Mage Data

Category: Blogs – Database Firewall

  • Reimagining Test Data: Secure-by-Design Database Virtualization

    Reimagining Test Data: Secure-by-Design Database Virtualization

    Enterprises today are operating in an era of unprecedented data velocity and complexity. The demand for rapid software delivery, continuous testing, and seamless data availability has never been greater. At the same time, organizations face growing scrutiny from regulators, customers, and auditors to safeguard sensitive data across every environment—production, test, or development.

    This dual mandate of speed and security is reshaping enterprise data strategies. As hybrid and multi-cloud infrastructures expand, teams struggle to provision synchronized, compliant, and cost-efficient test environments fast enough to keep up with DevOps cycles. The challenge lies not only in how fast data can move, but in how securely it can be replicated, masked, and managed.

    Database virtualization was designed to solve two of the biggest challenges in Test Data Management—time and cost. Instead of creating multiple full physical copies of production databases, virtualization allows teams to provision lightweight, reusable database instances that share a common data image. This drastically reduces storage requirements and accelerates environment creation, enabling developers and QA teams to work in parallel without waiting for lengthy data refresh cycles. By abstracting data from its underlying infrastructure, database virtualization improves agility, simplifies DevOps workflows, and enhances scalability across hybrid and multi-cloud environments. In short, it brings speed and efficiency to an otherwise resource-heavy process—freeing enterprises to innovate faster.

    Database virtualization was introduced to address inefficiencies in provisioning and environment management. It promised faster test data creation by abstracting databases from their underlying infrastructure. But for many enterprises, traditional approaches have failed to evolve alongside modern data governance and privacy demands.

    Typical pain points include:

    • Storage-Heavy Architectures: Conventional virtualization still relies on partial or full data copies, consuming vast amounts of storage.
    • Slow, Manual Refresh Cycles: Database provisioning often depends on DBAs, leading to delays, inconsistent refreshes, and limited automation.
    • Fragmented Data Privacy Controls: Sensitive data frequently leaves production unprotected, exposing organizations to compliance violations.
    • Limited Integration: Many solutions don’t integrate natively with CI/CD or hybrid infrastructures, making automated delivery pipelines cumbersome.
    • Rising Infrastructure Costs: With exponential data growth, managing physical and virtual copies across clouds and data centers drives up operational expenses.

    The result is an environment that might be faster than before—but still insecure, complex, and costly. To thrive in the AI and automation era, enterprises need secure-by-design virtualization that embeds compliance and efficiency at its core.

    Modern data-driven enterprises require database virtualization that does more than accelerate. It must automate security, enforce privacy, and scale seamlessly across any infrastructure—cloud, hybrid, or on-premises.

    This is where Mage Data’s Database Virtualization (DBV) sets a new benchmark. Unlike traditional tools that treat masking and governance as secondary layers, Mage Data Database Virtualization builds them directly into the virtualization process. Every virtual database created is masked, compliant, and policy-governed by default—ensuring that sensitive information never leaves production unprotected.

    Database Virtualization lightweight, flexible architecture enables teams to provision virtual databases in minutes, without duplicating full datasets or requiring specialized hardware. It’s a unified solution that accelerates innovation while maintaining uncompromising data privacy and compliance.

    1. Instant, Secure Provisioning
      Create lightweight, refreshable copies of production databases on demand. Developers and QA teams can access ready-to-use environments instantly, reducing cycle times from days to minutes.
    2. Built-In Data Privacy and Compliance
      Policy-driven masking ensures that sensitive data remains protected during every clone or refresh. Mage Data Database Virtualization is compliance-ready with frameworks like GDPR, HIPAA, and PCI-DSS, ensuring enterprises maintain regulatory integrity across all environments.
    3. Lightweight, Flexible Architecture
      With no proprietary dependencies or hardware requirements, Database Virtualization integrates effortlessly into existing IT ecosystems. It supports on-premises, cloud, and hybrid infrastructures, enabling consistent management across environments.
    4. CI/CD and DevOps Integration
      DBV integrates natively with Jenkins, GitHub Actions, and other automation tools, empowering continuous provisioning within DevOps pipelines.
    5. Cost and Operational Efficiency
      By eliminating full physical copies, enterprises achieve up to 99% storage savings and dramatically reduce infrastructure, cooling, and licensing costs. Automated refreshes and rollbacks further cut
      manual DBA effort.
    6. Time Travel and Branching (Planned)
      Upcoming capabilities will allow enterprises to rewind databases or create parallel branches, enabling faster debugging and parallel testing workflows.

    The AI-driven enterprise depends on speed—but the right kind of speed: one that doesn’t compromise security or compliance. Mage Data Database Virtualization delivers precisely that. By uniting instant provisioning, storage efficiency, and embedded privacy, it transforms database virtualization from a performance tool into a strategic enabler of governance, innovation, and trust.

    As enterprises evolve to meet the demands of accelerating development, they must modernize their entire approach to data handling—adapting for an AI era where agility, accountability, and assurance must coexist seamlessly.

    Mage Data’s Database Virtualization stands out as the foundation for secure digital transformation—enabling enterprises to accelerate innovation while ensuring privacy and compliance by design.

  • Building Trust in AI: Strengthening Data Protection with Mage Data

    Building Trust in AI: Strengthening Data Protection with Mage Data

    Artificial Intelligence is transforming how organizations analyze, process, and leverage data. Yet, with this transformation comes a new level of responsibility. AI systems depend on vast amounts of sensitive information — personal data, intellectual property, and proprietary business assets — all of which must be handled securely and ethically.

    Across industries, organizations are facing a growing challenge: how to innovate responsibly without compromising privacy or compliance. The European Commission’s General-Purpose AI Code of Practice (GPAI Code), developed under the EU AI Act, provides a structured framework for achieving this balance. It defines clear obligations for AI model providers under Articles 53 and 55, focusing on three key pillars — Safety and Security, Copyright Compliance, and Transparency.

    However, implementing these requirements within complex data ecosystems is not simple. Traditional compliance approaches often rely on manual audits, disjointed tools, and lengthy implementation cycles. Enterprises need a scalable, automated, and auditable framework that bridges the gap between regulatory expectations and real-world data management practices.

    Mage Data Solutions provides that bridge. Its unified data protection platform enables organizations to operate compliance efficiently — automating discovery, masking, monitoring, and lifecycle governance — while maintaining data utility and accelerating AI innovation.

    The GPAI Code establishes a practical model for aligning AI system development with responsible data governance. It is centered around three pillars that define how providers must build and manage AI systems.

    1. Safety and Security
      Organizations must assess and mitigate systemic risks, secure AI model parameters through encryption, protect against insider threats, and enforce multi-factor authentication across access points.
    2. Copyright Compliance
      Data sources used in AI training must respect intellectual property rights, including automated compliance with robots.txt directives and digital rights management. Systems must prevent the generation of copyrighted content.
    3. Transparency and Documentation
      Providers must document their data governance frameworks, model training methods, and decision-making logic. This transparency ensures accountability and allows regulators and stakeholders to verify compliance.

    These pillars form the foundation of the EU’s AI governance model. For enterprises, they serve as both a compliance obligation and a blueprint for building AI systems that are ethical, explainable, and secure.

    Mage Data’s platform directly maps its data protection capabilities to the GPAI Code’s requirements, allowing organizations to implement compliance controls across the full AI lifecycle — from data ingestion to production monitoring.

    GPAI Requirement

    Mage Data Capability

    Compliance Outcome

    Safety & Security (Article 53)

    Sensitive Data Discovery

    Automatically identifies and classifies sensitive information across structured and unstructured datasets, ensuring visibility into data sources before training begins.

    Safety & Security (Article 53)

    Static Data Masking (SDM)

    Anonymizes training data using over 60 proven masking techniques, ensuring AI models are trained on de-identified yet fully functional datasets.

    Safety & Security (Article 53)

    Dynamic Data Masking (DDM)

    Enforces real-time, role-based access controls in production systems, aligning with Zero Trust security principles and protecting live data during AI operations.

    Copyright Compliance (Article 55)

    Data Lifecycle Management

    Automates data retention, archival, and deletion processes, ensuring compliance with intellectual property and “right to be forgotten” requirements.

    Transparency & Documentation (Article 55)

    Database Activity Monitoring

    Tracks every access to sensitive data, generates audit-ready logs, and produces compliance reports for regulatory or internal review.

    Transparency & Accountability

    Unified Compliance Dashboard

    Provides centralized oversight for CISOs, compliance teams, and DPOs to manage policies, monitor controls, and evidence compliance in real time.

    By aligning these modules to the AI Code’s compliance pillars, Mage Data helps enterprises demonstrate accountability, ensure privacy, and maintain operational efficiency.

    Mage Data enables enterprises to transform data protection from a compliance requirement into a strategic capability. The platform’s architecture supports high-scale, multi-environment deployments while maintaining governance consistency across systems.

    Key advantages include:

    • Accelerated Compliance: Achieve AI Act alignment faster than traditional, fragmented methods.
    • Integrated Governance: Replace multiple point solutions with a unified, policy-driven platform.
    • Reduced Risk: Automated workflows minimize human error and prevent data exposure.
    • Proven Scalability: Secures over 2.5 billion data rows and processes millions of sensitive transactions daily.
    • Regulatory Readiness: Preconfigured for GDPR, CCPA, HIPAA, PCI-DSS, and EU AI Act compliance.

    This integrated approach enables security and compliance leaders to build AI systems that are both trustworthy and operationally efficient — ensuring every stage of the data lifecycle is protected and auditable.

    Mage Data provides a clear, step-by-step plan:

    This structured approach takes the guesswork out of compliance and ensures organizations are always audit-ready

    The deadlines for AI Act compliance are approaching quickly. Delaying compliance not only increases costs but also exposes organizations to risks such as:

    • Regulatory penalties that impact global revenue.
    • Data breaches harm brand trust.
    • Missed opportunities, as competitors who comply early gain a reputation for trustworthy, responsible AI.

    By starting today, enterprises can turn compliance from a burden into a competitive advantage.

    The General-Purpose AI Code of Practice sets high standards but meeting them doesn’t have to be slow or costly. With Mage Data’s proven platform, organizations can achieve compliance in weeks, not years — all while protecting sensitive data, reducing risks, and supporting innovation.

    AI is the future. With Mage Data, enterprises can embrace it responsibly, securely, and confidently.

    Ready to get started? Contact Mage Data for a free compliance assessment and see how we can help your organization stay ahead of the curve.

  • What is a Zero-Trust Security Model?

    What is a Zero-Trust Security Model?

    Traditional computer security models ensure that people without the proper authorization cannot access an organization’s network. However, a single set of compromised login credentials can lead to a breach of the entire network.

    A Zero-Trust Security Model goes some way to solving this problem by requiring users to continually verify their identity, even if they’re already inside the secure digital perimeter. This approach restricts users to the minimum amount of information necessary to do their job. In the event of a breach, hackers will find it difficult or impossible to move laterally through a network and gain access to more information.

    A Zero-Trust Security Model doesn’t mean that you don’t trust the people you’re sharing data with. Instead, a zero-trust security model implements checkpoints throughout a system so you can be confident that your trust in each user is justified.

    What is a Zero-Trust Security Model?

    Imagine for a moment that a computer network is like a country. In a traditional security model, the country would have border checkpoints around its perimeter. Employees who presented the correct login info would be allowed to enter, and bad actors trying to gain access would be kept outside.

    While this is a good idea in theory, in practice, problems emerge. For example, bad actors who breached the perimeter would get much or all of the information inside the network. Likewise, employees who are past the first barrier may gain access to documents or other information that they shouldn’t see.

    These problems with the traditional model of cybersecurity drove the U.S. Department of Defense to adopt a new strategy in the early 2000s. Those responsible for network security treated their systems as though they had already been breached, and then asked the question: “Given that the system has been breached, how do we limit the collateral damage?”

    To meet that objective, they developed an approach that required users, consisting of both humans and machines, to continually prove that they were allowed to be present every time they attempted to access a new resource. To return to our metaphor from earlier, employees would have to show ID at the country’s border, and show ID every time they tried to access a new building, which in this example represents resources within the system. This approach meant that bad actors would find it harder to move through the system with a single breach, and also made it easy to restrict employees to the appropriate areas in the network based on their security clearance.

    Zero-Trust Security Comes of Age

    The external and internal benefits of a Zero-Trust Security Model quickly became clear to the private sector, too. While many businesses adapted the system for their own use, or offered it as a service to others, it wasn’t until August 2020 that the National Institute of Standards and Technology (NIST) released the first formal specification for Zero-Trust Security Model implementation.

    NIST Special Publication 800-207 details how to implement a Zero-Trust Architecture (ZTA) in a system. The Seven Tenets of Zero Trust form the core of this approach.

    1. All data sources and computing services are resources
    2. All communication is secured regardless of network location
    3. Access to individual enterprise resources is granted on a per-session basis
    4. Access to resources is determined by a dynamic policy
    5. The enterprise monitors and measures the integrity and security posture of all owned and associated assets
    6. All resource authentication and authorization are dynamic and strictly enforced before access is allowed
    7. The enterprise collects as much information as possible about the current state of assets, network infrastructure, and communications and uses that information to improve its security posture

    Of these seven tenets, two especially speak to what’s different between ZTA and more traditional approaches. Session-based access (#3) means that access permissions are reevaluated each time a new resource is accessed, or if sufficient time has passed between access requests to the same resource. This approach reduces the potential for bad actors to exploit lost devices or gain access through an unattended workstation.

    Dynamic policy controls (#6) look beyond user credentials, such as a username and password. For example, a dynamic policy may also consider other factors such as the type of device, what network it is on, and possibly previous activity on the network to determine if the request is legitimate. This kind of observation improves detection of external malicious actors, even when the correct login credentials are provided.

    Access control is run through a Policy Decision Point. The Policy Decision Point is composed of a Policy Engine, which holds the rules for granting access, and the Policy Administrator, which carries out the allowance or disallowance of access to resources when a request is made.

    Benefits of Zero-Trust Security

    Many powerful benefits emerge when a system is set up to align with ZTA standards. Arguably, the most important of these is the compartmentalization of system resources. When resources are compartmentalized, hackers who gain access to one area of your network won’t gain access to other resources. For example, a breached email account wouldn’t give the hacker access to your project documentation or financial systems.

    Compartmentalization also holds benefits for managing your employees. With a compartmentalized system, you won’t have to and shouldn’t give your employees access to more resources than they need to do their jobs. This approach reduces the risk of the employee intentionally or accidentally viewing sensitive information. Compartmentalization also minimizes the damage done by leaks, as employees generally won’t have access to documentation beyond their immediate needs.

    Because a core policy of ZTA is the continuous collection of data about how each user behaves on the network, it becomes far easier to spot breaches. In many cases, organizations with ZTA systems detect breaches not because of failed authentication but rather because a feature of the access request, such as location, time, or type of resource requested, differs from regular operation and is flagged by the Policy Decision Point. For example, a request for a resource from Utah to a server for a company based in Virginia would raise flags, even if a bad actor provided a valid username and password.

    Zero-Trust Security Model Integration

    While Zero-Trust Security Models hold many benefits for many companies, it’s essential to acknowledge that it’s not a “plug-and-play” system. The approach differs significantly from traditional security practices. Most companies will need a total overhaul of their network to apply it. That can be a disruptive process and will likely lower productivity in the short term as new systems are implemented, and employees adapt to the new policies.

    That doesn’t make moving to a Zero-Trust system the wrong choice, but it does mean that the transition has some tradeoffs. However, if you’re looking for the absolute best industry standard for security, Zero-Trust is the way to go.

    If you’re contemplating increasing your security, you need to know exactly what data you’ll be securing. Mage Data helps organizations find and catalog their data, including highlighting Personally Identifiable Information, which you’d want to provide an extra layer of security to in a Zero-Trust system. Schedule a demo today to see what Mage Data can do to help your organization better secure its data.