In a world where data breaches and privacy threats are the norm, safeguarding sensitive information is no longer optional—it's critical. As regulations tighten and privacy concerns soar, our customers are demanding cutting-edge solutions that don't just secure their data but do so with finesse. Enter Format Preserving Encryption (FPE). When paired with ALTR's capability to seamlessly share encryption keys with trusted third parties via platforms like Snowflake's data sharing, FPE becomes a game-changer.
Format Preserving Encryption (FPE) is a type of encryption that ensures the encrypted data retains the same format as the original plaintext. For example, if a credit card number is encrypted using FPE, the resulting ciphertext will still appear as a string of digits of the same length. This characteristic makes FPE particularly useful in scenarios where maintaining data format is crucial, such as legacy systems, databases, or applications requiring data in a specific format.
FPE maintains the data format, allowing easy integration into existing data pipelines without requiring significant changes. This minimizes the impact on business operations and reduces the costs associated with implementing encryption.
Many regulatory frameworks, such as the GDPR, PCI-DSS, and HIPAA, mandate the protection of sensitive data. FPE helps organizations comply with these regulations by ensuring that data is encrypted to preserve its usability and format, which can sometimes be a requirement in these standards.
Unlike traditional encryption methods, FPE allows encrypted data to be used in its existing form for specific operations, such as searches, sorting, and indexing. This ensures organizations can continue to derive value from their data without compromising security.
Snowflake is a cloud-based data warehousing platform that allows organizations to store, process, and analyze large volumes of data. One of its differentiating features is data sharing, which enables companies to share live, governed data with other Snowflake accounts in a secure and controlled manner while also shifting the cost of the computing operations of the data over to the share's consumer.
Snowflake's data sharing allows recipients to access shared data in real-time, ensuring they always have the most up-to-date information. This is particularly valuable in scenarios where timely access to data is critical, such as in financial services or healthcare.
Snowflake's platform is designed with security at its core. Data sharing is governed by robust access controls, ensuring only authorized parties can view or interact with the shared data. This is crucial for maintaining the confidentiality and integrity of sensitive information.
Snowflake's architecture allows for easy scalability, enabling organizations to share large volumes of data with multiple parties without compromising performance. Additionally, the platform supports a wide range of data formats and types, making it suitable for diverse use cases.
When FPE is combined with the ability to share encryption keys via Snowflake's data sharing, it unlocks a new level of security and flexibility for organizations. This combination addresses several critical challenges in data protection and sharing:
By leveraging FPE, organizations can encrypt sensitive data while preserving its format. However, there are scenarios where this encrypted data needs to be shared with trusted third parties, such as partners, auditors, or service providers. Through Snowflake's data sharing and ALTR's FPE Key Sharing, companies can securely share encrypted data along with the corresponding encryption keys. This allows the third party to decrypt the data within the policies that they have defined and use it as needed.
In a multi-cloud or hybrid environment, data often needs to be moved between different systems or shared with external entities. Traditional encryption methods can be cumbersome in such scenarios, as they require extensive reconfiguration or critical management efforts. However, with FPE and Snowflake's key sharing, organizations can seamlessly share encrypted data across different environments without compromising security. The encryption keys can be securely shared via Snowflake, ensuring only authorized parties can decrypt and access the data.
Many regulations require organizations to demonstrate that they have implemented appropriate security measures to protect sensitive data. By using FPE, companies can encrypt data that complies with these regulations. At the same time, the ability to share encryption keys through Snowflake ensures that data can be securely shared with auditors or regulators. Additionally, Snowflake's robust logging and auditing capabilities provide a detailed record of who accessed the data and when which is essential for compliance reporting.
In finance, healthcare, and retail industries, collaboration with external partners is often essential. However, sharing sensitive data with these partners presents significant security risks. By combining FPE with ALTR's key sharing, organizations can securely share encrypted data with partners, ensuring that sensitive information is transmitted throughout the data's lifecycle, including across shares. This enables more effective collaboration without compromising data security.
Specific data processing tasks, such as data analytics or AI model training, require access to large volumes of data. In scenarios where this data is sensitive, encryption is necessary. However, traditional encryption methods can hinder the efficiency of these tasks due to the need for decryption before processing. With FPE, the data can remain encrypted during processing, while ALTR's key sharing allows the consumer to decrypt data only when absolutely necessary. This ensures that data processing is both secure and efficient.
To better understand the value of combining FPE with ALTR's key sharing, let's explore a few use cases:
In the financial sector, organizations handle a vast amount of sensitive data, including customer information, transaction details, and credit card numbers. FPE can encrypt this data while preserving its format, ensuring it can still be used in legacy systems and applications. Through Snowflake's data sharing, financial institutions can securely share encrypted transaction data with external auditors, partners, or regulators, along with the necessary encryption keys. This ensures compliance with regulations while maintaining the security of sensitive information.
Healthcare organizations often need to share patient data with external entities, such as insurance companies or research institutions. FPE can encrypt patient records, ensuring they remain secure while preserving the format required for healthcare applications. Snowflake's data sharing allows healthcare providers to securely share this encrypted data with third parties. At the same time, ALTR enables the sharing of the corresponding encryption keys, enabling them to access and use the data while ensuring compliance with HIPAA and other regulations.
Retailers often need to share customer data with marketing partners, payment processors, or logistics providers. FPE can be used to encrypt customer information, such as names, addresses, and payment details while maintaining the format required for retail systems. Snowflake's data sharing enables retailers to securely share this encrypted data with their partners; with ALTR, the encryption keys are also shared, ensuring that customer information is always protected.
The combination of Format Preserving Encryption and ALTR's key-sharing capabilities represents a significant advancement in the field of data security. This approach addresses several critical challenges in data protection and sharing by enabling organizations to securely share encrypted data with trusted third parties.
In an increasingly interconnected world, businesses must collaborate with external partners and share data to remain competitive. However, this collaboration often comes with significant security risks. By leveraging FPE and ALTR's key sharing, organizations can strengthen trust with their partners by ensuring that sensitive data is always protected, even when shared. This leads to more effective and secure collaboration, ultimately driving business success.
Data breaches, including financial losses, reputational damage, and regulatory penalties, can devastate businesses. Organizations can significantly reduce the risk of data breaches by encrypting sensitive data with FPE and securely sharing it via Snowflake. Even if the data is intercepted, it remains protected, as only authorized parties with the corresponding encryption keys can decrypt it.
As organizations continue to innovate and leverage new technologies, such as artificial intelligence and machine learning, the need for secure data sharing will only grow. The combination of FPE and ALTR's key sharing enables businesses to securely share and process data innovatively without compromising security. This ensures that organizations can continue to innovate while protecting their most valuable asset – their data.
Integrating Format Preserving Encryption with ALTR's key sharing capabilities offers a powerful solution for organizations seeking to protect sensitive data while enabling secure collaboration and innovation. By preserving the format of encrypted data and allowing for secure key sharing, this approach addresses critical challenges in data protection, regulatory compliance, and data sharing across multiple environments. As businesses navigate the complexities of the digital age, the value of this combined solution will only become more apparent, making it a vital component of any robust data security strategy.
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“Today is the day!” you exclaim to yourself as you settle into your desk on Monday morning. After months of meticulous planning, the migration from Teradata to Snowflake begins now. You have been through all the back-and-forth with leadership on why this migration is needed: Teradata is expensive, Teradata is not agile, Snowflake creates a single source of data truth, and Snowflake is instantly on and scales when you need it. It’s perfect for you and your business.
As you follow your meticulously planned checklist for the migration, you're utilizing cutting-edge tools like DBT, Okta, and Sigma. These tools are not just cool, they're the future. You're moving your database structure, loading the initial non-sensitive data, repointing your ETL pipelines, and witnessing the power of modern technology in action. Everything is working like a charm.
A few weeks or months of testing go by, your downstream consumers of data are still using Teradata but are starting to give thumbs up on the Snowflake workloads that you have already migrated. Things are going well. You have not thought about CPU or disk space for the Teradata box in a while, which was the point of the migration. You finally get word from all stakeholders that this trial migration was a success! You call your Snowflake team, and tell them to back up the truck, you are clear to move the remaining workloads. Life is good. But then, comes a knock at the door.
It’s Pat from Security & Risk. You know Pat well and enjoy Pat’s company, but you also do as much as possible to avoid Pat because you are in data and, well, we all know the feeling. Pat tells you, “Heard we are finally getting off Teradata; that’s awesome! Do you have a plan for the PII and SSNs that are kept in that one Teradata database that we require using Protegrity for audit and compliance reasons?” You nod, “I do, but I couldn't do it without your expertise. I’ve been reading the Snowflake documentation, and I'm in the process of writing a few small AWS Lambdas to interface with Protegrity. Your input is crucial to this process.” Pat smiles, gives a non-assuring hand on your back and walks out. Phew, no more Pat.
Four weeks later, you're utterly exhausted. You've logged over 50 hours in Snowflake with fellow data engineers, and tapped into the expertise of one of the cloud ops team members who knows Lambda inside out. You have escalated to Snowflake support, but your external function calls from Snowflake to AWS keep timing out. AWS support is unable to help. Now, you have memory limits being hit with AWS Lambda. Suddenly, the internal network team does not want to keep the ports open to hit Protegrity from AWS, and you need to use a Private Link connection with additional security controls. You are behind on the Teradata migrations. There is no end in sight of the scale problems. Shoot, this is not working.
Don’t worry, you are not alone. This is the same experience felt by hundreds of Snowflake customers, and it stems from the same problem: everything about your Snowflake migration was planned for the new architecture of Snowflake except for one thing: data protection. You followed all the blogs and user guides, and your stateless data pipeline feeding Snowflake with a Kafka bus is perfect. Sigma is running without limits. The team is happy, but they want that customer data now. Except, you can’t use it until you solve this security problem.
Snowflake and OLAP workloads, generally, turned data protection on its head. OLTP workloads are easy to secure. You know the access points and the typical pattern of user behavior, so you can easily plan for scale and up-time. OLAP is widely unpredictable. Large queries, small queries, ten rows, 10M rows, it’s a nightmare for security. There is only one path forward: you must get purpose-built data protection for Snowflake.
You need a data protection solution that matches Snowflake’s architecture, just like when you matched Protegrity to Teradata. If Snowflake is going to be elastic, your data protection needs to be elastic. If Snowflake is going to be accessed by many downstream consumers, you need to be able to integrate data protection into the access policies in Snowflake. Who is going to do that work? Who will maintain this code? How can you control costs? The answer to all those questions is ALTR.
ALTR’s purpose-built native app for data protection is an easy solution for Snowflake. You can install it on your own. You can use your Snowflake committed dollars to pay for the service. ALTR’s data protection scale is controlled by Snowflake and nothing else. It’s the easiest way to get back on track. Call your Snowflake team, ask them about ALTR. It will feel good walking back into Pat’s office with your head held high and your data migration back on track.
Whether your team currently has Protegrity or Voltage, you will face the same problems. Do not waste your time trying to get these solutions to scale, just call ATLR.
Don’t just take my word for it…
In a world where data is the lifeblood of organizations, managing and securing that data is no longer just an IT task—it's a business imperative. Yet, despite the critical nature of data governance, many solutions out there are still bogged down by complexity, time-consuming processes, and significant risks. Enter ALTR, the cutting-edge solution that’s not just simplifying data governance but revolutionizing it. Here’s why ALTR is the game-changer your organization needs, and why it’s quickly becoming the go-to for companies leveraging Snowflake.
Data governance has long been a domain reserved for the technically savvy, with traditional methods requiring extensive SQL coding and intricate configurations. This not only made the process time-consuming but also left it vulnerable to human error. ALTR redefines this narrative with a user-centric approach. Seamlessly integrated into Snowflake and instantly accessible through Snowflake Partner Connect, ALTR is designed to be up and running in mere minutes. The intuitive interface, built on ALTR’s robust Management API, empowers even non-technical users to accomplish tasks that once required days of coding—now achievable with just a few clicks. ALTR democratizes data governance, making it fast, simple, and accessible to all, ensuring that security is no longer a complex or exclusive domain, but one that everyone can master.
In the high-speed world of data, there's simply no room for delay. ALTR recognizes this need for urgency, delivering Proof of Value and Time to Value in a matter of hours and days—rather than the months or quarters typical of traditional solutions. With ALTR’s SaaS model, you can unlock its features in your Snowflake sandbox environment at no cost, letting you experience its power before making any commitments.
But ALTR doesn’t just stop at providing tools; we empower you with expertise. Our team of Field Engineers is ready to assist in crafting tailored solutions, automating processes, and ensuring seamless interoperability with your data catalogs, ETL/ELT tools, and SIEMs. Customers often leverage ALTR’s Rapid POC Framework, which accelerates the definition of use case requirements and success criteria. Over just two or three focused one-hour screenshare sessions with an ALTR Field Engineer, you’ll produce the artifacts, evidence, and performance metrics needed to confidently move toward full-scale implementation.
It’s not merely about demonstrating ALTR’s value—it’s about ensuring you realize that value at lightning speed, setting your team up for swift, scalable success.
In the realm of data governance and security, complexity is the silent killer. The more convoluted your protocols, the more they drain your resources—whether that’s time, money, or manpower. ALTR was engineered to dismantle this complexity from the ground up. Managing access policies across multiple access points like SNOW SQL, BI Tools, applications, and data shares is an overwhelming task on its own. When you add the intricacies of data security within Snowflake’s ecosystem, the challenge becomes even more daunting. ALTR alleviates these burdens by enabling automation at scale and empowering users to handle these tasks with ease. By simplifying these traditionally complex processes, ALTR doesn't just reduce friction; it eliminates the barriers that have historically plagued data governance. With ALTR, complexity is no longer an obstacle—it's a thing of the past.
Human error is the Achilles’ heel of data security. From misconfigurations to overlooked details, the potential for mistakes is vast. Recent Snowflake Security incidents serve as stark reminders of these risks. ALTR addresses this vulnerability head-on. By eliminating the need for manual SQL scripting and enabling point-and-click automation, ALTR significantly reduces the risk of human error. But it doesn’t stop there. ALTR’s advanced Data Protection features—such as Format-Preserving Encryption and Tokenization—ensure that your data is protected at rest, in transit, and even in use. Coupling this with access policy automation means your data is safe from external threats, internal misuse, and even potential risks from privileged users.
In today’s data-driven world, interoperability isn’t just a nice-to-have; it’s essential. ALTR’s SaaS architecture is designed to work seamlessly within your existing data ecosystem and InfoSec stack, making it an ideal partner for your CISO’s peace of mind. Whether it’s leveraging Snowflake Object Tags or integrating with your SIEM, SOAR, or workflow resolution software, ALTR ensures everything works together flawlessly. By making real-time Data Activity Monitoring logs, policy alerts, and notifications available within your existing systems, ALTR takes interoperability to the next level, ensuring that your data governance is as efficient as it is secure.
ALTR is not just another data governance tool—it’s a revolution in how data is managed and secured. By focusing on ease of use, rapid proof of value, reduced complexity, minimized risk, and seamless interoperability, ALTR is setting a new standard in the industry. For companies leveraging Snowflake, ALTR is the key to unlocking the full potential of their data while safeguarding it from the ever-present threats of today’s digital landscape. In a world where data is king, ALTR is the crown. Don’t just manage your data security—master it with ALTR.
Keeping a tight grip on data access control is crucial for protecting sensitive information. However, when these systems get too complicated, they can bring about a whole host of challenges and additional risks. If you're finding that your data access control is more headache than help, it might be time to take a closer look. Let's explore ten signs that your data access control might be overly complex and explore some practical solutions to help streamline and strengthen your data security approach.
Are you experiencing persistent configuration errors? This may indicate an overly complex data access control system. These errors often arise from the intricate setup and continuous and manual adjustments needed to manage permissions. When systems require detailed and specific configurations, even minor mistakes can lead to significant vulnerabilities. Frequent misconfigurations are a security risk and a drain on resources, necessitating constant oversight and corrections.
Is your team struggling to respond to access requests or security incidents promptly? This suggests your system is too convoluted. The more complex the system, the harder it is for security teams to act swiftly and efficiently. Complex workflows and multiple layers of approval can slow down response times, increasing the risk of security breaches going undetected or unaddressed for extended periods.
Excessive resources spent on maintaining and updating access controls indicate unnecessary complexity. High maintenance costs often stem from the need for specialized training and continuous updates to keep the system running smoothly. These costs add up quickly, diverting funds and constrained resources from other critical areas, making the system financially unsustainable over the long term.
Are you using multiple tools to manage access control? This can create redundancies, integration issues and make the system harder to manage and more expensive to maintain. Each tool requires its own configuration, management, and monitoring, adding layers of complexity that can overwhelm security teams.
Is your security team struggling to monitor access in real-time? This could be a sign of system complexity and can lead to undetected breaches and delayed responses. Complex systems generate vast amounts of data, making it challenging to filter out critical security alerts from the noise. Ineffective real-time monitoring can result in slow threat detection and response times, increasing the risk of significant security incidents.
Wide variations in access control policies across different parts of the organization can lead to security gaps and enforcement inconsistencies. Ensuring a unified security approach becomes challenging when other departments or teams use different policies. Attackers who look for weak spots in the security fabric can exploit this inconsistency.
Are you struggling to conduct regular audits and ensure compliance with industry regulations? This could indicate that your access control processes are too complex. The intricate nature of these systems often requires specialized knowledge to navigate and assess, making compliance audits time-consuming and costly. Non-compliance can expose the organization to legal and financial risks, including fines and reputational damage.
Complex access controls can make monitoring and restricting insider access difficult, leading to a higher incidence of insider threats. Insiders who already have a level of trusted access can exploit overly complex systems to bypass security measures or access unauthorized data. The difficulty in tracking and managing insider activities in such environments exacerbates this risk.
Are users struggling to get the access they need to data? This indicates overly complex access controls, which can decrease productivity and lead to frustration. This can also lead to users seeking workarounds, such as using unauthorized methods to access data, which further compromises security.
Selecting the right Data Security Platform (DSP) is crucial for effectively managing data access control and safeguarding sensitive information. Here are the key attributes to consider when choosing a DSP:
A robust DSP should offer automated tools for quickly identifying and classifying sensitive data. This capability ensures that high-risk data is discovered and protected promptly, meeting compliance requirements. Automated classification tools help streamline the identification process, reducing the manual effort involved and ensuring that all sensitive data is accounted for and adequately secured.
Look for a DSP that allows you to set up automated access controls with dynamic data masking capabilities. These controls ensure that only credentialed users can access sensitive information, minimizing the risk of unauthorized access. They also help maintain security policies consistently across the organization, reducing the potential for human error and enhancing overall data protection.
Effective DSPs provide real-time observability over how sensitive data is consumed in the cloud. This includes active alerts for unauthorized requests, allowing immediate response to potential security breaches. Real-time data activity monitoring is essential for maintaining an up-to-date security posture and ensuring that any suspicious activity is detected and addressed promptly.
Choose a DSP that offers integrated data security from source to cloud. Automated data access governance ensures that sensitive data is never at risk, providing comprehensive protection throughout its lifecycle. Integrated security measures help unify the approach to data protection, ensuring that all aspects of data security are covered and reducing the complexity involved in managing multiple security tools.
A good DSP should allow non-technical users to implement policies and simplify data ownership. This ensures that data security processes can remain streamlined and automated without requiring extensive technical knowledge. User-friendly interfaces and straightforward policy implementation tools enable broader participation in data governance, helping to maintain consistent security practices across the organization.
Managing data access control is vital for protecting sensitive information, but complexity can create numerous risks and challenges. By recognizing the signs and choosing the right Data Security Platform (DSP), you can create more robust and manageable data security environment.
We're thrilled to announce that ALTR's Snowflake native app, Format-Preserving Encryption (FPE), is now available on the Snowflake Marketplace. This marks a significant step forward in our mission to make data security seamless, efficient, and scalable for our customers. Let's dive into what this means for you.
Imagine encrypting your sensitive data without altering its original structure or format. That's precisely what FPE does. It transforms plaintext data into ciphertext while keeping the same format. For example, a phone number like "(800) 416-4710" might be encrypted as"(201) 867-5309." This means your applications and systems can continue operating smoothly without needing changes to handle encrypted data.
Traditionally, encrypting data involved a lot of headaches. On-premises systems were expensive, costing millions of dollars per license, and they introduced significant lag because of the back-and-forth calls between Snowflake and the on-premises servers. This not only slowed down your queries but also burned a hole in your pocket with monthly costs.
With ALTR's Snowflake Native FPE, all the encryption and decryption happen locally within Snowflake. No more external calls, no more lag—just fast, secure data processing. Plus, your data stays protected at rest within the Snowflake Data Cloud, ensuring it's always secure.
Snowpark, Snowflake's developer framework, provides the perfect environment for our FPE solution. It supports fully functional applications, enabling us to deliver powerful encryption directly in Snowflake. This means you get top-notch data protection without compromising performance or ease of use.
Here's why this is excellent news for you:
ALTR's FPE integrates seamlessly with our existing data access control and security solutions. This means you can easily implement and manage comprehensive data security through our SaaS platform, no-code interface, and automated policy enforcement.
You save millions in licensing fees and monthly operational costs by eliminating the need for on-premises appliances. Plus, faster query response times make your data operations more efficient.
FPE ensures that your sensitive data is always protected, even as you scale and evolve your data ecosystem. It's particularly beneficial for industries like financial services and healthcare, where maintaining data interoperability with legacy systems is crucial.
"ALTR's FPE offering running natively in our Snowflake environment proved to be far more effective, scalable, and affordable than the legacy solutions we considered. Further, with ALTR's cloud-native, SaaS architecture, we could extend FPE upstream into our data pipeline, expanding our compliance footprint to include a staging area prior to workloads landing in Snowflake."
Craig Hipwell, Customer Platforms Delivery Manager,Shell Energy Customer Platforms Delivery Manager,
With ALTR's FPE now available on the Snowflake Marketplace, you have all the tools you need to protect your data efficiently, effectively and at scale. It's time to take your data security to the next level.
Explore our FPE solution on the Snowflake Marketplace and see how easy it can be to keep your data safe while maintaining top performance.
I’ve spent the last two decades in enterprise software sales, where I’ve had the privilege of building and leading high-performance sales and marketing teams. My career has taken me from established, large-scale organizations to dynamic, ground-zero startups. Throughout this journey, I’ve successfully brought together all facets of Go-To-Market strategies under a cohesive team structure. My expertise lies in navigating complex ecosystems such as ServiceNow and Snowflake, where I’ve developed comprehensive market strategies that drive growth and success. I’ve consistently focused on aligning sales initiatives with broader business goals, ensuring sustainable revenue streams and long-term customer relationships.
Several factors influenced my decision to join ALTR. First and foremost, I was thoroughly impressed by the ALTR team. Their deep understanding of current data security challenges and their forward-thinking approach to simplifying and scaling data security stood out to me. Additionally, the opportunity to be at the forefront of the cloud data revolution is incredibly exciting. As businesses increasingly migrate their critical data to the cloud, they adopt advanced technologies like machine learning and artificial intelligence to gain competitive advantages. ALTR's dedication to helping clients balance this technological innovation with robust cloud data security makes it an inspiring endeavour to be part of.
My vision for ALTR is to establish us as the de facto standard for cloud data security. This involves offering a robust, rock-solid platform and leading the industry with unmatched security expertise. Under my leadership, I aim to drive the company past critical growth milestones typical for a thriving SaaS enterprise. This includes expanding our market reach, continuously innovating our product offerings, and maintaining a relentless focus on customer satisfaction. I foresee ALTR evolving into a cornerstone of data security, trusted by organizations worldwide to protect their most valuable asset: their data.
Over the years, data security and governance have undergone significant transformations. The landscape has evolved from simple perimeter defenses to sophisticated, multi-layered security strategies. Despite these advancements, cybercriminals continue to outpace many enterprises due to the high stakes involved. The fundamental principles of data security – knowing where your data is, who has access to it, and ensuring its protection – remain unchanged. However, in the next five years, the challenge will lie in meeting these requirements at the scale and speed of the cloud.
Building a successful software company hinges on four key pillars:
Product: It all starts with having a viable and innovative product that addresses real market needs. ALTR excels here with its scalable data security solutions tailored for the cloud era.
Market: Identifying and targeting an addressable market is crucial. The demand for robust data security and governance solutions grows exponentially as more businesses move to the cloud.
Defense: Defending your market position against competitors is essential. ALTR’s advanced technology, coupled with its deep industry expertise, provides a formidable defense.
Team: The most critical element is having a team that can build and execute together effectively. At ALTR, we have a dedicated, talented team committed to our mission of simplifying data security and data access governance.
These pillars make ALTR an exceptional solution for organizations seeking to enhance their data security and governance and make it an attractive place for top talent in the industry. By joining ALTR, professionals can work on the frontlines of data security innovation, contributing to solutions that make a real difference in today’s digital landscape.
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For data engineers, there's a comforting hum in the familiar, a primal urge to build things ourselves."DIY is better," whispers the voice in their heads. But when it comes to data masking in Snowflake, is building policies from scratch the best use of our time?
Sure, the initial build of a masking policy might be a quick win. You get that rush of creation, the satisfaction of crafting something bespoke. But here's the harsh reality: that initial high fades fast. Masking policies are rarely static. Data evolves, regulations shift, and suddenly, your DIY masterpiece needs an overhaul.
This is where the actual cost of the"DIY is better" mentality becomes apparent. Let's delve into the hidden complexities that lurk beneath the surface of Snowflake's manual masking policies.
Ah, version control. The unsung hero of software development. But when it comes to DIY masking policies, it can be atangled mess. Every change, every tweak you make, needs to be meticulously documented and tracked. One wrong move, and you could be staring down the barrel of a data breach caused by an outdated policy.
Imagine the chaos if multiple engineers are working on the same masking logic. How do you ensure everyone is on the same page? How do you revert to a previous version if something goes wrong? While Snowflake recently announced a Private Preview for version control via Git, with a purpose-built UI like ALTR, version control is baked in and highly user-friendly. There is no need for complex terminal commands –just intuitive clicks and menus. Changes are tracked, history is preserved, and rollbacks are a breeze.
Snowflake offers a seemingly endless buffet of objects – a staggering 74 and counting, with new additions continually emerging. However, managing these objects poses a central challenge within the Snowflake ecosystem.
For instance, while masking policies reside within schemas, their impact extends far beyond. A single masking policy can be applied to tables and columns across numerous schemas within your Snowflake account.
This creates a masking policy headache. Choosing the correct schema for each policy is crucial, as poor placement leads to confusion and complex updates. Furthermore, meticulous documentation is essential to track policy location and impact. Without it, any changes or troubleshooting become a nightmare due to the potential for widespread, unforeseen consequences across your Snowflake environment.
With ALTR, you do not have to consider object management when masking policies. With our unified interface, you can easily create, edit, and deploy policies automatically in seconds, eliminating the need to navigate the intricate web of Snowflake objects and their relationships.
Data masking policies are living documents. As your data landscape changes, so too should your masking logic. New regulations might demand a shift in how you mask specific fields. A data breach requires you to tighten masking rules.
With DIY policies, every update becomes a time-consuming ordeal. You must identify the relevant policy, modify the logic, test it thoroughly, and then deploy the changes across all affected Snowflake objects. Multiply that process by the number of policies you have, and you've just booked a one-way ticket to Update City – population: you, stressed and overworked.
ALTR simplifies this process. Its intuitive UI allows for quick and easy changes to policies. Updates can be deployed across all relevant objects with a single click, eliminating the need for manual deployment across potentially hundreds of locations.
Let's not forget the critical step of validation. Every change you make to a masking policy must be rigorously tested to ensure it functions as intended. This involves creating test data, applying the new masking logic, and verifying that the sensitive data is adequately protected.
Imagine manually validating dozens of masking policies across hundreds or thousands of tables and columns. It's a daunting task, and relying solely on automated pipelines for testing adds another layer of complexity that needs ongoing maintenance. It's enough to make any data engineer break out in a cold sweat.
The benefits of ditching DIY masking policies extend far beyond just saving time. It's about empowerment. With ALTR's easy-to-use UI, even non-technical users can create and edit masking policies. This frees up valuable engineering time, allowing you to focus on more strategic initiatives. It also fosters a culture of data ownership and responsibility, where everyone involved understands the importance of data security.
Let's face it: the "DIY is better" mentality can be a trap in data masking. It might seem like a quick win initially, but the long-term costs – time, complexity, and risk – are too high. Embrace the power of purpose-built tools like ALTR. Free your engineering time, empower your team, and ensure your data is masked effectively and efficiently.
Ready to ditch the DIY trap? Schedule an ALTR demo.
Snowflake Arctic and the Future of AI Governance If you’re reading this, then it’s certain you saw the news about Snowflake’s Arctic model launch. Machine learning and AI is the next natural step for the Snowflake Data Cloud. Not only because it’s a hot trend, but because the Snowflake story naturally leads you to AI. What makes machine learning better? Lots of data. Where are you putting more and more of your data? Snowflake. Of course, there’s no such thing as a free lunch. While your data scientists, developers, and all the other Snowflake enthusiasts in your orbit are rushing to see how they can start leveraging Arctic (and there are already ways popping out of the Snowflake teams as well), maybe you’re here because you have accountability for your organization’s data. You may have one very important question: how is Arctic going to affect my governance and security stance? We’re here to answer that question, and the answer is mostly good – if you’re going to do the right things right now.
The TLDR on this is simple. Arctic is like every other thing that runs in the Snowflake Data Cloud. Nothing in Snowflake escapes the watchful eye of Snowflake governance policies. Nothing in Snowflake can skip past the network controls, security checks, encryption, or RBAC (Role-Based Access Control). The simplest way to understand this is that to use all this power in the Arctic LLM you have a list of simple, built-in Snowflake functions. You only have permission to use the AI stuff if you have permission to use those functions. And you only have permission to feed data that you are already allowed to access into those functions. Simple, right? End of the story, right? If that were the end, that would also be the end of this post. Honestly, I probably wouldn’t have bothered to write it if that were the case.
While it’s true that AI access is limited to the Cortex functions and that people will only be able to bring the data they already have access to into those functions, when you combine AI and the huge wells of data that Snowflake tends to have things may get weird. It’s not unusual for people (or services) to be over-provisioned. Just yesterday we were on the line with a prospect who was shocked to see ALTR’s real-time auditing picking up dozens of jobs running under the Snowflake SYSADMIN role. These queries running with too much privilege happened because lots of folks were granted this role through nesting to make it easier for them to get some data that had been put in a database that it probably shouldn’t have been in, and it was easier to grant the role than move the data. (This sort of security gap is exactly why this company is looking at ALTR in the first place!) With that SYSADMIN role, those users could have accessed tons of stuff they weren't supposed to. They didn’t (we know that because ALTR’s auditing would have caught them), but since they had the access, they could have. Humans tend to only query data they know they have access to. But what happens when AI takes the wheel?
Right now, the impact that AI’s power can have in Snowflake is limited. But just like having a model like Snowflake’s Arctic was the next natural step in the Snowflake story, there are more natural steps we can imagine. People are going to throw all the data they have at this thing to attempt to get amazing results. What happens when they have access to data they shouldn’t? What happens when they should have access to a table, but maybe there’s sensitive information in columns and there needs to be advanced data protection in place to make that data usable in the context of Cortex, Arctic, and AI in general? The machines won’t use the same approaches humans will (and vice versa). That’s why humans and AIs make such an effective team when things go right. But that also means these LLMs won’t limit themselves to only what they know. They will crawl through every scrap of data they have access to trying to find the right answer to get that good feedback we’ve programed them to seek. What happens when that machine is mistakenly given SYSADMIN role like the humans were? And, of course, people are going to build fully automated systems where the AI-powered machines will run all the time pushing these boundaries. Humans sleep, take time off, and eat a meal every now and then. What happens when your governance and security must be on watch 24/7 because they’re contending with machines that never step away?
The good news is that we’re only standing on the tip of this iceberg (pun intended). Most of this stuff is still a little while away. But as with everything else related to AI, it’s going to move fast. So now more than ever it's crucial that security and governance be integrated into the data and development pipelines and CI/CD approaches as well as automated as much as possible. Snowflake has all the controls you need to prevent the bad stuff from happening, but you need to use them effectively and automatically. The sensitive information in your data needs special attention more than ever in an AI-powered world. In that conversation yesterday, the customer asked about the new Arctic stuff and how ALTR could address that even though it just dropped this month. The answer is simple: ALTR has been in the proactive security business since the start. Since Snowflake did the right thing by building security directly into the Arctic and AI design, it’s just another thing ALTR can help you lock down as you roll it out. It all fits together perfectly. The next natural step in that company’s story – and maybe in yours – is to decide to let us help them out. We’re ready for AI when you are.
The data deluge is absolute. Organizations are swimming in an ever-growing sea of information, struggling to keep their heads above water. With its rigid processes and bureaucratic burdens, traditional data governance often feels like a leaky life raft – inadequate for navigating the dynamic currents of the modern data landscape.
Enter agile data governance, the data governance equivalent of a high-performance catamaran, swift and adaptable, ready to tackle any challenge the data ocean throws its way.
Traditional data governance often operates siloed, with lengthy planning cycles and a one-size-fits-all approach. Agile data governance throws this rigidity overboard. It's a modern, flexible methodology that views data governance as a collaborative, iterative process.
Here's the critical distinction: While traditional data governance focuses on control, agile data governance emphasizes empowerment. It fosters a data-savvy workforce, breaks down silos, and prioritizes continuous improvement to ensure data governance practices remain relevant and impactful.
Gone are the days of data governance operating in isolation. Agile fosters a spirit of teamwork, breaking down silos and bringing together data owners, analysts, business users, and IT professionals. Everyone plays a role in shaping data governance practices, ensuring they are relevant and meet real-world needs.
Forget lengthy upfront planning that quickly becomes outdated in the face of evolving data needs. Agile embraces a "test and learn" mentality, favoring iterative cycles. Processes are continuously refined based on ongoing feedback, data insights, and changing business priorities.
The data landscape is a living, breathing entity, constantly shifting and evolving. Agile data governance recognizes this reality. It's designed to bend and adapt, adjusting sails (figuratively) to navigate new regulations, integrate novel data sources, or align with evolving business strategies.
Agile data governance is not about control; it's about empowerment. It fosters a data-savvy workforce by prioritizing training programs that equip employees across the organization with the skills to understand, use, and govern data responsibly. Business users become active participants, not passive consumers, of data insights.
Agile data governance thrives on a culture of constant improvement. Regular assessments evaluate the effectiveness of data governance practices, identifying areas for refinement and ensuring that the program remains relevant and impactful.
Repetitive, mundane tasks are automated wherever possible. This frees up valuable human resources for higher-value activities like data quality analysis, user training, and strategic planning. Data classification, access control management, and dynamic data masking are prime candidates for automation.
Agile thrives on data-driven decision-making. Metrics and measurement are woven into the fabric of the program. Key performance indicators (KPIs) track the effectiveness of data governance initiatives, providing valuable insights to guide continuous improvement efforts. These metrics can encompass data quality measures, access control compliance rates, user satisfaction levels with data discoverability, and the impact of data insights on business outcomes.
The data landscape in 2024 is a rapidly evolving ecosystem. Here's why agile data governance is no longer optional but a strategic imperative:
Regulatory environments are becoming more dynamic than ever. Agile data governance allows organizations to adapt their practices swiftly to ensure continuous compliance with evolving regulations like data privacy laws (GDPR, CCPA) and industry-specific regulations.
Artificial intelligence (AI) is transforming decision-making across industries. Agile data governance ensures high-quality data feeds reliable AI models. The focus on clear data lineage and ownership within agile data governance aligns perfectly with the growing need for explainable AI.
Agile data governance empowers business users to access, understand, and utilize data for informed decision-making. This fosters a data-driven culture where valuable insights are readily available to those who need them most, driving innovation and improving business outcomes.
The iterative approach and automation focus of agile data governance streamline processes and free up valuable resources for higher-value activities. This allows organizations to navigate the complexities of the data landscape with efficiency.
Are your current data governance practices keeping pace with the ever-changing data landscape? Here are ten questions to assess your organization's agility:
1. Do different departments (IT, business users, data owners) collaborate to define and implement data governance practices?
2. Can your data governance processes adapt to accommodate new data sources, changing regulations, and evolving business needs?
3. Are business users encouraged to access and utilize data for decision-making?
4. Do you regularly evaluate the effectiveness of your data governance program and make adjustments as needed?
5. Are repetitive tasks like data lineage tracking and access control automated?
6. Do you track key metrics to measure the success of your data governance program?
7. Do you utilize an iterative approach with short planning, implementation, and improvement cycles?
8. Does your organization prioritize training programs to equip employees with data analysis and interpretation skills?
9. Are data governance policies and procedures clear, concise, and accessible to all relevant stakeholders?
10. Do business users feel confident finding and understanding the data they need to make informed decisions?
By honestly answering these questions, you can gain valuable insights into the agility of your data governance program. If your answers reveal a rigid, one-size-fits-all approach, it might be time to embrace the transformative power of agile data governance.
Agile data governance is not just a trendy buzzword; it's a critical approach for organizations in 2024 and beyond. By embracing its principles and building a flexible framework, organizations can transform their data from a burden into a powerful asset, propelling them toward a successful data-driven future.
Our customers are confused. Given the state of the world, it’s safe to say everyone is a little confused now. The confusion we’re concerned with today is about the markets ALTR plays in and how the analysts of the world – particularly Gartner – are breaking those down and making recommendations. What we’ll aim to do here is analyze the analysis. We’ll lay out the questions customers are asking about the markets and solutions for Data Security Posture Management (DSPM) and Data Security Platform (DSP), see what Gartner is saying about those today, offer some reasons why we think they are right, and finally show why the confusion is real.
Maybe that seems like a contradictory stance to take, but let’s not forget what F. Scott Fitzgerald told us: “The test of a first-rate intelligence is the ability to hold two opposing ideas in mind at the same time and still retain the ability to function.” By the end of this post, it should be clear that Gartner and others have only correctly identified a confusing time in data governance and security; they have not made things any more confusing.
Let’s start out where customers have told us they get confused. We’ll go right to the source and quote from Gartner’s own public statements on DSPM and DSP. First, let’s look at how they define Data Security Posture Management:
Data security posture management (DSPM) provides visibility as to where sensitive data is, who has access to that data, how it has been used, and what the security posture of the data stored, or application is.
We could pick that apart right away, but instead let’s immediately compare it with their definition of a Data Security Platform:
Data security platforms (DSPs) combine data discovery, policy definition and policy enforcement across data silos. Policy enforcement capabilities include format-preserving encryption, tokenization and dynamic data masking.
At first glance, these seem incredibly similar – and they are. However, there are important differences in the definitions’ text, in their implied targets, and in the implications of these factors. The easiest place to see a distinction is in the second part of the DSP definition: “policy definition and policy enforcement." The Data Security Platform does not only look at the “Posture” of that system. It is going to deliver a security solution for the data systems where it’s applied.
When talking to customers about this, they will often point out two details. First, they will say that if the DSP can’t do the discovery of at least the policy of the data systems then it isn’t much good that they give you ways to manage the protection. The subtlety here is that controlling the data policy implies that the solution would discover the current policy in order to control it going forward. (While it’s possible that some solution may give you policy control without policy discovery, ALTR gives you all those capabilities, so we don't have to worry about that.) The second thing they point out is that many of the vendors who are in the DSPM category also supply “policy definition and policy enforcement” in some way. That brings us to discussing the targets of these systems.
Something you will note as a common thread for the DSPM systems is how incredibly broad their support is for target platforms. They tend to support everything from on-prem storage systems all the way through cloud platforms doing AI and analytics like Snowflake. The trick they use to do this is that they are not concerned with the actual enforcement at that broad range, and that’s appropriate. Many of the systems they target, especially those on-prem, will have complicated systems that do policy definition and enforcement. Whether that’s something like Active Directory for unstructured data stored on disk or major platforms like SAP’s built-in security management capabilities, they are not looking for outside systems to get involved. However, the value of seeing the permissions and access people use at that broad scope can be very important. Seeing the posture of these systems is the point of the DSPM.
Of course, a subset of the systems will allow the DSPM to make changes that can be effective easily without requiring them to get too deep. If it’s about a simple API call or changing a single group membership, then the DSPM can likely do it. However, in systems where there are especially complex policies those simple, single API calls become about the “policy definition and policy enforcement" in the Data Security Platform definition. The DSP will get deep within the systems they target. Often, part of the core value of a DSP is that it will simplify what are extremely complicated policy engines and give ways to plug these policy definition steps into the larger scope of systems building or the SDLC. That focus and depth on the actual controls in targeted systems is the main difference between DSPM and DSP. The Data Security Platform narrows the scope, but it deepens the capabilities to control policies and to deliver security and governance results.
The other important aspect of the distinction between these solutions is the Data Security Platform capabilities for Data Protection. That’s the “format-preserving encryption, tokenization and dynamic data masking” part of the DSP definition. Many data systems will have built-in solutions for data masking. Almost none will have built-in tokenization or format-preserving encryption (FPE). If these capabilities are crucial to delivering the data products and solutions an organization needs, then DSP is where they will look for solutions. This not only impacts data use in production settings, but often is associated with development and testing use cases where use of sensitive information is forbidden but use of realistic data is required.
Let’s recognize the elephant in the analysis: DSPM and DSP are going to have overlap. If you’ve been around long enough or have read deeply enough, that should be as shocking as the fact that (if you’re in an English-speaking part of the world) the name of this day ends in “y.” Could the DSP forgo all the core capabilities of DSPM and just deliver the deeper policy and data protection features? If the DSM vendors could be sure that every customer will have DSPM to integrate with, sure. That isn’t always the case. Even if it were, it’s not guaranteed that the politics and process at an organization would make such integration possible even if it is technically possible. Could DSPM simply expand to cover all the depth of DSP including the Data Protection features? The crucial word in there is “simply.” If it were simple they would have done it already.
It’s sure that you will see consolidation of the market over time with players merging, expanding, and being bought to make suites. Right now, organizations have real-world challenges, and they need solutions despite the overlaps. So DSPM and DSP will stay independent until market forces make it necessary for them to change.
The overlaps, the similar goals, and the limits of language in describing Data Security Posture Management and Data Security Platforms are the source of the confusion. Hopefully, it’s now clear that DSP is the deeper solution that gives you everything you need to solve problems all the way down to Data Protection. DSPM will continue to add more platforms to grow horizontally. DSP will continue to dive deeply into the platforms they support today and cautiously add new platforms to dive more deeply into as the market needs them to. If you started this a little mad at the Gartners of the world, maybe you now see how they are right to give you two different markets with so much in common. Like with many things in life, if you are confused, it only means you are sane and paying attention. You keep paying attention, and we’ll keep helping you stay sane.
Data privacy laws are not just a legal hurdle – they're the key to building trust with your customers and avoiding a PR nightmare. The US, however, doesn't have one single, unified rulebook. It's more like a labyrinth – complex and ever-changing.
Don't worry; we've got your back. This guide will be your compass, helping you navigate the key federal regulations and state-level laws that are critical for compliance in 2024.
Data breaches are costly and damaging. But even worse is losing the trust of your customers. Strong data privacy practices demonstrate your commitment to safeguarding their information, a surefire way to build loyalty in a world where privacy concerns are at an all-time high.
Think of it this way: complying with data privacy laws isn't just about checking boxes. It's about putting your customers first and building a solid foundation for your business in the digital age.
The US regulatory landscape is an intricate web of federal statutes and state-specific legislation. Here's a breakdown of some of the key players:
These laws set the baseline for data privacy across the country.
Privacy Act of 1974 restricts how federal agencies can collect, use, and disclose personal information. It grants individuals the right to access and amend their records held by federal agencies.
Health Insurance Portability and Accountability Act (HIPAA) (1996) sets national standards for protecting individuals' medical records and other health information. It applies to healthcare providers, health plans, and healthcare clearinghouses.
Gramm-Leach-Bliley Act (GLBA) (1999): Also known as the Financial Services Modernization Act, GLBA safeguards the privacy of your financial information. Financial institutions must disclose their information-sharing practices and implement safeguards for sensitive data.
Children's Online Privacy Protection Act (COPPA) (2000) protects the privacy of children under 13 by regulating the online collection of personal information from them. Websites and online services must obtain verifiable parental consent before collecting, using, or disclosing personal information from a child under 13.
Driver's Privacy Protection Act (DPPA) (1994) restricts the disclosure and use of personal information obtained from state motor vehicle records. It limits the use of this information for specific purposes, such as law enforcement activities or vehicle safety recalls.
Video Privacy Protection Act (VPPA) (1988) prohibits the disclosure of individuals' video rental or sale records without their consent. This law aims to safeguard people's viewing habits and protect their privacy.
The Cable Communications Policy Act of 1984 includes provisions for protecting cable television subscribers' privacy. It restricts the disclosure of personally identifiable information without authorization.
Fair Credit Reporting Act (FCRA) (1970) regulates consumer credit information collection, dissemination, and use. It ensures fairness, accuracy, and privacy in credit reporting by giving consumers the right to access and dispute their credit reports.
Telephone Consumer Protection Act (TCPA) (1991)combats unwanted calls by imposing restrictions on unsolicited telemarketing calls, automated dialing systems, and text messages sent to mobile phones without consent.
Controlling the Assault of Non-Solicited Pornography and Marketing Act of 2023 (CAN-SPAM Act) establishes rules for commercial email, requiring senders to provide opt-out mechanisms and identify their messages as advertisements.
Family Educational Rights and Privacy Act (FERPA) (1974) protects the privacy of students' educational records. It grants students and their parents the right to inspect and amend these records while restricting their disclosure without consent.
Many states are taking matters into their own hands with comprehensive data privacy laws. California, Virginia, and Colorado are leading the charge, with more states following suit. These laws often grant consumers rights to access, delete, and opt out of the sale of their personal information. Here are some of the critical state laws to consider:
California Consumer Privacy Act (CCPA) (2018) was a landmark piece of legislation establishing a new baseline for consumer data privacy rights in the US. It grants California residents the right to:
Colorado Privacy Act (2021): Similar to the CCPA, it provides consumers with rights to manage their data and imposes obligations on businesses for data protection.
Connecticut Personal Data Privacy and Online Monitoring Act (2023) specifies consumer rights regarding personal data, online monitoring, and data privacy.
Delaware Personal Data Privacy Act (2022) outlines consumer rights and requirements for personal data protection.
Florida Digital Bill of Rights (2023) focuses on entities generating significant revenue from online advertising, outlining consumer privacy rights.
Indiana Consumer Data Protection Act (2023) details consumer rights and requirements for data protection.
Iowa Consumer Data Protection Act (2022) describes consumer rights and requirements for data protection.
Montana Consumer Data Privacy Act (2023) applies to entities conducting business in Montana, outlining consumer data protection requirements.
New Hampshire Privacy Act (2023): This act applies to entities conducting business in New Hampshire, outlining consumer data protection requirements.
New Jersey Data Protection Act (2023): This act applies to entities conducting business in New Jersey, outlining consumer data protection requirements.
Oregon Consumer Privacy Act (2022): This act details consumer rights and rules for data protection.
Tennessee Information Protection Act (2021) governs data protection and breach reporting.
Texas Data Privacy and Security Act (2023) describes consumer rights and data protection requirements for businesses.
Utah Consumer Privacy Act (2023) provides consumer rights and emphasizes data protection assessments and security measures.
Virginia Consumer Data Protection Act (2021) grants consumers rights to access, correct, delete, and opt out of their data processing.
Data doesn't respect borders. The EU's General Data Protection Regulation (GDPR) is a robust international regulation that applies to any organization handling the data of EU residents. Understanding the GDPR's requirements for consent, data security, and data subject rights is essential for businesses operating globally.
Conquering the data privacy maze requires vigilance and a proactive approach. Here are some critical steps:
Identify which federal and state laws apply to your business and understand their specific requirements. Conduct a comprehensive data inventory to understand what personal information you collect, store, and use.
Develop clear and concise data privacy policies that outline your data collection practices and how you safeguard information. Make these policies readily available to your customers.
Give your customers control over their data by providing mechanisms to access, delete, and opt out of data sharing. Be upfront about how you use their data and respect their choices.
Implement robust security measures to protect customer data from unauthorized access, disclosure, or destruction.
The data privacy landscape is constantly evolving. Regularly review and update your policies and procedures to comply with emerging regulations. Appoint a team within your organization to stay abreast of these changes.
The data privacy landscape is complex and constantly evolving, but it doesn't have to be overwhelming. By understanding the key regulations, taking a proactive approach, and building a culture of compliance, you can emerge as a more vital, trusted organization. In today's data-driven world, prioritizing data privacy isn't just good practice – it's essential for building lasting customer relationships and achieving long-term success.
Data has undeniably become the new gold in the swiftly evolving digital transformation landscape. Organizations across the globe are mining this precious resource, aiming to extract actionable insights that can drive innovation, enhance customer experiences, and sharpen competitive edges. However, the journey to unlock the true value of data is fraught with challenges, often likened to navigating a complex labyrinth where every turn could lead to new discoveries or unforeseen obstacles. This journey necessitates a robust data infrastructure, a skilled ensemble of data engineers, analysts, and scientists, and a meticulous data consumption management process. Yet, as data operations teams forge ahead, making strides in harnessing the power of data, they frequently encounter a paradoxical scenario: the more progress they make, the more the demand for data escalates, leading to a cycle of growth pains and inefficiencies.
One of the most significant bottlenecks in this cycle is the considerable amount of time and resources devoted to data governance tasks. Traditionally, data control and protection responsibility has been shouldered by data engineers, data architects and Database Administrators (DBAs). On the surface, this seems logical – these individuals maneuver data from one repository to another and possess the necessary expertise in SQL coding, a skill most tools require to grant and restrict access. But is this alignment of responsibilities the most efficient use of their time and talents?
The answer, increasingly, is no.
While data engineers, DBAs and data architects are undoubtedly skilled, their actual value lies in their ability to design complex data pipelines, craft intricate algorithms, and build sophisticated data models. Relegating them to mundane data governance tasks underutilizes their potential and diverts their focus from activities that could yield far greater strategic value.
Imagine the scenario: A data scientist, brimming with the potential to unlock groundbreaking customer insights through advanced machine learning techniques, finds themself bogged down in the mire of access control requests, data masking procedures, and security audit downloads.
This misallocation of expertise significantly hinders the ability of data teams to extract the true potential from the organization's data reserves.
Enter the paradigm shift: data governance automation. This transformative approach empowers organizations to delegate the routine tasks of data governance and security to dedicated teams equipped with no-code control and protection solutions.
Solutions like ALTR offer a platform that empowers data teams to quickly and easily check off complex data governance task including:
Leverage automated, tag-based, column and row access controls on PII/PHI/PCI data.
Protect sensitive data with column-based and row-based access policies and dynamic data masking and scale policy creation with attribute-based and tag-based access control.
Maintain a comprehensive data access and usage patterns record, facilitating security audits and regulatory compliance.
Receive real-time data activity monitoring, policy anomalies, and alerts and notifications.
With more time at their disposal, data teams can delve deeper into data, employing advanced analytics techniques and AI models to uncover previously elusive insights. This could lead to breakthrough innovations, more personalized customer experiences, and data-driven decision-making across the organization.
Data teams can spearhead initiatives to democratize data access, enabling a broader base of users to engage with data directly. Organizations can cultivate a data-driven culture where insights fuel every department's decision-making processes by implementing intuitive, self-service analytics platforms and conducting data literacy workshops.
Attention can be turned towards optimizing the data infrastructure for scalability, performance, and cost-efficiency. This includes adopting cloud-native services, containerization, and serverless architectures that can dynamically scale to meet the fluctuating demands of data workloads.
With the foundational tasks of data governance automated, data teams can focus on developing new data products and services. This could range from predictive analytics tools for internal use to data-driven applications that enhance customer engagement or open new revenue streams.
Finally, data teams could invest time in building collaborative ecosystems and forging partnerships with other organizations, academia, and open-source communities. These ecosystems can foster innovation, accelerate the adoption of best practices, and enhance the organization's capabilities through shared knowledge and resources.
Automating data governance tasks presents a golden opportunity for data teams to realign their focus toward activities that maximize the strategic value of data. By embracing this shift, organizations can alleviate the growing pains associated with data management and pave the way for a future where data becomes the linchpin of innovation, growth, and competitive advantage. The question then is not whether data teams should adopt data governance automation but how quickly they can do so to unlock their full potential.
Let's face it: your current data security strategy is probably as outdated as a dial-up modem. You're still relying on clunky, manual processes, struggling to keep pace with ever-evolving regulations, and dreading the thought of a potential data breach. It's time to ditch the Stone Age tools and step into the ALTR era.
ALTR isn't just another data security platform; it's a game-changer. It's the excalibur you've been searching for, ready to slay the dragons of data security challenges and protect your kingdom (read: organization) from the ever-present threats.
Data classification is where the battle lines are drawn in data security. Yet, many organizations are stuck with rudimentary checkbox approaches that barely scrape the surface of what's needed. ALTR challenges this status quo by offering an intelligent, dynamic data classification system that doesn't just identify sensitive data but understands it. With ALTR, you're not just tagging data; you're gaining deep insights into its nature, usage, and risk profile. This isn't just classification; it's a strategic reconnaissance of your data landscape, enabling precise, informed decisions about access and security policies.
In data protection, static defenses are as outdated as castle moats. ALTR brings the agility and adaptability of dynamic data masking to the forefront. Imagine your sensitive data cloaked in real-time, visible only to those with the right 'magical' keys. This isn't just about hiding data; it's about creating a flexible, responsive shield that adjusts to context, user, and data sensitivity, ensuring that your data remains protected in storage and in use.
With ALTR, database activity monitoring evolves from a passive logbook to an active, all-seeing eye that watches over your data landscape. This feature isn't just about tracking access; it's about understanding behavior, detecting anomalies, and preempting threats before they manifest. ALTR doesn't just alert you to breaches; it helps prevent them by offering insights into data access patterns, ensuring that any deviation from the norm is detected and dealt with in real-time.
In a world where data breaches are a matter of when, not if, ALTR's tokenization vault offers the ultimate sleight of hand—making your sensitive data vanish, replaced by indecipherable tokens. This is more than encryption; it's a transformation that renders data useless to thieves, all while maintaining its utility for your business processes. With ALTR, tokenization isn't just a security measure; it's a strategic move that protects your data without compromising performance or functionality.
ALTR's Format Preserving Encryption (FPE) challenges the traditional trade-offs between data usability and security. With FPE, your data remains operational, retaining its original form and function, yet securely encrypted to ward off prying eyes. This feature is a game-changer, ensuring that your data can continue fueling business processes and insights while securely locked away from unauthorized access.
Data access governance with ALTR is not about looking back at what went wrong; it's about looking ahead and preventing breaches before they happen. This is governance with teeth, offering not just oversight but foresight, enabling you to anticipate risks, enforce policies proactively, and ensure that every access to sensitive data is justified, monitored, and compliant with the highest security standards.
It's time to shed the cumbersome, outdated tools and strategies holding your data governance efforts back. The era of treating data security and compliance as burdensome chores is over. With ALTR, you're not just upgrading your technology stack; you're revolutionizing your entire approach to data governance. This isn't just a step forward; it's a leap into a new realm of possibilities where data security becomes your strength, not your headache.
Your data is the prize in the digital battlefield, and ALTR is your ultimate defence mechanism. By embracing ALTR, you're not just mitigating the risk of data breaches; you're rendering your data fortress impregnable. With dynamic data masking, tokenization, and format-preserving encryption, sensitive information becomes a moving target, elusive and indecipherable to unauthorized entities. This is data security reimagined, where your defences evolve in real-time, staying several steps ahead of potential threats.
The labyrinth of data protection regulations can be daunting, with every misstep risking heavy penalties and reputational damage. ALTR transforms this maze into a clear path, simplifying compliance with its intelligent data governance framework. Whether GDPR, HIPAA, CCPA, or any other regulatory acronym, ALTR equips you to meet and exceed these standards with minimal effort. Say goodbye to the endless compliance checklists and welcome a solution that embeds regulatory adherence into the very fabric of your data governance strategy.
In the past, enhancing data security often meant compromising efficiency, but ALTR changed the game. By automating data classification, access governance, and policy enforcement, ALTR frees your teams from the quagmire of manual processes. This means less time spent on routine data governance tasks and more time available for strategic initiatives that drive business growth. Operational efficiency isn't just about doing things faster; it's about doing them more innovative, and that's precisely what ALTR enables.
Knowledge is power, especially when managing and protecting your data. ALTR doesn't just secure your data; it shines a light on it, offering unprecedented insights into how, when, and by whom your data is accessed. These insights aren't just numbers and graphs; they're actionable intelligence that can inform your data governance policies, identify potential security risks, and uncover opportunities to optimize data usage. With ALTR, data insights become a strategic asset, driving informed decision-making across the organization.
Stop struggling with the relics of the past. It's time to embrace the future of data governance with ALTR, where data security, compliance, efficiency, and insights converge to propel your organization into a new era of digital excellence.
In an era where digital footprints are more significant than ever, the question isn't whether you should revisit your data security policy but how urgently you need to do so. With escalating cyber threats, evolving compliance landscapes, and sophisticated hacking techniques, the sanctity of data security has never been more precarious. As we navigate this digital dilemma, it's imperative to ask: Is your data security policy robust enough to withstand the challenges of today's cyber ecosystem?
Recent years have witnessed an unprecedented spike in cyberattacks, targeting not just large corporations but small businesses and individuals alike. From ransomware attacks that lock out users from their own data to phishing scams that trick individuals into handing over sensitive information, the arsenal of cybercriminals is both vast and evolving. The question remains: Is your current data security policy equipped to fend off these modern-day digital marauders?
As if the threat landscape wasn't daunting enough, businesses today also grapple with a labyrinth of regulatory requirements. GDPR, CCPA, and HIPAA - the alphabet soup of data protection laws- are confusing and comprehensive. Each of these regulations mandates stringent data protection measures, and non-compliance can result in hefty fines and irreparable damage to reputation. It's crucial for your data security policy to not only protect against cyber threats but also ensure compliance with these ever-changing legal frameworks.
Perhaps the most unpredictable aspect of data security is the human element. Studies suggest that many data breaches result from human error or insider threats. Whether a well-meaning employee clicking on a malicious link or a disgruntled worker leaking sensitive information, the human factor can often be the weakest link in your data security chain. A robust data security policy must address this variability, incorporating comprehensive training programs and strict access controls to mitigate the risk of human-induced breaches.
The rapid advancement of technology brings with it new challenges in data security. The rise of IoT devices, the proliferation of cloud computing, and the advent of AI and machine learning have opened new frontiers for cybercriminals to exploit. Each of these technologies, while transformative, also introduces new vulnerabilities. Data security policies must evolve in tandem with these technological advancements, ensuring they address the unique challenges posed by each new wave of innovation.
So, what does a robust data security policy look like today? Here are the key elements:
Clearly defines the reasons behind the policy, such as protecting sensitive information, ensuring privacy, and complying with legal and regulatory requirements.
Outlines the extent of the policy's applicability, specifying which data, systems, personnel, and departments are covered. It should clarify whether the policy applies to all data types or only specific classifications and whether it includes both digital and physical data formats.
Establishes categories for data based on its sensitivity and the level of protection it requires. Common classifications include Public, Internal Use Only, Confidential, and Highly Confidential.
Specifies handling requirements for each classification level, including storage, transmission, and sharing protocols. This ensures that more sensitive data receives higher levels of protection.
Identifies individuals or departments responsible for different types of data, outlining their responsibilities regarding data accuracy, access control, and compliance with the security policy.
Defines the role of the security team or Chief Information Security Officer (CISO) in overseeing and enforcing the data security policy.
Clarifies the responsibilities of general users, including adherence to security practices, reporting suspected breaches, and understanding the implications of policy violations.
Details the mechanisms for granting, reviewing, and revoking access to data, ensuring that individuals have access only to the data necessary for their role.
Outlines the authentication protocols required to access different types of data, including multi-factor authentication, passwords, and biometric verification.
Specifies when and how data should be encrypted, particularly for sensitive information in transit and at rest.
Addresses the protection of physical assets, including servers, data centers, and paper records, outlining measures like access control systems and surveillance.
Covers security measures for user devices that access the organization's network, including antivirus software, firewalls, and secure configurations.
Defines how long different types of data should be retained based on legal, regulatory, and business requirements.
Details methods for securely disposing of no longer needed data, ensuring that it cannot be recovered or reconstructed.
A clear, step-by-step guide for responding to data security incidents, including identification, containment, eradication, recovery, and post-incident analysis.
Outlines the procedure for reporting security incidents, including who should be notified and in what timeframe.
Mandates ongoing security awareness training for all employees, tailored to their specific roles and the data they handle.
Includes initiatives to keep data security in mind for employees, such as regular updates, posters, and security tips.
Establishes a regular schedule for reviewing and updating the data security policy to ensure it remains relevant in changing threats, technologies, and business practices.
Describes the process for proposing, reviewing, and implementing amendments to the policy, ensuring that changes are documented and communicated to all relevant parties.
Identifies relevant legal and regulatory requirements that the policy helps to address, such as GDPR, HIPAA, or PCI DSS.
Outlines the legal implications of policy violations for the organization and individual employees, including potential penalties and disciplinary actions.
In light of the evolving threat landscape and the complex regulatory environment, revisiting your data security policy is not just advisable; it's imperative. The cost of complacency can be catastrophic, ranging from financial losses to a tarnished reputation and legal repercussions. The time to act is now. By fortifying your defenses, staying abreast of regulatory changes, and fostering a culture of security, you can safeguard your organization against the multifaceted threats of the digital age. Remember, in data security, vigilance is not just a virtue; it's a necessity.
Protecting sensitive data is paramount in today's digital landscape. But choosing the proper armor for the job can be confusing. Two major contenders dominate the data governance and data security ring: Format-preserving Encryption (FPE) and Tokenization. While both seek to safeguard information, their mechanisms and target scenarios differ significantly.
Format-preserving encryption is a cryptographic technique that secures sensitive data while preserving its original structure and layout. FPE achieves this by transforming plaintext data into ciphertext within the same format, ensuring compatibility with existing data structures and applications. Unlike traditional encryption methods, which often produce ciphertext of different lengths and formats, FPE generates ciphertext that mirrors the length and character set of the original plaintext.
FPE allows companies to encrypt sensitive data while preserving the format required by existing systems, applications, or databases. This means they can integrate encryption without needing to extensively modify their data structures or application logic, minimizing disruption and avoiding potential errors or system failures arising from significant changes to established data formats or application workflows.
In some cases, the functionality of applications or systems may rely on specific data formats. FPE allows companies to encrypt data while preserving this functionality, ensuring that encrypted data can still be used effectively by applications and processes.
FPE algorithms are designed to be efficient and fast, allowing for encryption and decryption operations to be performed with minimal impact on system performance. This is particularly important for applications and systems where performance is critical.
When migrating data between different systems or platforms, maintaining the original data format can be essential to ensure compatibility and functionality. FPE allows companies to encrypt data during migration while preserving its format, simplifying the migration process.
Tokenization is a data protection technique that replaces sensitive information with randomly generated tokens. Unlike format-preserving encryption, which uses algorithms to transform data into ciphertext, tokenization uses a non-mathematical approach. Instead, it generates a unique token for each piece of sensitive information and stores sensitive information in a secure database or token vault (read more about ALTR's PCI compliant vaulted tokenization offering). The original data is then replaced with the corresponding token, removing any direct association between the sensitive information and its tokenized form.
Tokenization helps improve security by replacing sensitive data such as credit card numbers, bank account details, or personal identification information with tokens. Since tokens have no intrinsic value and are meaningless outside the system they're used in, malicious actors cannot exploit them even if intercepted.
Scalability is a crucial strength of tokenization systems, stemming from their straightforward mapping of original data to tokens. This simplicity enables easy management and facilitates seamless scalability, empowering companies to manage substantial transaction volumes and data loads without compromising security or performance, all while minimizing overhead. This scalability is especially vital in sectors with high transaction rates, like finance and e-commerce, where robust and efficient data handling is paramount.
Tokenization can facilitate interoperability between different systems and platforms by providing a standardized method for representing and exchanging sensitive data without compromising security.
Tokenization systems often offer straightforward integration with existing IT infrastructure and applications. Many tokenization solutions provide APIs or libraries, allowing developers to incorporate tokenization into their systems easily. This ease of integration can simplify adoption and reduce development time drastically.
Consider a financial institution that needs to securely store and process credit card numbers for various internal systems and applications. Instead of encrypting the credit card numbers, which could potentially disrupt downstream processes that rely on the original format, the company opts for tokenization.
Here's how it could work: When a credit card number is created or updated, the unique and identifiable numbers are replaced with randomly generated tokens. These tokens are then used to reference the original sensitive information, securely stored in a separate database or system with strict access controls.
When authorized personnel need to access or use the encrypted credit card numbers for legitimate purposes, they can retrieve the tokens and use them to access the stored sensitive information. This allows the company to maintain compatibility with existing systems and processes that rely on the specific format of credit card numbers, such as payment processing or customer account management.
By implementing tokenization in this scenario, the organization can streamline access to data while ensuring that sensitive information remains protected.
One scenario where a company might choose format-preserving encryption (FPE) over tokenization is in the context of protecting sensitive data while preserving its format and structure for specific business processes.
Imagine a healthcare organization that needs to securely store and share patient records containing personally identifiable information, such as names, addresses, and medical histories. Instead of tokenizing the entire document, which could slow down access and processing times, the organization decided to encrypt specific fields within the documents containing sensitive information.
Here's how it could work: When a patient record is entered into the system, FPE is applied to encrypt sensitive fields, such as patient name, address, and medical record number, while preserving its original format. The encrypted data maintains the same structure, length, and validation rules as the original fields.
When authorized personnel need to access the patient records for legitimate purposes , they can decrypt them using the appropriate encryption keys. This allows for efficient retrieval and processing of data without compromising security.
By using FPE in this scenario, the company can ensure that sensitive data remains protected while maintaining the integrity and usability of the data within its business operations. This approach balances security and functionality, allowing the company to meet data protection requirements without sacrificing operational efficiency or compatibility with existing systems.
Format-Preserving Encryption (FPE) and Tokenization offer practical strategies for securing sensitive data. By understanding each technique's unique advantages and considerations, organizations can make informed decisions to safeguard their data, protect against potential threats, and foster trust with customers and stakeholders.
In the ever-evolving landscape of data security, the debate between Vault and Vaultless tokenization has gained prominence. Both methods aim to protect sensitive information, but they take distinct approaches, each with different sets of advantages and limitations. In this blog, we will dive into the core differences that organizations consider when choosing an approach and how ALTR makes it easier to leverage the enhanced security of Vault Tokenization while still allowing for the scalability you'd typically find with Vaultless Tokenization. This decision ultimately comes down to performance, scalability, security, compliance, and total cost of ownership.
Tokenization (both Vaulted and Vaultless), at its core, is the process of replacing sensitive data with unique identifiers or tokens. This ensures that even if a token is intercepted, it holds no intrinsic value to the interceptor without the corresponding key, which is stored in a secure vault or system.
Vaulted (or “Vault”) tokenization relies on a centralized repository, known as a vault, to store the original data. The tokenization process involves generating a unique token for each piece of sensitive information, while securely storing the actual data in the vault. Access to the vault is tightly controlled, ensuring only authorized entities can retrieve or decrypt the original data. For maximum security, the token should have no mathematical relationship to the underlying data; thus, preventing brute force algorithmic hacking, as can be possible when purely relying on encryption. Securing data in a vault helps reduce the surface area of systems that need to remain in regulatory compliance (ex. SOC 2, PCI- DSS, HIPAA, etc.), by ensuring the sensitive data located in the source system is fully replaced with non-sensitive values, thus requiring no compliance controls to maintain security.
The primary technical differentiator between Vaulted and Vaultless Tokenization is the centralization of data storage in a secure vault. This centralized storing method guarantees security and simplifies management and control, but may lead to concerns around scalability, and performance.
Vaulted tokenization shines in scenarios where centralized control and compliance are paramount. Industries with stringent regulatory requirements often find comfort in the centralized security model of vaulted tokenization.
Vaultless tokenization, on the other hand, distributes the responsibility of tokenization across various endpoints or systems all within the core source data repository. In this approach, the generation and management of tokens occurs locally, eliminating the need for a centralized vault to store the original data. Each endpoint independently tokenizes and detokenizes data without relying on a central authority. While Vaultless Tokenization has a technically secure approach, this solution relies on tokenizing and detokenizing data from within the same source system. Similarly, this solution is less standardized across the industry and may result in vulnerability to compliance requirements around observability and proving that data stored locally is sufficiently protected.
The decentralized nature of Vaultless tokenization enhances fault tolerance and reduces the risk of a single point of failure from a compromised vault. However, it introduces the challenge of ensuring consistent tokenization across distributed systems and guaranteeing data security and regulatory compliance.
While each approach has its merits, the ideal data security solution lies in striking a balance that combines the security of Vaulted Tokenization with the performance and scalability of Vaultless Tokenization. A hybrid model aims to leverage the strengths of both methods, offering robust protection without sacrificing efficiency, performance, industry norms, or compliance regulations.
ALTR’s Vault tokenization solution is a REST API based approach for interacting with our highly secure and performant Vault. As a pure SaaS offering, utilizing ALTR’s tokenization tool requires zero physical installation, and enables users to begin tokenizing or detokenizing their data in minutes. ALTR’s solution leverages the auto-scaling nature of the cloud, enabling on-demand performance that can immediately scale up or down based on usage.
ALTR’s Vaulted Tokenization enhances the security and performance of sensitive data by being a SaaS delivered tool and having an advanced relationship with Amazon Web Services. Because of ALTR’s interoperability, many constraints of Vaulted Tokenization have been removed by properly building a scalable vault using cloud resources. ALTR can perform millions of tokenization and detokenization operations per minute per client basis without having the need for a Vaultless type of local implementation.
In conclusion, the relative differences between Vaulted and Vaultless Tokenization underscore the importance of a nuanced approach to data security. The evolving landscape calls for solutions that marry the robust protection of a vault with the agility and scalability of a cloud-native SaaS model. ALTR’s Vault tokenization solution enables this unique offering by combining cloud-native scalability and ease-of setup / maintenance, with a tightly controlled, compliance optimized vault (PCI Level 1 DSS and SOC 2 type 2 certifications). Striking this balance ensures that organizations can navigate the complexities of modern data handling, safeguarding sensitive information without compromising performance or scalability.
