms are the keys to safeguarding data. Sensitive data exposures occur due to weak cryptography protocols. They also occur due to a lack of encryption or hashing mechanisms.
  • Insecure Passwords: Insecure password practices and insufficient validation checks compromise enterprise security, facilitating data exposure.
  • Unsecured Web Pages: JSON payloads get delivered from web servers to frontend API handlers. Attackers can easily exploit the data transaction between the server and client when users browse unsecure web pages with weak SSL and TLS certificates.
  • Poor Access Controls and Misconfigurations: Insufficient multi-factor authentication (MFA) or excessive permissioning and unreliable security posture management contribute to sensitive data exposure through misconfigurations.
  • Insider Threat Attacks: Current or former employees may unintentionally or intentionally target data, posing risks to organizational security and integrity.

External Causes of Sensitive Data Exposure

  • SQL Injection: SQL Injection happens when attackers introduce malicious queries and SQL blocks into server requests. This lets them tamper with backend queries to retrieve or alter data, causing SQL injection attacks.
  • Network Compromise: A network compromise occurs when unauthorized users gain control of backend services or servers. This compromises network integrity, risking resource theft or data alteration.
  • Phishing Attacks: Phishing attacks contain malicious links. They exploit urgency, tricking recipients into disclosing sensitive information like login credentials or personal details.
  • Supply Chain Attacks: When compromised, Third-party service providers or vendors exploit the dependent systems and unintentionally expose sensitive data publicly.

Impact of Sensitive Data Exposure

Exposing sensitive data poses significant risks. It encompasses private details like health records, user credentials, and biometric data. Accountability, governed by acts like the Accountability Act, mandates organizations to safeguard granular user information. Failure to prevent unauthorized exposure can result in severe consequences. This can include identity theft and compromised user privacy. It can also lead to regulatory and legal repercussions and potential corruption of databases and infrastructure. Organizations must focus on stringent measures to mitigate these risks.

Data table on the impact of sensitive data exposure and its severity.

Examples of Sensitive Data Exposure

Prominent companies, including Atlassian, LinkedIn, and Dubsmash, have unfortunately become notable examples of sensitive data exposure incidents. Analyzing these cases provides insights into the causes and repercussions of such data exposure. It offers valuable lessons for enhancing data security measures.

Atlassian Jira (2019)

In 2019, Atlassian Jira, a project management tool, experienced significant data exposure. The exposure resulted from a configuration error. A misconfiguration in global permission settings allowed unauthorized access to sensitive information. This included names, email addresses, project details, and assignee data. The issue originated from incorrect permissions granted during the setup of filters and dashboards in JIRA.

LinkedIn (2021)

LinkedIn, a widely used professional social media platform, experienced a data breach where approximately 92% of user data was extracted through web scraping. The security incident was attributed to insufficient webpage protection and the absence of effective mechanisms to prevent web crawling activity.

Equifax (2017)

In 2017, Equifax Ltd., the UK affiliate of credit reporting company Equifax Inc., faced a significant data breach. Hackers infiltrated Equifax servers in the US, impacting over 147 million individuals, including 13.8 million UK users. Equifax failed to meet security obligations. It outsourced security management to its US parent company. This led to the exposure of sensitive data such as names, addresses, phone numbers, dates of birth, Equifax membership login credentials, and partial credit card information.

Cost of Compliance Fines

Data exposure poses significant risks, whether at rest or in transit. Attackers target various dimensions of sensitive information. This includes protected health data, biometrics for AI systems, and personally identifiable information (PII). Compliance costs are subject to multiple factors influenced by shifting regulatory landscapes. This is true regardless of the stage.

Enterprises failing to safeguard data face substantial monetary fines or imprisonment. The penalty depends on the impact of the exposure. Fines can range from millions to billions, and compliance costs involve valuable resources and time. Thus, safeguarding sensitive data is imperative for mitigating reputation loss and upholding industry standards.

How to Determine if You Are Vulnerable to Sensitive Data Exposure?

Detecting security vulnerabilities in the vast array of threats to sensitive data is a challenging task. Unauthorized access often occurs due to lax data classification and insufficient access controls. Enterprises must adopt additional measures to assess their vulnerability to data exposure.

Deep scans, validating access levels, and implementing robust monitoring are crucial steps. Detecting unusual access patterns is crucial. In addition, using advanced reporting systems to swiftly detect anomalies and take preventive measures in case of a breach is an effective strategy. It proactively safeguards sensitive data.

Automation is key as well - to allow burdened security teams the ability to keep pace with dynamic cloud use and data proliferation. Automating discovery and classification, freeing up resources, and doing so in a highly autonomous manner without requiring huge setup and configuration efforts can greatly help.

How to Prevent Sensitive Data Exposure

Effectively managing sensitive data demands rigorous preventive measures to avert exposure. Widely embraced as best practices, these measures serve as a strategic shield against breaches. The following points focus on specific areas of vulnerability. They offer practical solutions to either eliminate potential sensitive data exposures or promptly respond to them:

Assess Risks Associated with Data

The initial stages of data and access onboarding serve as gateways to potential exposure. Conducting a thorough assessment, continual change monitoring, and implementing stringent access controls for critical assets significantly reduces the risks of sensitive data exposure. This proactive approach marks the first step to achieving a strong data security posture.

Minimize Data Surface Area

Overprovisioning and excessive sharing create complexities. This turns issue isolation, monitoring, and maintenance into challenges. Without strong security controls, every part of the environment, platform, resources, and data transactions poses security risks. Opting for a less-is-more approach is ideal. This is particularly true when dealing with sensitive information like protected health data and user credentials. By minimizing your data attack surface, you mitigate the risk of cloud data leaks.

Store Passwords Using Salted Hashing Functions and Leverage MFA

Securing databases, portals, and services hinges on safeguarding passwords. This prevents unauthorized access to sensitive data. It is crucial to handle password protection and storage with precision. Use advanced hashing algorithms for encryption and decryption. Adding an extra layer of security through multi-factor authentication strengthens the defense against potential breaches even more.

Disable Autocomplete and Caching

Cached data poses significant vulnerabilities and risks of data breaches. Enterprises often use auto-complete features, requiring the storage of data on local devices for convenient access. Common instances include passwords stored in browser sessions and cache. In cloud environments, attackers exploit computing instances. They access sensitive cloud data by exploiting instances where data caching occurs. Mitigating these risks involves disabling caching and auto-complete features in applications. This effectively prevents potential security threats.

Fast and Effective Breach Response

Instances of personal data exposure stemming from threats like man-in-the-middle and SQL injection attacks necessitate swift and decisive action. External data exposure carries a heightened impact compared to internal incidents. Combatting data breaches demands a responsive approach. It's often facilitated by widely adopted strategies. These include Data Detection and Response (DDR), Security Orchestration, Automation, and Response (SOAR), User and Entity Behavior Analytics (UEBA), and the renowned Zero Trust Architecture featuring Predictive Analytics (ZTPA).

Tools to Prevent Sensitive Data Exposure

Shielding sensitive information demands a dual approach—internally and externally. Unauthorized access can be prevented through vigilant monitoring, diligent analysis, and swift notifications to both security teams and affected users. Effective tools, whether in-house or third-party, are indispensable in preventing data exposure.

Data Security Posture Management (DSPM) is designed to meet the changing requirements of security, ensuring a thorough and meticulous approach to protecting sensitive data. Tools compliant with DSPM standards usually feature data tokenization and masking, seamlessly integrated into their services. This ensures that data transmission and sharing remains secure.

These tools also often have advanced security features. Examples include detailed access controls, specific access patterns, behavioral analysis, and comprehensive logging and monitoring systems. These features are essential for identifying and providing immediate alerts about any unusual activities or anomalies.

Sentra emerges as an optimal solution, boasting sophisticated data discovery and classification capabilities. It continuously evaluates data security controls and issues automated notifications. This addresses critical data vulnerabilities ingrained in its core.

Conclusion

In the era of cloud transformation and digital adoption, data emerges as the driving force behind innovations. Personal Identifiable Information (PII), which is a specific type of sensitive data, is crucial for organizations to deliver personalized offerings that cater to user preferences. The value inherent in data, both monetarily and personally, places it at the forefront, and attackers continually seek opportunities to exploit enterprise missteps.

Failure to adopt secure access and standard security controls by data-holding enterprises can lead to sensitive data exposure. Unaddressed, this vulnerability becomes a breeding ground for data breaches and system compromises. Elevating enterprise security involves implementing data security posture management and deploying robust security controls. Advanced tools with built-in data discovery and classification capabilities are essential to this success. Stringent security protocols fortify the tools, safeguarding data against vulnerabilities and ensuring the resilience of business operations.

If you want to learn more about how you can prevent sensitive data exposure, request a demo with our data security experts today.

Discover Ron’s expertise, shaped by over 20 years of hands-on tech and leadership experience in cybersecurity, cloud, big data, and machine learning. As a serial entrepreneur and seed investor, Ron has contributed to the success of several startups, including Axonius, Firefly, Guardio, Talon Cyber Security, and Lightricks, after founding a company acquired by Oracle.

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Team Sentra
December 26, 2024
5
Min Read
Data Security

Create an Effective RFP for a Data Security Platform & DSPM

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

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?

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?

3. Posture, Risk Assessment & Threat Monitoring

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?

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?

5. Infrastructure & Deployment

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?

6. Operations & Support

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?

7. Pricing & Licensing

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?

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.

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.

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.

Read More
Gilad Golani
December 16, 2024
4
Min Read
Data Security

Best Practices: Automatically Tag and Label Sensitive Data

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.

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

  1. 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.
  2. 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.
  1. 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.
  2. 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.
  3. 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.

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.

The process involves three key actions:

  1. 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.
  2. 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.
  3. 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:

  1. Set up the Microsoft Purview integration - which will allow Sentra to import and sync MPIP sensitivity labels.
  2. 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.

Read More
Yair Cohen
December 4, 2024
6
Min Read
Data Security

PII Compliance Checklist: 2025 Requirements & Best Practices

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.

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

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.

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.

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.


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.


# 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:

  • 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.

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.

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