Over the past few decades, many organizations have adopted data loss prevention (DLP) solutions to secure their sensitive data. This initially worked in the early days of the Internet, but the reality is that legacy DLP solutions can’t effectively prevent data loss in today’s cloud-first landscape.
As companies are using more cloud-based tools, network boundaries are fading and it’s no longer enough to protect data that’s leaving the perimeter. In turn, they’re adopting different types of enterprise DLP solutions — such as email, endpoint, and network — but these still aren’t enough to deal with the cloud data landscape.
Cloud DLP is emerging, but it’s creating a new silo and segmentation that makes securing data a challenge. An effective data security tool requires next-generation capabilities for data loss prevention across different cloud platforms, container environments, virtual machines, and more.
Here’s what it takes to protect sensitive data in the modern cloud era.
Why Companies Should Consider DLP Alternatives
Traditional DLP solutions were created in the ‘90s and initially solved what they set out to do: protect sensitive data from leaving the organization via email or endpoints like laptops and mobile devices. DLPs worked by simply blocking certain data from crossing network borders without authorization.
In the past, legacy DLPs were effective because emails, text documents, images, and other files were within a well-defined perimeter and could be more easily classified as sensitive or not. The problem is that cloud technologies have introduced more freedom and flexibility for business units and development teams, making most enterprise infrastructure borderless.
A variety of applications used for collaboration, partner enablement, and customer engagement have also led to a proliferation of information that makes it very difficult to classify data without considering contextual information. Even cloud DLPs, built upon existing DLP technologies, then adapted for this borderless and data-centric era, cannot properly protect data without additional tools and context.
Instead of using DLPs and other solutions that protect email, endpoints, the network, or the cloud, companies should consider an alternative that focuses on understanding and securing the data itself. This means automatically detecting and protecting sensitive data everywhere, even as cloud environments rapidly change and companies continue to generate more data.
7 Essential Capabilities for Cloud Data Loss Protection
Here’s what you should look for when choosing a data security solution that can scale to the data volumes and sprawl that the cloud era is introducing.
1. Protects Data Everywhere
Most organizations today have sprawling cloud environments with sensitive data everywhere, which makes the traditional DLP approach obsolete. It’s no longer enough to protect data that’s crossing network borders because modern infrastructure is a complex web of cloud services, SaaS applications, APIs, and more.
An effective data security solution needs to be able to continuously and automatically discover data wherever it resides, including cloud storage buckets, databases, containers, virtual machines, and SaaS applications. This is crucial for implementing data security across hybrid environments that are constantly evolving.
2. Automatically Detects Sensitive Data
While traditional DLPs prevented certain structured data that security teams identified from leaving points of control by defining regular expressions to classify data, these solutions weren’t actually aware of where data was stored and whether it was sensitive. That means DLPs couldn’t easily protect large amounts of unregulated and unstructured data because they didn’t have enough contextual understanding to correctly apply security policies.
An effective data security solution should be able to automatically discover and classify sensitive data anywhere, including data in unexpected locations. This also includes raw data that’s less obvious to classify as sensitive, which is especially important as companies continue to amass more unstructured data over time.
3. Enables Custom Data Policies
Many legacy DLP solutions either allowed or block data access, but this is highly restrictive when companies have sprawling cloud environments. Data might be high or low risk depending on the unique security requirements of the organization or industry. DLPs lack the flexibility and contextual awareness to dynamically apply security policies that make sense in specific situations and end up giving too many false positives.
Instead, an effective data security solution should consider where the data is stored, the sensitivity of the data, the context in which it’s being accessed, and other factors when applying security policies. This means giving security teams tools for creating custom data policies that align with broader security, compliance, and regulatory frameworks.
4. Integrates with Leading Tools & Platforms
Legacy DLPs were notoriously difficult to implement because the security team would usually need to map out all possible data paths and access rights, which was a highly manual and labor-intensive task. Today’s employees use far too many tools and devices for this approach to be realistic.
A modern data security solution needs to be able to easily integrate into existing workflows and toolchains to accelerate onboarding. This requires out-of-the-box integrations with a variety of tools and platforms, including data catalogs, workflow tools, identity management platforms, SIEM solutions, and more.
5. Identifies and Remediates Potential Risks
Legacy DLPs focus on protecting sensitive data, but they do little to help security teams understand what data is actually vulnerable. This is especially a problem because today’s data sprawl has created a much larger attack surface for security teams to manage.
By taking a proactive approach with data security posture management (DSPM) capabilities, a modern data security solution can help organizations improve their cyber resilience. DSPM involves detecting vulnerabilities and prioritizing remediation efforts so that security teams can respond to potential security threats faster.
6. Balances Data Access with Security
Since employees now communicate using a wide variety of tools and devices, it’s difficult to define all the different ways data could be transferred and then secure these using a legacy DLP. This means security teams often implement overly restrictive data policies, and in turn, face a lot of alerts and access requests. When employees can’t transfer or access the data they need, they’ll constantly turn to security teams for permission or find ways to circumvent the DLP restrictions altogether.
An effective data security solution can intelligently apply governance policies based on contextual information to maintain a strong security posture while also providing adequate access to those who need it. This can help security teams avoid constant security alerts and reduce their workload. At the same time, employees can remain productive without the need to avoid overly strict security restrictions.
7. Continuously Monitors for Data Landscape Changes
Many legacy DLPs require security teams to classify sensitive data manually, which isn’t effective for protecting the vast amounts of data that companies collect, share, and store each day. This meant that DLPs adapt to changes too slowly, and often leave new data stores vulnerable.
A data security solution needs to continuously monitor for changes to keep up with the dynamic nature of data. Using machine learning and other advanced techniques, a modern solution can identify data stores containing PII, PHI, PCI DSS, IP data, or other secrets. This is crucial for staying on top of potential data security risks.
Cloud Data Loss Prevention with Cyera
In the cloud-first era — where network boundaries are no longer a reliable place to enforce information security policies — companies need to look to a modern data security solution as a DLP alternative. This requires a tool that can automatically adapt to an ever-changing data landscape and protect a growing amount of unstructured data.
Cyera is a holistic data security solution that can effectively protect data in the modern cloud-native world. By automatically detecting and classifying data across all environments, Cyera can create a sensitive data inventory and maintain a contextualized data risk assessment. Then the platform can enforce appropriate security controls on this sensitive data to ensure cloud data loss prevention.
In addition, Cyera includes robust DSPM capabilities that highlight data protection issues and prioritize appropriate actions to mitigate them. The platform provides automated remediate workflows based on established security risk, regulatory, and compliance frameworks to help security teams improve their cloud data security posture.
To learn more about cloud data loss prevention with Cyera, schedule a demo, or request a data risk assessment.
