Mage Data

Tag: TDM

  • Are There Good Open Source Tools for Sensitive Data Discovery?

    Are There Good Open Source Tools for Sensitive Data Discovery?

    Open-source tools have come into their own in the past decade, including tools for sensitive data discovery. What used to be the domain of large corporations has been democratized, and teams of passionate people can (and do) develop amazing tools. However, with the ever-growing number of data privacy and security laws, the stakes around data classification have never been higher. Getting sensitive data discovery right has significant consequences…so it’s critical you understand what you’re getting with these tools, and how you can use them in ways that will keep you (and your customer and employee data) safe.

    What Makes Data Discovery Tools Open-Source?

    We’ve already covered what makes software open source in this article in depth , but we want to give a quick recap of what we’ll be discussing here. Unlike closed-source tools, free sensitive data discovery tools are released under a license allowing others to use and alter the software for their purposes freely. Generally, instead of being created and owned by a corporation, open-source software is developed by a passionate community, who collaborate to create new features and often determine future direction democratically.

    Many talented people are working on great open-source sensitive data discovery tools like OpenDataDiscovery, ReDiscovery, DataDefender, and more. Consequently, to answer the question in the title of the article, there are good open-source tools for sensitive data discovery. However, that’s not necessarily the question you should be asking—instead, you should be trying to determine if they’ll be right for your company. And one of the best ways to make that determination is through a SWOT Analysis, taking a detailed look at the Strengths, Weaknesses, Opportunities, and Threats that come from using open-source tools for data discovery.

    Data Discovery Tools: Strengths

    First up are the strengths—the things that open-source data discovery tools do well.

    Interoperability and Flexibility

    Because there are generally a variety of perspectives involved in open-source tools, there’s often little incentive to hide features and programs behind walled gardens. In this case, that often translates into tools with a wide range of integrations and connections for data. And even when a certain database type isn’t supported, these tools often provide a way for you to build the integration yourself, ensuring that getting data is rarely a roadblock.

    Price

    And, of course, the best price you can get for anything is free. That could mean you save a bit of money or free up resources to invest in areas that need it more. Whatever the case, it will be hard to get a better deal than what you get with open-source tools.

    Data Discovery Tools: Weaknesses

    Of course, no software is perfect. Here are some things open-source data discovery tools don’t always do well.

    Unknown Development Cycles

    Many B2B tools feature a regular and predictable development cycle. Some open-source projects are very organized, and others are less so. Regardless, there’s no guarantee that a feature or fix will come out on time—or even that there will be a roadmap to start with. The inherent unpredictability of the process can sometimes be frustrating.

    Enterprise Readiness

    As companies grow, their data environments become more complex at an exponential rate. Not all open-source data discovery tools can handle the complexity of a modern enterprise data environment. And of those that can, not all will be able to provide the detailed reporting and compliance options that companies need to meet their legal obligations.

    Data Discovery Tools: Opportunities

    With open-source tools, companies have some opportunities they wouldn’t necessarily have with paid tools.

    Opportunity to Influence Development

    As a user of an open-source tool, you’re part of the community developing it. While you still won’t have ultimate control over its development direction, you’ll likely have the ability to vote on next steps and generally have greater influence on the development process than you would over most paid tools. This can provide the opportunity to get the features you need faster than traditional development.

    Customization via Forking

    And if the community doesn’t prioritize your needs, you’re allowed to fork, or make a copy of, the underlying source code, allowing your company to continue development in the way it sees fit. That’s an option you’re typically never going to have with traditional software.

    Data Discovery Tools: Threats

    Of course, there are some downsides to open-source tools.

    Poor/Nonexistent Customer Support

    Because open-source tools are generally community-run projects where people work for free, customer support is not guaranteed. People, including other users, are often very helpful through online forums, but that often doesn’t rise to the same level of support you would get from just about any paid tool. And when you have a serious issue with your software, this problem can keep you from resolving it quickly. And as a reminder, 99 percent success in data discovery isn’t good enough, and could open you up to serious legal ramifications. If you’re having an issue with sensitive data discovery, failing to find a quick solution can be an expensive mistake.

    Rogue Developers

    While it’s unlikely that the developers of an open-source data discovery tool would insert malware or create serious security vulnerabilities, it’s not unheard of. But even if no one acts maliciously, there’s a real chance that the project will eventually be abandoned without warning. And abandoned software won’t receive security updates or new features and could leave you looking for a new solution once more.

    How Mage Data Helps with Sensitive Data Discovery

    If you’ve reached the end of the above SWOT analysis feeling that the strengths and opportunities far outweigh the weaknesses and risks, then there’s a good chance that there’s a great open-source sensitive data discovery tool out there for you. But that won’t be the case for all businesses. It doesn’t mean that the tools are bad, just that they are not a good fit for all business contexts.

    Remember that sensitive data discovery is the starting point of good data management. There are so many more things that need to be done to keep data safe and companies compliant. Here at Mage, we’ve developed a world-class AI-powered sensitive data discovery tool, that’s part of a larger suite of tools designed to manage data from discovery all the way to retirement. If that sounds more like what you need, sign up for a free consultation today to learn more about what Mage Data can do for you.

  • Data Security Platform: Securing the Digital Perimeter

    Data Security Platform: Securing the Digital Perimeter

    In today’s data-driven world, organizations face increasing challenges in protecting sensitive information while ensuring compliance with stringent data privacy regulations. The exponential growth of data has also led to a higher risk of unauthorized access, breaches and cyber-attacks being faced by organizations. In such a scenario, protecting sensitive information is a top priority for businesses, and the use of Data Security Platforms (DSP) has emerged as a crucial component in the battle against data threats. This article delves into the significance of a DSP, its role in compliance with data privacy regulations, and the common challenges faced during adoption.

    What is a Data Security Platform?

    A Data Security Platform is designed to protect sensitive and valuable data from unauthorized access, breaches and other security threats. Gartner defines Data Security Platforms (DSPs) as products and services characterized by data security offerings that target the integration of the unique protection requirements of data across data types, storage silos and ecosystems.

    Gartner, in their report “2023 Strategic Roadmap for Data Security Platform Adoption” lists 6 capabilities required for a Data Security Platform (Fig 1)

    Fig.1

    Let us go through each of these capabilities in detail:

    Data Discovery and Classification

    Data Discovery and Classification involves the automated scanning and analysis of an organization’s data repositories to identify and categorize sensitive data. This process helps organizations understand where sensitive information resides, such as personal identifiable information (PII), financial data, intellectual property, or other confidential data.

    The data classification process tags data with relevant labels indicating its sensitivity level and compliance requirements. For example, data might be classified as “Confidential,” “Internal Use Only,” or “Public.” This classification enables organizations to enforce appropriate access controls, data protection measures, and data handling policies based on the data’s sensitivity. It also aids in compliance with data protection regulations since organizations can ensure that sensitive data is treated according to the applicable laws.

    Data Access Controls

    Data Access Controls are mechanisms that ensure only authorized users have appropriate access to specific data. This component plays a vital role in preventing unauthorized access to sensitive information, reducing the risk of data breaches and insider threats.

    Role-based access control (RBAC) is a common approach in data security platforms, where permissions are assigned based on the user’s role within the organization. Access rights can be granted or revoked based on job functions, ensuring that users only have access to data they need to perform their tasks.

    Data Access Controls work hand-in-hand with the data classification process, as the access privileges are often determined based on the sensitivity level of the data. Strong access controls help ensure that data is only accessible to authorized individuals and minimize the risk of data leaks or unauthorized disclosures.

    Data Masking

    Data Masking is the process of concealing original sensitive data by replacing it with realistic but fictional data. The purpose of data masking is to create a structurally similar version of the data without revealing the actual information. This is particularly important for non-production environments like testing or development, where real data is not needed.

    Data Masking is commonly used to protect sensitive data while ensuring that applications and processes can still function realistically with representative data. This prevents the exposure of actual sensitive data during testing or other non-production activities, reducing the risk of data breaches resulting from mishandling or accidental leaks in lower-security environments.

    Database Encryption

    Database Encryption involves converting plaintext data into ciphertext using encryption algorithms, rendering the data unreadable and useless without the appropriate decryption key.

    At-rest encryption ensures that data stored on disk or in a database is protected even if physical storage media is compromised. In contrast, in transit encryption safeguards data as it is transmitted over networks, preventing eavesdropping or interception by unauthorized parties.

    Database encryption adds an extra layer of security, making it significantly harder for attackers to access sensitive data, even if they gain unauthorized access to the underlying infrastructure.

    Database Activity Monitoring

    Database Activity Monitoring (DAM) is a real-time surveillance mechanism that captures and records user activities and behaviors related to database access and usage. It tracks queries, data modifications, login attempts, and other interactions with the database.

    DAM helps detect suspicious or unauthorized activities, such as unauthorized attempts to access sensitive data or unusual data access patterns. When abnormal behavior is detected, the system can trigger alerts to security teams, enabling them to respond promptly to potential security threats and prevent data breaches.

    Data Risk Analytics

    Data Risk Analytics involves the use of advanced analytics and machine learning techniques to assess security risks associated with an organization’s data environment. By analyzing patterns, trends, and historical data, this component can identify potential vulnerabilities and predict security risks before they escalate.

    Data Risk Analytics helps security teams gain insights into potential data security issues, such as weak access controls, suspicious user behaviors, or unsecured data repositories. These insights enable organizations to take proactive measures to strengthen their overall data security posture and mitigate potential risks before they lead to security incidents or data breaches.

    The Advantages of a Data Security Platform (DSP)

    In an era where data breaches and privacy concerns dominate headlines, organizations need to fortify their data security measures across the entire enterprise data landscape to safeguard their reputation, build customer trust, and sustain financial stability. A Data Security Platform (DSP) provides a centralized approach to data security, enabling businesses to efficiently manage data protection across various systems and applications. It serves as a comprehensive solution that comprises various components enabling data security across the sensitive data lifecycle. . By adopting a DSP, organizations can

    Figure 2

    Ensuring Compliance with Data Privacy Regulations

    The implementation of a DSP significantly aids organizations in complying with various data privacy regulations:

    GDPR Compliance

    The GDPR mandates stringent data protection measures, including data minimization, purpose limitation, and user consent management. A DSP helps organizations meet these requirements by implementing encryption, access controls, and consent management mechanisms.

    CCPA and Other Privacy Regulations

    The California Consumer Privacy Act (CCPA) and similar regulations empower individuals with greater control over their personal information. A DSP enables organizations to manage user preferences, handle data subject requests, and maintain auditable records for compliance.

    Emerging Regulations

    As new privacy regulations continue to emerge globally, a DSP provides a future-proof solution by offering flexibility and scalability to adapt to evolving compliance requirements. This ensures organizations can stay ahead of the regulatory curve.

    Overcoming Challenges during DSP Adoption

    While adopting a DSP offers significant advantages, organizations may face certain challenges:

    Integration Complexity

    Integrating a DSP with existing IT infrastructure and applications can be complex. To overcome this challenge, organizations should carefully plan the integration process, seek vendor support, and collaborate closely with IT teams to ensure a seamless deployment.

    Employee Training and Awareness

    The successful adoption of a DSP depends on the knowledge and awareness of employees. Organizations should invest in comprehensive training programs to educate employees about the DSP’s functionalities, data protection best practices, and the importance of compliance.

    Balancing Security and Usability

    Organizations may face the challenge of balancing data security measures with usability and productivity. It is crucial to strike the right balance by implementing security controls that protect data without hindering operational efficiency.

    Keeping Pace with Changing Regulations

    Data privacy regulations continue to evolve, necessitating ongoing monitoring and updates to the DSP. Organizations should stay informed about regulatory changes, actively engage with legal and compliance experts, and collaborate with the DSP vendor to ensure the platform remains up to date.

    Conclusion

    In an era where data security and compliance with privacy regulations are critical, a Data Security Platform (DSP) emerges as a comprehensive solution for organizations. By adopting a DSP, organizations can fortify their data security measures, ensure compliance with regulations, and mitigate the risks associated with data breaches. Although challenges may arise during adoption, proactive planning, employee training, and ongoing monitoring can help organizations overcome them and achieve data security excellence in today’s complex digital landscape.

    At Mage Data, we focus our efforts on empowering organizations with the tools and technologies to secure their data throughout its lifecycle – from creation and storage to processing and transmission. With Mage Data, you get access to a Data Security Platform that has been ranked as the Gartner Peer Insights Customer’s Choice for 3 years in a row and has also been named as an Overall Leader for Data Security Platforms by KuppingerCole. If you’re on the lookout for a comprehensive Data Security Platform that meets your organization’s IT strategic goals, feel free to reach out.