#pii data classification

[innovativeroutinesinternational]

Safeguard Sensitive Information with PII Data Classification and Data Masking

In the current digital environment, safeguarding sensitive information has become increasingly vital. With the exponential growth of online interchanges and data exchanges, ensuring the security of personal and confidential data is essential to prevent unauthorized access and protect against potential threats. Organizations across industries prioritize PII data classification and masking to mitigate security risks, ensure regulatory compliance, and maintain customer trust. These processes empower businesses to effectively identify, categorize, and secure personally identifiable information (PII), reducing the likelihood of breaches. Companies can enhance their data privacy strategies by employing robust techniques and building robust defenses against cyber threats.

This blog explores the significance of PII data classification and masking, showcasing their role in safeguarding sensitive information while maintaining operational efficiency.

Understanding PII Data Classification

PII data classification is the foundation of a solid data protection strategy. It involves categorizing personal data based on its sensitivity, enabling organizations to apply the appropriate levels of security. By identifying what qualifies as PII—such as names, Social Security numbers, or email addresses—companies can streamline their efforts to protect such information.

Benefits of PII Data Classification

Enhanced Data Visibility: Knowing where PII resides helps organizations maintain control over their data.

Regulatory Compliance: Industries governed by regulations like GDPR, CCPA, or HIPAA require a precise classification for legal adherence.

Risk Mitigation: Proper classification ensures high-risk data receives stringent protection, reducing the impact of potential breaches.

Without classification, sensitive data can remain unnoticed, leaving it vulnerable to exposure. This step ensures that security measures are proactive and aligned with organizational goals.

What Is Data Masking and Why Is It Essential?

Data masking, often paired with PII data classification, is a technique that obscures sensitive information while preserving its usability for authorized operations. This approach replaces accurate data with fictional yet realistic substitutes, ensuring the original values remain hidden.

Why Businesses Rely on Data Masking

Data Security: Masking prevents unauthorized access to sensitive information, even in testing or development environments.

Preservation of Data Utility: Unlike encryption, which renders data unreadable, masking allows continued use of data for non-production tasks.

Compliance Support: Data masking aligns with privacy laws, safeguarding customer data without disrupting operations.

For example, a retail company might mask customer credit card numbers during application testing. The masked data ensures sensitive information is inaccessible, reducing the risk of exposure while enabling seamless application development.

PII Data Classification and Data Masking: A Powerful Combination

While each process is valuable, combining PII data classification and data masking creates a comprehensive data security framework. Together, they offer an end-to-end solution for managing sensitive data throughout its lifecycle.

Key Advantages of Using Both Techniques

Holistic Protection: Classification identifies sensitive data, while masking ensures security in various environments.

Operational Efficiency: Masked data can be used for analytics, training, or software development without compromising security.

Scalable Solutions: These techniques grow with the organization, adapting to evolving data management needs.

For instance, financial institutions often employ both methods to protect customer information while running advanced analytics. This dual approach minimizes vulnerabilities and optimizes resource use.

Best Practices for Implementing PII Data Classification and Data Masking

Assess Your Data Landscape: Conduct audits to identify all PII in your systems.

Leverage Automation: Use automated tools for consistent classification and real-time masking.

Ensure Cross-Department Collaboration: Foster communication between IT, compliance, and business teams for unified implementation.

Regularly Update Strategies: Your security measures should adapt as data and regulations evolve.

Adopting these practices ensures that your organization meets current security standards and stays ahead of emerging threats.

Conclusion

Organizations striving to protect their sensitive information must consider the importance of PII data classification and masking. Together, these techniques fortify defenses against data breaches, ensure observance of privacy regulations, and build trust among customers and stakeholders. By embracing these essential strategies, your organization can confidently navigate the challenges of modern data security while safeguarding its most valuable asset—information.

Invest in PII data classification and data masking today to stay ahead in the ever-evolving world of cybersecurity.

[mastergarryblogs]

Future of Data Loss Prevention Market: Emerging Technologies & Investment Insights

Executive Summary

The global Data Loss Prevention market is poised for exponential growth, driven by increasingly complex data ecosystems, rising regulatory mandates, and escalating threats of cyber espionage and insider threats. From 2024 to 2031, the market is projected to grow at a CAGR of 21.1%, fueled by the digital transformation of enterprises and increasing reliance on cloud platforms. North America, followed by Asia-Pacific and Europe, leads the charge, with BFSI, IT & telecom, and healthcare sectors showcasing the highest adoption rates.

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Data Loss Prevention Market Overview and Growth Forecast

Data security has become a strategic imperative for organizations navigating a landscape fraught with breaches, ransomware, and compliance penalties. In response, the DLP market, valued at several billion USD in 2022, is forecasted to surpass US$25 billion by 2031, bolstered by advanced analytics, AI-powered classification engines, and hybrid deployment models.

Key Data Loss Prevention Market Drivers:

Explosive growth in unstructured data and hybrid work environments.

Enforcement of data protection regulations (e.g., GDPR, CCPA).

Demand for real-time policy enforcement across endpoints, networks, and cloud environments.

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Data Loss Prevention Market Segmentation

By Deployment Type

Cloud-Based DLP

Dominates the landscape due to scalability, remote accessibility, and centralized policy enforcement. Cloud-native DLP tools facilitate seamless integration with SaaS, IaaS, and hybrid cloud infrastructures.

On-Premises DLP

Preferred by legacy-intensive industries with strict data residency requirements such as defense and banking.

By Component: Software and Services

Software

Network DLP: Monitors inbound/outbound traffic for unauthorized data movements.

Endpoint DLP: Protects devices from data exfiltration via USBs, print, clipboard, etc.

Storage-Based DLP: Enforces encryption and access controls in at-rest repositories.

Services

Managed Security Services: Full-stack monitoring and response capabilities.

Consulting Services: Architecture planning, compliance mapping, risk mitigation.

Training and Support: Enablement services to reduce insider threats through user education.

By Organization Size

Large Enterprises

Adopt comprehensive DLP platforms integrated with SIEM, CASB, and identity governance tools.

Small and Medium Enterprises (SMEs)

Adopt modular DLP tools with intuitive dashboards, driven by increasing awareness and affordable SaaS models.

By Application Area

Encryption: Highest penetration rate; often mandated by compliance.

Web and Email Protection: Essential for preventing data leakage via phishing or malicious links.

Policy and Compliance Management: Automates regulatory adherence and audit readiness.

Cloud Storage Monitoring: Critical in safeguarding data in Dropbox, OneDrive, Google Workspace, etc.

Incident Response and Workflow Automation: Enables fast containment and reporting of data breaches.

By End-Use Industry

Banking, Financial Services, and Insurance (BFSI)

Handles voluminous PII and PCI data; integrates DLP with fraud detection and KYC workflows.

Healthcare

Deploys DLP for HIPAA-compliant handling of EHRs, diagnostics, and insurance data.

Government

Adopts DLP for securing classified data, identity records, and national infrastructure schematics.

Retail & Logistics

Protects customer loyalty programs, supply chain data, and payment gateways.

Data Loss Prevention Market Regional Insights and Trends

North America

Leads the global market, driven by high cybersecurity maturity and regulatory stringency.

Major players headquartered here include Microsoft, Cisco, and McAfee.

Asia-Pacific

Fastest-growing region, fueled by digitalization in India, China, and ASEAN.

Surge in demand for endpoint and mobile DLP in decentralized work environments.

Europe

Strongly influenced by GDPR and national data sovereignty laws.

Increasing uptake in financial and healthcare sectors.

Middle East & Africa / Latin America

Emerging markets with rising cybersecurity budgets.

Adoption driven by digital banking and e-government initiatives.

Competitive Landscape

Top Data Loss Prevention Market Players:

Microsoft: Offers comprehensive DLP across M365, Azure, and Endpoint Manager.

Broadcom (Symantec): Provides enterprise-grade solutions with advanced analytics.

CrowdStrike, Cisco, IBM: Leading vendors integrating DLP with broader XDR and AI ecosystems.

Digital Guardian: Specialist vendor known for IP protection in manufacturing and pharma.

Check Point, Citrix, BlackBerry: Offer niche capabilities around secure access and insider threat detection.

Data Loss Prevention Market Trends and Strategic Recommendations

Emerging Trends

Integration with Zero Trust frameworks.

AI and ML-driven classification to reduce false positives.

Cloud-native DLP extending to containers and serverless functions.

Real-time protection for collaboration tools (Slack, Teams, Zoom).

Strategic Moves

Invest in behavioral analytics to detect anomalous data usage patterns.

Implement context-aware DLP policies tailored to roles, locations, and devices.

Prioritize incident response automation to minimize breach dwell times.

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Conclusion

The Data Loss Prevention market is undergoing a transformative evolution as enterprises pivot towards holistic, AI-driven data security. By aligning DLP strategies with cloud-native architectures, real-time threat intelligence, and compliance automation, organizations can confidently secure sensitive data across complex digital ecosystems. Stakeholders must prioritize adaptive DLP models that go beyond legacy perimeter defense, enabling proactive, contextual, and user-centric data protection.

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[hubertdudek]

Databricks: what’s new in April 2025? Updates & Features Explained! #databricks Databricks, What’s New in Databricks? April 2025 Updates & Features Explained! 📌 Key Highlights for This Month: - *00:04* PowerBI task - Refresh PowerBI from Databricks - *01:36* SQL task values - Pass SELECT result to workflow - *05:38* Cost-optimized jobs - Serverless standard mode - *06:34* Google Sheets - Query Databricks - *07:48* Git for dashboards - *08:38* Genie sampling - Genie can read data - *11:22* UC functions with PyPl libraries - *12:22* Anomaly detection - *15:02* PII scanner - Data classification - *16:13* Turn off Hive metastore - *17:17* AI builder - Extract data and more - *21:12* AI query with schema - *22:41* PyDABS - *23:28* ALTER statement - *24:03* TEMP VIEWS in DLT - *24:18* Apps on behalf of the user ============================= 📚 *Notebooks from the video:* 🔗 [GitHub Repository](https://ift.tt/S13qG0b) 🔔𝐃𝐨𝐧'𝐭 𝐟𝐨𝐫𝐠𝐞𝐭 𝐭𝐨 𝐬𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐜𝐡𝐚𝐧𝐧𝐞𝐥 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐮𝐩𝐝𝐚𝐭𝐞𝐬. https://www.youtube.com/@hubert_dudek/?sub_confirmation=1 🔗 Support Me Here! ☕Buy me a coffee: https://ift.tt/9qIpuET ✨ Explore Databricks AI insights and workflows—read more: https://ift.tt/1djZykN ============================= 🎬Suggested videos for you: ▶️ [What’s new in January 2025](https://www.youtube.com/watch?v=JJiwSplZmfk) ▶️ [What’s new in February 2025](https://www.youtube.com/watch?v=tuKI0sBNbmg) ▶️ [What’s new in March 2025](https://youtu.be/hJD7KoNq-uE) ============================= 📚 **New Articles for Further Reading:** - 📝 *More on Databricks into Google Sheets:* 🔗 [Read the full article](https://ift.tt/3cfjJLy) - 📝 *More on Anomaly Detection & Data Freshness:* 🔗 [Read the full article](https://ift.tt/5RB4bWM) - 📝 *More on Goodbye to Hive Metastore:* 🔗 [Read the full article](https://ift.tt/lxjpoRS) - 📝 *More on Databricks Refresh PowerBI Semantic Model:* 🔗 [Read the full article](https://ift.tt/8JAfSvZ) - 📝 *More on ResponseFormat in AI Batch Inference:* 🔗 [Read the full article](https://ift.tt/B07yqRT) ============================= 🔎 Related Phrases: #databricks #bigdata #dataengineering #machinelearning #sql #cloudcomputing #dataanalytics #ai #azure #googlecloud #aws #etl #python #data #database #datawarehouse via Hubert Dudek https://www.youtube.com/channel/UCR99H9eib5MOHEhapg4kkaQ April 22, 2025 at 02:17AM

[learning-code-ficusoft]

Securing Data in Snowflake: Best Practices

Snowflake is a cloud-based data warehouse designed for scalability and flexibility, but securing data within it requires a structured approach. This guide outlines best practices for securing data in Snowflake across authentication, access control, encryption, monitoring, and compliance.

1. Strong Authentication & Access Control

Use Multi-Factor Authentication (MFA)

Enforce MFA for all user accounts to prevent unauthorized access.

Snowflake supports native MFA and integration with SSO providers like Okta, Azure AD, and Ping Identity.

Leverage Role-Based Access Control (RBAC)

Use Snowflake’s RBAC model to grant the least privilege necessary.

Create custom roles instead of assigning direct user permissions. Example:

sql

CREATE ROLE analyst; GRANT USAGE ON DATABASE sales TO ROLE analyst; GRANT USAGE ON SCHEMA sales_data TO ROLE analyst; GRANT SELECT ON ALL TABLES IN SCHEMA sales_data TO ROLE analyst;

Use Network Policies to Restrict Access

Restrict access to trusted IPs using network policies:

sql

CREATE NETWORK POLICY secure_access ALLOWED_IP_LIST=('192.168.1.1/32', '10.10.0.0/16'); ALTER ACCOUNT SET NETWORK POLICY = secure_access;

2. Data Encryption and Protection

Enable End-to-End Encryption

Data in transit: Encrypted using TLS 1.2+.

Data at rest: Encrypted using AES-256 by default.

Use External Key Management (BYOK)

Integrate AWS KMS, Azure Key Vault, or GCP KMS for managing encryption keys.

Mask Sensitive Data Using Dynamic Data Masking

Apply column-level masking to protect PII and financial data:

sql

CREATE MASKING POLICY ssn_mask AS (val STRING) RETURNS STRING -> CASE WHEN CURRENT_ROLE() IN ('admin', 'auditor') THEN val ELSE 'XXX-XX-XXXX' END; ALTER TABLE customers MODIFY COLUMN ssn SET MASKING POLICY ssn_mask;

3. Secure Data Sharing and Access

Limit Data Sharing with Secure Views

Use secure views to control access to specific columns:

sql

CREATE SECURE VIEW customer_summary AS SELECT id, name, country FROM customers;

Enable Row-Level Security

Restrict data access based on user roles:

sql

CREATE ROW ACCESS POLICY country_policy AS (country STRING) RETURNS BOOLEAN -> CASE WHEN CURRENT_ROLE() = 'US_SALES' THEN country = 'USA' WHEN CURRENT_ROLE() = 'EU_SALES' THEN country IN ('France', 'Germany') ELSE FALSE END; ALTER TABLE sales_data ADD ROW ACCESS POLICY country_policy ON (country);

4. Monitoring and Auditing

Enable Snowflake Access History for Auditing

Track who accessed what data using ACCESS_HISTORY:

sql

SELECT * FROM SNOWFLAKE.ACCOUNT_USAGE.ACCESS_HISTORY WHERE QUERY_TEXT LIKE '%SELECT%' ORDER BY EVENT_TIMESTAMP DESC;

Set Up Alerting for Suspicious Activities

Use Snowsight or external SIEM tools (Splunk, Datadog) for log monitoring.

Automate alerts for anomalies such as failed logins or sudden data exports.

5. Compliance and Governance

Leverage Snowflake Data Classification

Use automated data classification to tag sensitive data (e.g., PII, financial records).

Enforce Retention and Time Travel Policies

Set appropriate Time Travel retention (default: 1 day, max: 90 days).

Use Fail-Safe for disaster recovery (7-day retention).

sql

ALTER TABLE transactions SET DATA_RETENTION_TIME_IN_DAYS = 30;

Conclusion

Securing Snowflake requires a multi-layered approach, combining authentication, RBAC, encryption, network security, and monitoring. By implementing these best practices, you can ensure data protection, compliance, and governance while maintaining efficient access control.

WEBSITE: https://www.ficusoft.in/snowflake-training-in-chennai/

[fromdevcom]

Protecting sensitive information from unauthorized access is the process of data security. It covers all of the many cybersecurity techniques you employ, such as encryption, access control (both physical and digital), and others, to protect your data from misuse. Security of data has always been crucial. However, in the present health crisis, more individuals are working remotely (and cloud usage has increased to match), which means that there is more risk than ever that someone may gain unwanted access to your data. Whatever your company does, if it processes personally identifiable information (PII), you need to enhance your company’s data security. Benefits of Data Security Let's look at some of the main advantages that your firm may gain from ensuring a high degree of security of data products:   Protect information Feeling confident and at ease may come from knowing that your information is secure and protected from both internal and external dangers. This allows you to focus on your business plans. Build your reputation Companies and organizations interested in long-term partnerships typically pay special attention to potential partners' reputations. Reputable data protection practices enhance an organization's credibility and foster confidence. Comply with data security requirements All of your company's private information should be safeguarded in accordance with applicable IT security standards and laws. Ensuring correct adherence to data security laws safeguards your business against steep fines and diminished client confidence. Lower legal costs Avoiding an incident altogether is always more cost-effective than dealing with its effects. By putting in place a complete security platform with automatic forensic export of monitoring findings, you may cut back on forensic costs. Data Security Best Practices Identify and categorize sensitive data To properly safeguard your data, you must be aware of the specific categories that you have. Start by allowing your security team to examine and report on your data repositories. They can later classify the information according to its worth to your company. As new data is generated, modified, processed, or communicated, the categorization can be updated. It would be beneficial if you also included rules to stop users from inflating the level of categorization. For example, it should be possible to upgrade or downgrade the data categorization only for privileged users. A policy on data usage is essential It goes without saying that data categorization alone is insufficient; you must create a policy that specifies the different forms of access, classification-based criteria for access, who has access to the data, what constitutes proper data usage, etc. Limit user access to certain locations, then turn them off when they are done. Remember that any policy violations should have severe consequences. Control access to private information You must provide the appropriate user with the appropriate access control. Limit access to information using the principle of least privilege, which states that only privileges required for achieving the intended goal should be made available. This will guarantee that data is used by the appropriate user. Protect data in a physical way When talking about data security best practices, physical security is frequently forgotten. Start by locking down your workstations while not in use to prevent the removal of any equipment from the area. This will protect hard discs and other delicate parts where you save your data. Setting up a BIOS password to prevent hackers from booting into your operating systems is another helpful data security tip. It's also important to pay attention to gadgets like USB flash drives, Bluetooth devices, cellphones, tablets, and computers. Keep records of your cybersecurity procedures When it comes to cybersecurity, relying on rumors and gut feelings is a bad idea. To make it simpler to deliver online

training, checklists, and specific knowledge to your employees and stakeholders, thoroughly document your cybersecurity best practices, rules, and protocols. Implement a security strategy based on risk Pay close attention to the smallest things, such as the hazards that your business may encounter and how they could damage employee and customer data. Here, a thorough risk assessment is necessary. Some actions that risk assessment enables you to take are determining the kind and location of your assets, determining the cybersecurity condition you are in, and maintaining an accurate security approach. You can adhere to rules and safeguard your company against potential leaks and breaches by using a risk-based strategy. Educate your staff Inform each employee about your company's cybersecurity policies and best practices. Maintaining their knowledge of new rules and regulations that the globe is adopting requires constant training. Show them instances of actual security lapses and get their opinions on your present security setup. Make use of multi-factor authentication One of the most sophisticated and tested methods of data protection is multi-factor authentication (MFA). MFA functions by introducing an additional security measure before account authentication. Accordingly, even if the hacker knows your password, they will still need to present a second or third element of verification, such as a security token, fingerprint, voice recognition, or confirmation on your mobile phone. Summary Best practices for data security go beyond the list of safety measures listed above. Additionally, you should regularly back up all your data, encrypt it both in transit and at rest, and enforce secure password usage, among other things. However, you must realize that cybersecurity is not about completely eradicating threats. That is impossible. But that doesn’t mean you should disregard it. You may at the very least greatly reduce hazards by using the proper security measures.

[rose-mason-talk]

Data Tokenization in 2025: A Comprehensive Guide

By 2025, is concerned about privacy and data security have reached levels that have never been seen before. Because of new privacy laws, more people using the cloud, and constant cyber threats, data tokenization has become a vital resource in this day and age. The remainder of this piece will talk about how data tokenization has changed over time, as well as its main uses, pros, and cons in the year 2025.

What is Data Tokenization?

It is possible for "tokenize" data, which means to replace sensitive information like credit card numbers, PII, or medical records with a non-sensitive version of that information. You can't use the token and figure out what it means without the data and the process of tokenization. It helps keep private data safe, which lowers the possibility of data breaches.

Key Components of Data Tokenization in 2025

a. Advanced Tokenization Algorithms

By 2025, the tokenization algorithms have advanced a great deal and are also more efficient. Nowadays, real-time data tokenization can be performed with modern algorithms on large amounts of information without considerable latency. Such algorithms are designed to maintain the usability of the original data as much as possible in the course of its maximum protection.

b. Tokenization in a Multicloud Environment

As enterprises are moving to multicloud architectures, tokenization systems today can allow integration on different clouds. Being able to tokenize and detokenize data within various environments enables organizations to secure their data and use different cloud providers for their elasticity and scalability.

c. Privacy-Enhanced Computation

proactively stay till 2025, tokenization will also use computation methods that protect privacy, like safe multiparty computation and homomorphic encryption. These methods make it possible to process private data in a cleaned-up or tokenized form that doesn't reveal the actual data. This makes it possible to share and analyze the data safely without compromising privacy.

How Tokenization Works in 2025

Step 1: Data Identification and Classification

Prior to any tokenization process, businesses first need to locate and categorize the confidential data within their systems. Due to emerging capabilities in machine learning and artificial intelligence, such data discovery systems are capable of detecting and classifying sensitive content present in both structured and unstructured databases without any human intervention.

Step 2: Tokenization Process

Following data has been classified, the private information is transformed to an unique resource which is generated by the tokenization system.like the credit card number "4111 1111 1111 1111" could be changed into a symbol like "ABCD1234XYZ5678." The original sensitive data is kept safely in a token vault that only those with permission can get to.During data usage, the tokenized format is employed, allowing operations to continue without exposing the original information. This ensures both the confidentiality of the data and its safe handling throughout the process.

Step 4: Detokenization (if needed)

There are a variety of applications where the tokenized data could be used, for instance, e-commerce, CRM systems, business intelligence tools, etc. Because these tokens do not carry sensitive information, the chances of data compromise, while the information is being processed or stored, is significantly mitigated for the organizations.

Benefits of Data Tokenization in 2025

a. Enhanced Data Privacy

The GDPR ( General Data Protection Regulation)in Europe and CCPA the California Consumer Privacy Act  are just two examples of rules and guidelines that require organizations to protect personal data. Other data protection policies are also on the way all over the world. In order to follow the rules, tokenization encrypts private data and lowers the chance that any personal identification information (PII) will be Shared.

b. Reduced Data Breach Risk

So when it comes to data theft, in most cases if not all of them the data that has been tokenized is of no value thus minimizes the risk or the effect of the outside threats. In most cases where an attacker possesses system information with a tokenized piece of data, the attacker cannot be able to disassemble or work out the data without the token storage.

c. Secure Data Analytics

In 2025, owing to the development of tokenization, businesses can now utilize and analyze sensitive information without venturing into privacy concerns. Take the case of the medical or financial industries; data tokenization allows predictive analytics and machine learning to be performed on sensitive data without breaking any data privacy laws.

d. Scalable Across Environments

Organizations can now execute tokenization within on-premises, hybrid, and multi-cloud environments. This flexibility extends even more for organizations migrating toward the cloud or extending their digital infrastructure as data protection becomes paramount.

Secure Your Data with Tokenization – Get Started Today with real world asset tokenization solutions

Use Cases for Tokenization in 2025

a. Financial Services

Tokenization has been usually employed by financial institutions to protect P-C-I and account numbers for years. More so, in 2025, the usage of banks, DeFi and cryptocurrencies propelled this entirely into a new dimension. Aids such as tokenization of identity and transaction provides a layer of security even for purpose of use wallets making it easier to operate under strict financial regulations.

b. Healthcare

The healthcare industry which handles very delicate patient information has embraced tokenization to safeguard medical records, insurance details, and billing information. Tokenization is used by healthcare institutions in the year 2025 for sharing anonymized medical data for the sake of research and collaboration without compromising on patient privacy.

c. E-commerce and Retail

Tokenization is a technique that e-commerce platforms have consistently employed to ensure the safety of customer financial data. In the year 2025, tokenization has been implemented to protect other forms of customer information such as loyalty schemes, delivery addresses, and purchase patterns. Retailers also embrace the use of tokenization to avoid breaching the GDPR and CCPA regulations when conducting worldwide business transactions.

d. IoT and Smart Devices

The Internet of Things (IoT) gets more popular. Tokenization saves the huge amounts of data that smart devices generate. By 2025, tokenization will make it impossible to get to private data like fingerprint data from smartwatches and even geographic information from autonomous vehicles.

Future Trends in Data Tokenization

a. Zero-Knowledge Proofs and Blockchain Integration

Tokenization is talked about more when talking about blockchain applications in 2025, especially when talking about decentralized banking and supply chain. With zero knowledge proofs (ZKP), users can show that a transaction or piece of data is real without giving away the private data itself. This way of doing things is a completely new way to keep info safe and private.

b. Quantum-Resistant Tokenization

Due to the possible advent of quantum computing, which has the capacity to cathartic current encryption protocols, quantum secure tokenization methods are in the process of being constructed. These methods make sure that the tokenized data will be safe even in the post-quantum world.

c. AI-Driven Tokenization

AI is fundamentally important in providing an automated approach to the tokenization process. By 2025, AI technologies will be able to operate in real time to locate sensitive information and campaign for its tokenization while adjusting itself to variations in the flow of information. This lightens the burden placed on humans and enhances the effectiveness of the mechanisms put in place to safeguard data integrity.

Conclusion

By the year 2025, data tokenization is no longer an emerging trend, but a holy grail in the modern strategies of securing data. As the cloud computing technologies, AI, and quantum resistant techniques improve, the tokenization technology today is not only more advanced but also secure and scalable to businesses. Regardless of the industry – be it finance, healthcare, retail, the Internet of Things, or others – organizations are utilizing tokenization to secure data, adhere to international laws, and minimize the chances of data losses.

With advancements in technology, whereby every day, new products and services reach the target market, it can be expected that even in 2025 and beyond, the practice of tokenization will be one of the foremost tools in the enhancement of privacy, security, and trust in this age that is highly reliant on data.

[innovativeroutinesinternational]

Strengthening Data Security with PII Data Classification and Masking

In the digital age, protecting personal and sensitive information has become a top priority for businesses and organizations. As data breaches and cyber threats continue to rise, securing Personally Identifiable Information (PII) has become more complex and crucial. Effective security strategies require implementing both PII data classification and data masking to safeguard sensitive information and ensure compliance with privacy regulations. By understanding the role of these two key concepts, businesses can better protect their data and minimize risks.

Understanding PII Data Classification

PII data classification is the process of identifying, organizing, and categorizing personally identifiable information based on its sensitivity and the level of protection it requires. PII refers to any information that can be used to identify an individual, such as names, social security numbers, email addresses, phone numbers, and credit card details. Proper classification helps organizations determine how to handle and protect different types of PII to minimize the risk of exposure and ensure compliance with privacy laws such as GDPR, HIPAA, and CCPA.

By classifying PII data into various categories, organizations can prioritize their security measures according to the level of sensitivity. For instance, highly sensitive information such as financial or medical records might require stricter security protocols than general contact details. When paired with data masking, PII data classification provides a solid foundation for protecting personal data from unauthorized access.

The Importance of Data Masking

While PII data classification helps to categorize data, data masking plays a critical role in protecting that data by concealing it. Data masking is a technique that transforms sensitive information into a format that is still usable for testing or analytical purposes but without exposing the actual data. This process replaces real PII data with fictitious values, ensuring that sensitive information is not accessible to unauthorized individuals, even during non-production use cases like testing or training.

For example, a company conducting software testing may need to use customer data to evaluate system functionality. Instead of using real customer information, they can apply data masking to create a dummy dataset that mimics the structure of real data but without exposing any actual PII. This ensures that sensitive information is never at risk of being compromised during the development process.

Key Benefits of PII Data Classification and Data Masking

Enhanced Data SecurityOne of the primary advantages of using PII data classification in conjunction with data masking is the enhancement of overall data security. Classification helps organizations understand where sensitive information resides and what level of protection it needs. By masking this data, companies can ensure that even if an unauthorized user accesses the system, the masked information will be meaningless, protecting the original data from exposure.

Regulatory CompliancePrivacy regulations such as the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and California Consumer Privacy Act (CCPA) require organizations to implement strong security measures to protect PII. Failure to comply with these regulations can result in hefty fines and damage to a company's reputation. By implementing PII data classification and data masking, organizations can ensure that their data security practices align with regulatory requirements, helping them avoid legal and financial penalties.

Minimization of Data Breach RisksData breaches can have devastating consequences, leading to financial loss, legal liabilities, and reputational damage. The combination of PII data classification and data masking minimizes the risk of breaches by ensuring that only authorized personnel have access to sensitive data, and any exposed data is masked, rendering it useless to attackers. Even in the event of a breach, masked data significantly reduces the likelihood of misuse.

Streamlined Data ManagementWith PII data classification, businesses can better manage their data assets by understanding which data needs the most protection. This streamlined approach allows for more efficient allocation of resources, ensuring that security measures are focused on the most critical data. Data masking complements this by allowing businesses to use secure, masked data for non-production purposes such as development, testing, or analytics, without compromising security.

Protection Against Insider ThreatsInsider threats, whether intentional or accidental, pose a significant risk to data security. Employees with access to sensitive data may inadvertently expose it to unauthorized parties. By using PII data classification to identify sensitive data and applying data masking, organizations can limit access to actual PII, even to those within the company who may need the data for job-related tasks. This reduces the risk of insider threats by ensuring that sensitive information is only accessible when absolutely necessary.

Improved Trust with CustomersIn an era where customers are increasingly concerned about the security of their personal information, implementing strong data protection measures is critical for building trust. When customers know that their data is being handled securely—through practices like PII data classification and data masking—they are more likely to trust the organization with their information. This increased trust can lead to stronger customer relationships and long-term business success.

Implementing PII Data Classification and Data Masking

For businesses looking to enhance their data security, implementing PII data classification and data masking is a strategic move. To do this effectively, organizations should start by conducting a comprehensive audit of their data. This includes identifying all sources of PII, determining where it is stored, and assessing the current security measures in place.

Once the data has been classified according to its sensitivity, businesses can apply data masking techniques to protect the most critical information. It’s important to choose data masking solutions that integrate seamlessly with existing systems and workflows, ensuring minimal disruption to business operations. Automated tools can also help organizations maintain compliance by continuously monitoring data and applying the appropriate masking techniques where necessary.

Conclusion

In today's data-driven world, protecting personal information is essential for businesses to maintain trust and stay compliant with privacy regulations. By leveraging PII data classification and data masking, organizations can ensure that their sensitive data remains secure, even in the face of growing cyber threats. These techniques not only strengthen data protection but also reduce the risk of breaches, enhance compliance, and improve overall data management.

Incorporating PII data classification and data masking into your cybersecurity strategy is a proactive way to safeguard your organization’s data and reputation. With the right approach, you can confidently protect sensitive information while maintaining compliance with the latest data protection standards.

[aven-data]

Data Privacy and Security Considerations for SAP Carve-Out Projects

In the realm of SAP carve-out projects, ensuring data privacy and security stands as a paramount concern. As organizations navigate the intricate process of segregating SAP systems, they must remain vigilant in protecting sensitive data from unauthorized access, breaches, or inadvertent leaks. This necessitates a comprehensive approach that encompasses various aspects of data management, from access controls to encryption protocols.

Assessing Data Sensitivity and Classification

Before initiating a SAP carve-out, it's imperative to conduct a thorough assessment of the data landscape within the SAP environment. This involves identifying and classifying data based on its sensitivity and regulatory requirements. By categorizing data into tiers based on its level of confidentiality, organizations can tailor their security measures accordingly, allocating resources where they are most needed.

Implementing Robust Access Controls and Encryption

One of the cornerstones of data security in SAP carve-outs is the implementation of robust access controls. This entails restricting access to sensitive data only to authorized personnel through role-based access controls (RBAC) and stringent authentication mechanisms. Additionally, employing encryption technologies such as data-at-rest and data-in-motion encryption adds an extra layer of protection, rendering data unreadable to unauthorized parties even if it's intercepted.

Data Masking and Anonymization Techniques

In scenarios where sensitive data needs to be shared or migrated during a SAP carve-out, employing data masking and anonymization techniques can mitigate the risk of exposing personally identifiable information (PII). By replacing sensitive information with realistic, but fictitious, data, organizations can maintain the utility of the data for testing or training purposes while safeguarding individual privacy.

Monitoring and Incident Response

Continuous monitoring of SAP systems is essential for detecting and responding to security threats or breaches promptly. Implementing robust monitoring tools that provide real-time alerts for suspicious activities can help organizations identify and mitigate security incidents before they escalate. Additionally, having a well-defined incident response plan in place ensures a swift and coordinated response in the event of a security breach, minimizing the potential impact on the organization.

Conclusion

Data privacy and security considerations are paramount in SAP carve-out projects, where the segregation of systems introduces complexities and vulnerabilities. By adopting a proactive approach that encompasses data classification, access controls, encryption, masking, monitoring, and incident response, organizations can fortify their SAP environments against potential threats, safeguarding sensitive data and preserving trust among stakeholders.

[eswars-tech-world]

42. What are the considerations for handling data privacy and compliance in SSIS?

Interview questions on SSIS Development #etl #ssis #ssisdeveloper #integrationservices #eswarstechworld #sqlserverintegrationservices #interview #interviewquestions #interviewpreparation

Data privacy and compliance in SQL Server Integration Services (SSIS) involve ensuring that the handling, transformation, and movement of data comply with privacy regulations and organizational policies. Categories/Classifications/Types: Sensitive Data Handling: Identification and protection of sensitive data such as personally identifiable information (PII) and financial data. Encryption and…

[jcmarchi]

Best data security platforms of 2025 - AI News

New Post has been published on https://thedigitalinsider.com/best-data-security-platforms-of-2025-ai-news/

Best data security platforms of 2025 - AI News

With the rapid growth in the generation, storage, and sharing of data, ensuring its security has become both a necessity and a formidable challenge. Data breaches, cyberattacks, and insider threats are constant risks that require sophisticated solutions. This is where Data Security Platforms come into play, providing organisations with centralised tools and strategies to protect sensitive information and maintain compliance.

Key components of data security platforms

Effective DSPs are built on several core components that work together to protect data from unauthorised access, misuse, and theft. The components include:

1. Data discovery and classification

Before data can be secured, it needs to be classified and understood. DSPs typically include tools that automatically discover and categorize data based on its sensitivity and use. For example:

Personal identifiable information (PII): Names, addresses, social security numbers, etc.

Financial data: Credit card details, transaction records.

Intellectual property (IP): Trade secrets, proprietary designs.

Regulated data: Information governed by laws like GDPR, HIPAA, or CCPA.

By identifying data types and categorizing them by sensitivity level, organisations can prioritise their security efforts.

2. Data encryption

Encryption transforms readable data into an unreadable format, ensuring that even if unauthorised users access the data, they cannot interpret it without the decryption key. Most DSPs support various encryption methods, including:

At-rest encryption: Securing data stored on drives, databases, or other storage systems.

In-transit encryption: Protecting data as it moves between devices, networks, or applications.

Modern DSPs often deploy advanced encryption standards (AES) or bring-your-own-key (BYOK) solutions, ensuring data security even when using third-party cloud storage.

3. Access control and identity management

Managing who has access to data is a important aspect of data security. DSPs enforce robust role-based access control (RBAC), ensuring only authorised users and systems can access sensitive information. With identity and access management (IAM) integration, DSPs can enhance security by combining authentication methods like:

Passwords.

Biometrics (e.g. fingerprint or facial recognition).

Multi-factor authentication (MFA).

Behaviour-based authentication (monitoring user actions for anomalies).

4. Data loss prevention (DLP)

Data loss prevention tools in DSPs help prevent unauthorised sharing or exfiltration of sensitive data. They monitor and control data flows, blocking suspicious activity like:

Sending confidential information over email.

Transferring sensitive data to unauthorised external devices.

Uploading important files to unapproved cloud services.

By enforcing data-handling policies, DSPs help organisations maintain control over their sensitive information.

5. Threat detection and response

DSPs employ threat detection systems powered by machine learning, artificial intelligence (AI), and behaviour analytics to identify unauthorised or malicious activity. Common features include:

Anomaly detection: Identifies unusual behaviour, like accessing files outside normal business hours.

Insider threat detection: Monitors employees or contractors who might misuse their access to internal data.

Real-time alerts: Provide immediate notifications when a potential threat is detected.

Some platforms also include automated response mechanisms to isolate affected data or deactivate compromised user accounts.

6. Compliance audits and reporting

Many industries are subject to strict data protection regulations, like GDPR, HIPAA, CCPA, or PCI DSS. DSPs help organisations comply with these laws by:

Continuously monitoring data handling practices.

Generating detailed audit trails.

Providing pre-configured compliance templates and reporting tools.

The features simplify regulatory audits and reduce the risk of non-compliance penalties.

Best data security platforms of 2025

Whether you’re a small business or a large enterprise, these tools will help you manage risks, secure databases, and protect sensitive information.

1. Velotix

Velotix is an AI-driven data security platform focused on policy automation and intelligent data access control. It simplifies compliance with stringent data regulations like GDPR, HIPAA, and CCPA, and helps organisations strike the perfect balance between accessibility and security. Key Features:

AI-powered access governance: Velotix uses machine learning to ensure users only access data they need to see, based on dynamic access policies.

Seamless integration: It integrates smoothly with existing infrastructures across cloud and on-premises environments.

Compliance automation: Simplifies meeting legal and regulatory requirements by automating compliance processes.

Scalability: Ideal for enterprises with complex data ecosystems, supporting hundreds of terabytes of sensitive data.

Velotix stands out for its ability to reduce the complexity of data governance, making it a must-have in today’s security-first corporate world.

2. NordLayer

NordLayer, from the creators of NordVPN, offers a secure network access solution tailored for businesses. While primarily a network security tool, it doubles as a robust data security platform by ensuring end-to-end encryption for your data in transit.

Key features:

Zero trust security: Implements a zero trust approach, meaning users and devices must be verified every time data access is requested.

AES-256 encryption Standards: Protects data flows with military-grade encryption.

Cloud versatility: Supports hybrid and multi-cloud environments for maximum flexibility.

Rapid deployment: Easy to implement even for smaller teams, requiring minimal IT involvement.

NordLayer ensures secure, encrypted communications between your team and the cloud, offering peace of mind when managing sensitive data.

3. HashiCorp Vault

HashiCorp Vault is a leader in secrets management, encryption as a service, and identity-based access. Designed for developers, it simplifies access control without placing sensitive data at risk, making it important for modern application development.

Key features:

Secrets management: Protect sensitive credentials like API keys, tokens, and passwords.

Dynamic secrets: Automatically generate temporary, time-limited credentials for improved security.

Encryption as a service: Offers flexible tools for encrypting any data across multiple environments.

Audit logging: Monitor data access attempts for greater accountability and compliance.

With a strong focus on application-level security, HashiCorp Vault is ideal for organisations seeking granular control over sensitive operational data.

4. Imperva Database Risk & Compliance

Imperva is a pioneer in database security. Its Database Risk & Compliance solution combines analytics, automation, and real-time monitoring to protect sensitive data from breaches and insider threats.

Key features:

Database activity monitoring (DAM): Tracks database activity in real time to identify unusual patterns.

Vulnerability assessment: Scans databases for security weaknesses and provides actionable remediation steps.

Cloud and hybrid deployment: Supports flexible environments, ranging from on-premises deployments to modern cloud setups.

Audit preparation: Simplifies audit readiness with detailed reporting tools and predefined templates.

Imperva’s tools are trusted by enterprises to secure their most confidential databases, ensuring compliance and top-notch protection.

5. ESET

ESET, a well-known name in cybersecurity, offers an enterprise-grade security solution that includes powerful data encryption tools. Famous for its malware protection, ESET combines endpoint security with encryption to safeguard sensitive information.

Key features:

Endpoint encryption: Ensures data remains protected even if devices are lost or stolen.

Multi-platform support: Works across Windows, Mac, and Linux systems.

Proactive threat detection: Combines AI and machine learning to detect potential threats before they strike.

Ease of use: User-friendly dashboards enable intuitive management of security policies.

ESET provides an all-in-one solution for companies needing endpoint protection, encryption, and proactive threat management.

6. SQL Secure

Aimed at database administrators, SQL Secure delivers specialised tools to safeguard SQL Server environments. It allows for detailed role-based analysis, helping organisations improve their database security posture and prevent data leaks.

Key features:

Role analysis: Identifies and mitigates excessive or unauthorised permission assignments.

Dynamic data masking: Protects sensitive data by obscuring it in real-time in applications and queries.

Customisable alerts: Notify teams of improper database access or policy violations immediately.

Regulatory compliance: Predefined policies make it easy to align with GDPR, HIPAA, PCI DSS, and other regulations.

SQL Secure is a tailored solution for businesses dependent on SQL databases, providing immediate insights and action plans for tighter security.

7. Acra

Acra is a modern, developer-friendly cryptographic tool engineered for data encryption and secure data lifecycle management. It brings cryptography closer to applications, ensuring deep-rooted data protection at every level.

Key features:

Application-level encryption: Empowers developers to integrate customised encryption policies directly into their apps.

Intrusion detection: Monitors for data leaks with a robust intrusion detection mechanism.

End-to-end data security: Protect data at rest, in transit, and in use, making it more versatile than traditional encryption tools.

Open source availability: Trusted by developers thanks to its open-source model, offering transparency and flexibility.

Acra is particularly popular with startups and tech-savvy enterprises needing a lightweight, developer-first approach to securing application data.

8. BigID

BigID focuses on privacy, data discovery, and compliance by using AI to identify sensitive data across structured and unstructured environments. Known for its data intelligence capabilities, BigID is one of the most comprehensive platforms for analysing and protecting enterprise data.

Key Features:

Data discovery: Automatically classify sensitive data like PII (Personally Identifiable Information) and PHI (Protected Health Information).

Privacy-by-design: Built to streamline compliance with global privacy laws like GDPR, CCPA, and more.

Risk management: Assess data risks and prioritise actions based on importance.

Integrations: Easily integrates with other security platforms and cloud providers for a unified approach.

BigID excels at uncovering hidden risks and ensuring compliance, making it an essential tool for data-driven enterprises.

9. DataSunrise Database Security

DataSunrise specialises in database firewall protection and intrusion detection for a variety of databases, including SQL-based platforms, NoSQL setups, and cloud-hosted solutions. It focuses on safeguarding sensitive data while providing robust real-time monitoring.

Key features:

Database firewall: Blocks unauthorised access attempts with role-specific policies.

Sensitive data discovery: Identifies risky data in your database for preventative action.

Audit reporting: Generate detailed investigative reports about database activity.

Cross-platform compatibility: Works with MySQL, PostgreSQL, Oracle, Amazon Aurora, Snowflake, and more.

DataSunrise is highly configurable and scalable, making it a solid choice for organisations running diverse database environments.

10. Covax Polymer

Covax Polymer is an innovative data security platform dedicated to governing sensitive data use in cloud-based collaboration tools like Slack, Microsoft Teams, and Google Workspace. It’s perfect for businesses that rely on SaaS applications for productivity.

Key features:

Real-time governance: Monitors and protects data transfers occurring across cloud collaboration tools.

Context-aware decisions: Evaluates interactions to identify potential risks, ensuring real-time security responses.

Data loss prevention (DLP): Prevents sensitive information from being shared outside approved networks.

Comprehensive reporting: Tracks and analyses data sharing trends, offering actionable insights for compliance.

Covax Polymer addresses the growing need for securing communications and shared data in collaborative workspaces.

(Image source: Unsplash)

[gauravgupta1122]

Comprehending Sensitive Data: Categories, Risks, and Protective Measures

In the face of escalating cyber threats, protecting sensitive data has become imperative for organizations to safeguard their reputation and overall security. A data breach can lead to severe consequences, including financial losses, reputational damage, and legal penalties. Data Discovery and Classification emerge as pivotal tools, functioning as a GPS for navigating today's vast data landscapes. These tools are essential for maintaining data relevance, consistency, and security, especially in the context of growing data volumes and stringent regulatory demands.

Sensitive data encompasses various types, from personally identifiable information (PII) and protected health information (PHI) to financial details and intellectual property. Recognizing and classifying these data types enables organizations to tailor effective protection strategies. The sensitivity of data is context-dependent, considering factors like regulatory requirements, accessibility, age, dependencies, and value. Measurement of data sensitivity involves evaluating compliance, access privileges, data lifecycle, dependencies, and potential impact.

Protecting sensitive data involves a multi-faceted approach, including data discovery, classification, security controls, regular audits, employee training, and an incident response plan. Automated tools like SISA Radar play a crucial role in this process, ensuring efficiency, accuracy, and consistency. By adopting these measures, organizations not only enhance their security posture but also build trust with customers and stakeholders, effectively mitigating the damaging fallout of potential data breaches.

Read More: https://www.sisainfosec.com/blogs/understanding-sensitive-data/

[horek22]

Wesleyan University

Data Management and Visualization

Week1

I’ve chosen the "Mars craters” study since I’m interested to geographical topics.  

1st Question: I would like to invesitgate: “Is there crater diameter associated with the morphology?”

To do this, I choose following variables to my codebook:

CRATER_ID – crater ID for internal sue, based upon the region of the planet

(1/16ths), the “pass” under which the crate was identified, ad the order in which

it was identified

DIAM_CIRCLE_IMAGE – diameter from a non-­‐linear least squares circle fit to the

vertices selected to manually identify the crater rim (units are km)

MORPHOLOGY_EJECTA_1 – ejecta morphology classified. Examples below.

o If there are multiple values, separated by a “/”, then the order is the

inner-­‐most ejecta through the outer-­‐most, or the top-­‐most through

the bottom-­‐most

2nd question:

“Is the crater diameter of the crater associated with the type of the lake in the crater.”

To investigate that, I extended the codebook with 2 additonal variables:

LAKE – categorical variable: was there any lake in the crater  -  yes / no

LKE_CLASS: categorical variable of  Basin class: O, open; C, closed; LC, lake chain

Used literature to the 2nd question:

1)      “Distribution, Classification, and Ages of Martian Impact Crater Lakes:

https://www.sciencedirect.com/science/article/pii/S0019103599961912?ref=cra_js_challenge&fr=RR-1

2) https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/1999JE001219

Hypothesis based on the literature: There is no correlation between the crater diameter and the lake type (based on table 1 of 1) ).

[projectcubicle1]

The Three Pillars of Effective Data Protection

The Three Pillars of Effective Data Protection

All businesses have critical or sensitive data that gives them their competitive edge and keeps them open for business. Coca-Cola is a prime example, whose recipe of the popular soft drink has been guarded for over 130 years. Intellectual property, financial projections, and personal information of customers are all examples of such critical or sensitive data that requires protection from breach or unauthorized use. To protect their data, some businesses integrate data loss prevention (DLP) solutions and assume that’s all they need. Some organizations label their most critical data but fail to take into account for tracking it, leaving it to the hands of their trained (or not) employees to prevent unauthorized access. Still others protect data in use and data at rest, but forget to consider data in transit, moving from one place (or person) to another. A solid approach applies the following three strategies: DLP, data classification, and secure collaboration. In this article, we describe how each of these three pillars of data protection work and how they complement each other to ensure that data remains in the hands of those intended. 1 – Data Loss Prevention (DLP),

DLP identifies and prevents the misuse, loss, and exfiltration of critical data, where critical is defined by the company using the product. Often, this data includes that protected by government regulation such as personally identifiable information (PII) and protected health information (PHI). Businesses often use DLP as a dual-purpose solution: to prevent data breaches and accomplish legal compliance. DLP solutions come in different flavors, ranging from the type of attack surface to the type of variables used to identify data. More advanced DLP will combine content with metadata like the document owner, timestamps, and the locations of where the data has moved. Others specialize in the cloud (SaaS) or endpoints or the network, and more comprehensive solutions will monitor events across these layers to build context to identify what’s critical beyond traditional keyword or character matching. While the core functionality of DLP solutions is monitoring and alerting on data breaches, some DLP solutions also provide incident response capability for straight-forward use cases. If unauthorized data exfiltration is detected, for example, a DLP solution could immediately block the action or quarantine the device from sending out the data, removing it from the network until an investigation team can take the next step. 2 – Data Classification

The IDC’s Global DataSphere predicts that by 2026, the worldwide amount of data created annually will exceed 200,000 exabytes, or 2 billion gigabytes. With that kind of volume, enterprises need a way to prioritize and label what needs protecting, and the prerequisite to that is to understand the risk accepted for not prioritizing data that may not make the cut for “most critical.” That’s where data classification comes in. Intelligence communities are great examples of organizations who have data classification mastered from years of experience and necessity. The United Kingdom, for example, publishes guidance on how to classify the government's information assets. With classifications ranging from OFFICIAL to SECRET to TOP SECRET, the United Kingdom specifies OFFICIAL to all routine public sector business such as health records to TOP SECRET where if the information was leaked, it would directly threaten the internal stability of the UK or friendly nations. This type of classification is manual—at least, as far as public knowledge indicates. Other types of data can be classified automatically. Data such as credit cards numbers, financial data, and social security numbers all follow a distinct pattern that can be found using regular expressions (regex), which matches specified patterns of characters. Take a look at the regex for credit card validation as an example. For sensitive data that lacks such a clearly defined structure, current research is being done using machine learning techniques to combine contextual metadata to identify and classify such data. 3 – Secure Collaboration

Data comes in three forms: data in use (active), data at rest (stored), and data in motion (being transferred). Most if not all enterprises need to transfer data, either within an organization for collaboration or externally to fulfill a client request. If an organization needs to send multiple files, encrypting the path between the two endpoints (a client and a server) is necessary. Much like how an armored money truck is secured with bulletproof material and additional guards, protocols such as HTTPS and the secure file transfer protocol (sFTP) ensure the data remains encrypted between two endpoints, the sender and the recipient. Once the money truck arrives at the destination though, ensuring the contents is in the right hands and safely protected is up to the endpoint. Making sure the right person accesses the transferred data is accomplished using end-to-end encryption which is not a guaranteed option in popular business collaboration tools such as messaging or virtual video meetings. Additionally, the actual device (i.e., laptop, server) must be protected as the best encryption in transit will not compensate for a compromised account.

Tying it together

A three-pillared approach of data loss prevention, data classification, and secure collaboration will provide your organization a solid defensive strategy for protecting critical data for operations and legal compliance. By reducing the risk of cybersecurity attacks and data breaches both malicious and unintentional, you can maintain your competitive edge and keep your company open for business. Read the full article

[solixtechnologies]

What is Cloud Data Management?

The rise of multi-cloud, data-first architecture and the broad portfolio of advanced data-driven applications that have arrived as a result require cloud data management systems to collect, manage, govern and build pipelines for enterprise data. Cloud data management architectures span private, multi-cloud and hybrid cloud environments connecting to data sources not just from transaction systems, but from file servers, the Internet or multi-cloud repositories.

The scope of cloud data management includes enterprise data lake, enterprise archiving, enterprise content services, and consumer data privacy solutions. These solutions manage the utility, risk and compliance challenges of storing large amounts of data.

Cloud data platforms

Cloud data platforms are the centrepiece of cloud data management programs and provide uniform data collection and data storage at the lowest cost. Archives, data lakes, and content services enable cloud migration projects to connect, ingest, and manage any type of data from any source. For instance, cloud data platforms collect legacy and real-time data from mainframes, ERP, CRM, file stores, relational and non-relational databases, and even SaaS environments like Salesforce or Workday.

Enterprise Archiving

Studies have shown that data is accessed less frequently as it ages. Current data such as online data is accessed most frequently, but after two years, most enterprise data is hardly ever accessed. As data growth accelerates the load on production infrastructure grows, and the challenge to maintain application performance increases.

Application portfolios should be screened regularly for legacy applications that are no longer in use and those applications should be retired or decommissioned. In addition historical data from production databases should be archived to improve performance, optimize infrastructure and reduce overall costs. Information Lifecycle Management (ILM) should be used to establish data governance and compliance controls.

Enterprise archiving supports all enterprise data including databases, streaming data, file servers and email. Using ILM, enterprise archiving moves less frequently accessed data from production systems to nearline repositories. The archive data remains highly accessible and is stored in low cost buckets. Large organizations operating silos of file servers across departments and divisions use enterprise archiving to consolidate these silos into a unified and compliant cloud repository.

Enterprise Data Lake

Data-driven enterprises leverage vast and complex networks of data and services, and enterprise data lakes deliver the connections necessary to move data from any source to any  target location. Enterprise data lakes handle very large volumes of data and scale horizontally using commodity cloud infrastructure to deliver data pipeline and data preparation services for downstream applications such as SQL data warehouse, artificial intelligence (AI) and machine learning (ML).

Data pipelines are a series of data flows where the output of one element is the input of the next one, and so on. Data lakes serve as the collection and access points in a data pipeline and are responsible for data organization and access control.

Data preparation makes data-fit-for-use with improved data quality. Data preparation services include data profiling, data cleansing, data enrichment and data transformation and data modeling. As an open source and industry standard solution, enterprise data lakes safely and securely collect and store large amounts of data for cloud migration, and provide enterprise grade services to explore, manage, govern, prepare and provide access control to the data.

Enterprise Content Services (ECS)

Corporate file shares are overflowing with files and long ago abandoned data. Enterprise Content Services collect and store historical enterprise data that would otherwise be spread out across various islands of storage, on personal devices, file shares, Google Drive, Dropbox, or personal OneDrives. Organizations planning cloud data migration to tackle content sprawl should consider ECS for secure and compliant file storage at the lowest cost. Cloud data migration with ECS consolidates enterprise data onto a single platform and unifies silos of file servers in innovative ways to become more efficient and reduce costs.

Consumer Data Privacy

Consumer data privacy regulations are proliferating with nearly 100 countries now adopting regulations. The California Consumer Privacy Act (CCPA) and Europe’s General Data Protection Regulation (GDPR) are perhaps the best known laws, but new regulations are on the rise everywhere as security breaches, cyberattacks and unauthorized releases of personal information continue to grow unabated. These new regulations mandate strict controls over the handing of personally identifiable information (PII), yet variations across geographies make legal compliance a complex requirement.

Information Lifecycle Management (ILM) manages data throughout its lifecycle and establishes a system of controls and business rules including data retention policies and legal holds. Security and privacy tools like data classification, data masking and sensitive data discovery help data administrators achieve compliance with data governance policies such as NIST 800-53, PCI, HIPAA, and GDPR. Consumer data privacy and data governance are not only essential for legal compliance, they improve data quality as well.

What’s The Urgency?

Exponential data growth is a known fact, however, the implications are only being felt by enterprises in the recent couple of years. On one end, more and more data is required to support data-driven applications and analytics. On the other end, data growth results in operational inefficiencies, technical debts and increased compliance risks. Data growth is a double-edged sword if left unmanaged and delivers great value by enabling enterprises to more effectively manage their data.