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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 One wrong move, and you could be staring down the barrel of a data breach caused by an outdated policy.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 TL;DR 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:
The Ever-Shifting Regulatory Landscape: 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.
Unlocking the Potential of AI: 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.
Democratizing Data for a Data-Driven Culture: 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.
Optimizing for Efficiency and Agility: 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:
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.
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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.
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 TL;DR 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:
The Ever-Shifting Regulatory Landscape: 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.
Unlocking the Potential of AI: 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.
Democratizing Data for a Data-Driven Culture: 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.
Optimizing for Efficiency and Agility: 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:
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:
Map the Maze: 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.
Empower Your Customers: 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.
Embrace Transparency: 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.
Invest in Security Measures: Implement robust security measures to protect customer data from unauthorized access, disclosure, or destruction.
Stay Agile: 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:
Freed from the shackles of routine data governance tasks, data teams can pivot towards more strategic and value-driven initiatives. Here are some of the compelling opportunities that could unfold:
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 governance 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.
Here's why ALTR is the ultimate upgrade for your data governance arsenal:
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:
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.
Compatibility: 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.
Preserving Functionality: 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.
Performance: 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.
Data Migration: 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.
Enhanced Security: 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: 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.
Interoperability: Tokenization can facilitate interoperability between different systems and platforms by providing a standardized method for representing and exchanging sensitive data without compromising security.
System Integration: 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.
In today's digital age, data is the lifeblood of businesses and organizations. Safeguarding its integrity and ensuring it stays in the right hands is paramount. The responsibility for this critical task falls squarely on the shoulders of effective data access control systems, which govern who can access, modify, or delete sensitive information. However, like any security system, access controls can weaken over time, exposing and making your data vulnerable. So, how can you spot the warning signs of a deteriorating data access control process? In this blog, we'll uncover the telltale indicators that your data access control is on shaky ground.
It's undeniable that a data breach or leak is the most glaring and alarming indicator of your data access control's downfall. When unauthorized parties manage to infiltrate your sensitive information, it's akin to waving a red flag and shouting, "Wake up!" The unmistakable sign points to glaring vulnerabilities within your access control systems. These breaches bring dire consequences, including reputational damage, hefty fines, and the substantial erosion of customer trust. With the global average cost of a data breach at a staggering USD 4.45 million, it's most certainly something you want to avoid.
Do you find yourself with pockets of data hidden in different departments or applications, making it inaccessible to those who genuinely need it? This phenomenon creates data silos that obstruct collaboration and efficiency. Moreover, it complicates access control management, as each data silo may function under its own potentially inconsistent set of rules and protocols.
Does anyone within your organization "own" data, ensuring its proper use and security? Vague ownership fosters a culture where everyone feels entitled to access, making it difficult to track user activity, identify responsible parties in case of misuse, and enforce access control policies.
If access permissions are manually granted and updated, it's a clear sign that your access control system is outdated. Manual processes are time-consuming, error-prone, and hardly scalable. They create bottlenecks that delay legitimate users' access while increasing the risk of inadvertently granting unauthorized access. It's high time to transition to automated access control solutions to keep pace with the evolving demands of data security.
According to recent data, IT security decision-makers say 77% of developers have excessive privileges. This concerning statistic underscores the importance of scrutinizing our data access control practices. Are access permissions infrequently reviewed and adjusted to align with evolving roles and responsibilities? Failing to conduct regular reviews results in outdated permissions persisting, needlessly granting access to individuals who no longer require it. Hence, conducting frequent audits becomes imperative, not only for identifying potential vulnerabilities but also for ensuring compliance with stringent regulations.
Weak password practices, such as using easily guessable passwords, sharing passwords, or infrequently updating them, undermine the very foundation of data security. Data breaches often begin with compromised credentials, underscoring the critical importance of robust password policies and multi-factor authentication.
If users frequently request elevated access privileges to carry out their tasks, it suggests a deficiency in role-based access control (RBAC). RBAC assigns permissions based on roles and responsibilities, minimizing the need for escalated access and reducing the risk of misuse.
Are employees using unauthorized applications or cloud storage solutions to access and share data? Shadow IT bypasses established security controls, creating blind spots and escalating the risk of data leaks. The implementation of sanctioned alternatives and enforcement of their use is paramount.
Does your organization handle sensitive data subject to stringent regulations like HIPAA, GDPR, or PCI DSS? Failure to comply with these regulations can result in substantial fines and reputational harm. Aligning your access controls with regulatory requirements is imperative to avoid hefty penalties.
Is it challenging to track user activity and pinpoint the source of data breaches or leaks? How long after an incident or breach is your team notified? Without proper logging and auditing, investigating incidents becomes a time-consuming and frustrating endeavor. Effective logging and monitoring are prerequisites for quickly identifying and responding to security threats.
If you recognize any of these red flags within your data access control system, it's time to take decisive action. Here are some steps to strengthen your data access control:
Remember, data access control is an ongoing process, not a one-time fix. By heeding the warning signs and taking proactive measures, you can ensure that your data remains secure, protected from unauthorized access, and in the right hands, safeguarding your organization and its stakeholders.
One of ALTR’s customers, a health, well-being, and navigation company, operates in over 190 countries worldwide, offering employee well-being and engagement solutions to organizations and their employees across the globe. Their mission is to transform workplace culture, promoting physical, mental, and emotional well-being to foster healthier and more productive work environments.
In their quest to maintain the highest data governance and security standards, this organization embarked on a mission to securely store Personal Health Information (PHI) and Personally Identifiable Information (PII) data within Snowflake. This endeavor aimed to empower internal users with insightful access to data while simultaneously ensuring the establishment of a robust, closed-loop audit trail to meet stringent compliance requirements.
The InfoSec Team at this company grappled with the critical necessity of securely and confidentially housing their sensitive PHI and PII data. This imperative arose from their unwavering commitment to conforming to stringent regulatory frameworks and compliance mandates that govern handling such sensitive information within the health and wellness industry. The integrity and confidentiality of this data were paramount.
With an expansive and intricate data landscape sprawling across multiple Snowflake databases, they faced the formidable challenge of implementing and enforcing data governance policies at scale. The sheer volume of tagged columns, numbering in the thousands, necessitated an innovative approach to ensure the consistent and efficient application of governance protocols.
To align comprehensively with evolving data privacy regulations, this organization recognized the need to establish a meticulous and all-encompassing audit trail. This trail would serve as an indisputable record of every instance of access to sensitive data. Achieving full compliance required not just meeting the letter of the law but also demonstrating dedication to transparency and accountability in the data handling practices.
Implemented as a cloud-native solution and utilizing Snowflake's native governance and security features, ALTR offered the highest level of data protection—all with no code required to implement, maintain, or manage. Removing the roadblocks to protecting sensitive data ensures this organization’s data team can extract the most value from their data and maximize their investment in the platform.
ALTR introduced automated tag-based column control to govern PII and PHI data security at scale. With ALTR's user-friendly point-and-click interface and management API, this organization was able to harness the power of Snowflake object tagging and enable the automatic application of data masking to thousands of tagged columns spanning multiple Snowflake databases. As a result, they were able to apply policies uniformly to corresponding tagged columns quickly and easily and instantly enforce policies as soon as sensitive data is tagged.
ALTR's auditable query logs emerged as an indispensable tool, meticulously documenting every instance of sensitive PHI and PII data access to prove privacy controls were effective. This company can now govern each user down to the individual query, track and log all activity, including administrative actions and implement rules and thresholds to govern the flow of data.
This organization meticulously achieved a state of complete data observability, which has become the cornerstone of their data security framework. This heightened level of transparency not only fortified their data security infrastructure but also enabled them to proactively monitor, track, and respond to all instances of access to sensitive PHI and PII data. As a result, no unauthorized or suspicious activities go unnoticed, providing an invaluable layer of protection for their most critical information assets.
ALTR's solution empowered this customer to seamlessly automate data masking policies across a sprawling landscape of tagged columns spanning multiple Snowflake databases. This automation substantially reduced manual efforts and contributed to policy consistency and effectiveness.
The solution delivered an exhaustive audit trail that meticulously documented every instance of sensitive data access. This comprehensive audit trail played a pivotal role in this organization’s ability to fully satisfy the requirements of data privacy regulations and compliance standards.
This company's proactive embrace of tag-based policies and their astute utilization of automation exemplified their forward-thinking approach to data governance and significantly influenced the evolution of ALTR's data governance capabilities.
ALTR's easy-to-use solution allows our Data, Reporting and Analytics teams to leverage Snowflake object tagging to automatically apply data masking to thousands of tagged columns across multiple Snowflake data bases. We're able to store PII/PHI data securely and privately with a complete audit trail. Our internal users gain insight from this masked data and change lives for good.
- Director of Data Governance and Management
