When Did Cloud Data Storage Get So Messy?
Liat Hayun, Co-founder & CEO of Eureka Security
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In a recent chat with Eureka Security Advisor Andy Ellis, we explored the current urgency around enterprise cloud data store security and three major factors that led us to this point.
These elements of digital transformation empowered many enterprise teams to adopt different cloud data storage solutions that cater to highly specified needs and accelerate their productivity. As data and data-flows gained importance across core business operations and development, a great deal of data was moved, replicated and generated across multiple data stores with increasing speed and efficiency.
Multiplying and Slipping Through the Cracks
It’s important to understand the basis for this replication–despite the data itself remaining mostly the same, it often isn’t cloud-agnostic. Given that the architecture of cloud storage solutions widely differs from one to the next, and data must be available across multi-cloud systems, it’s often easiest to simply replicate data to fit each type of infrastructure.
By design, enterprise security teams are not notified on small changes, meaning that the quick task of moving and replicating data often takes place beyond their knowledge. This includes most instances in which time data is opened up to different teams and for different uses on a new data store, despite new data stores requiring their own tech stacks and dedicated knowledge for proper protection.
Security teams must understand each data store’s requirements based on what's in it and how it's being used in order to understand how to properly secure it and comply with standing policies. Given that the vast majority of today’s enterprises are multicloud, and that this trend is growing exponentially alongside the ease of replication, this issue has quickly spiraled beyond most security teams’ control.
Even security teams that manage to proactively track multiplying datasets struggle, as they lack an effective means of doing so. Most still rely on rudimentary and manual processes, such as excel spreadsheets, to manage this highly complicated and fast-moving issue. For especially data-driven enterprises, using this method to track all assets and inventory, as well as what has and will need to happen to them, is predictably unreliable.
Additional factors
The human talent gap contributes to many of cybersecurity’s present-day issues, and cloud data store security is no exception. This is especially true where aforementioned manual processes are involved.
The situation can also be further exacerbated in companies that prioritize scale over security. In such instances, enterprises mindfully set security considerations aside to grow as quickly as possible under the assumption that retroactive security is possible. However, without the right tools,it isn’t, leaving such enterprises critically exposed.
Very quick cloud migrations, as well as M&A processes, are other common contributing factors.
Getting Ahold of the Situation
Security teams need a way to keep up with and get ahead of their growing data sprawls–both in terms of understanding where their data is and what each store requires. At Eureka, we propose a three-pronged approach for this.
Where is the data?
First, it’s important to continuously see an entire map of data stores housing enterprise data in a manner befitting the speed at which they’re being generated. This is where handy automation in place of outdated manual procedures can help security teams make enormous strides in their data security. It is the only way they can enjoy the visibility they urgently need with a complete inventory and log of where enterprise data is, where it has moved to and what’s already been done to it.
What is the data?
Next, it is also critical to understand what’s in each of those data stores to understand what to do with them. Which are near-empty, and which store significant amounts of PII? This is where we move deeper into the potential of what true visibility means–security teams would benefit from simplified means to appreciate the risk of each asset under their protection. This is critical for informing the individual requirements they must to each data set across in each data store and prioritize which most urgently require security team intervention and resources.
What are the requirements?
Security teams must then understand the organizational policies they will have to put in place around data–meaning how data must be managed, controlled and protected–and then implement those requirements across the different technologies and multiple data stores used by their enterprise.
Once each requirement is known, it is important to appreciate further complications in practically applying them. Some data might necessitate special consultations with the rest of the enterprise’s departments and teams to ensure that efforts to safeguard information they need doesn’t prohibit their productivity. Oftentimes, this step requires some degree of negotiation, as certain protections are incompatible with the way the way the data in question must be used.
In other words, in addition to having to apply company and regulatory policies across different technologies, some data sets will also require their own unique set of rules. It’s critical for security teams to track such exceptions properly.
Staying in Charge of the Situation
Data isn’t static. This is precisely why the excel spreadsheet method guarantees outdated information. The same can be expected of any type of inventory that doesn’t offer continuous visibility and automatic updates.
Moreover, policy hardly remains static either. Whether internally or externally influenced, such as where regulatory compliance is concerned, policy has a tendency to evolve. This means that security teams must reliably track what has already been done to their data to ensure that their actions around it are up to the latest standard. As a bonus, continuous updates ensure healthy audits.
It is also important to keep ahead of configuration drift with detection and preventative controls. For some reason or other, it isn’t uncommon to find that a system is no longer configured the way security teams originally meant to leave it. It is critical for teams to catch such drift as early and quickly as possible.
Cloud Data Security Posture Management
Cloud Data Security Posture Management is a holistic approach to keeping all data residing in enterprise cloud data stores secure, regardless of where it is or how it got there, and without requiring deep expertise across how each data store operates.
Our Cloud Data Security Posture Management tool accomplishes this by
Informed by many conversations like the one we had with Andy, Eureka put a great deal of thought, strategy and passion for meaningful change in data security into the way we’ve designed this new tool. We are also constantly checking in with our design partners to ensure that this remains on track to resolve one of their biggest challenges around exposure.
