Data Security Posture Management Explained
The rapid adoption of cloud-based technologies, data democratization initiatives, and the demise of the traditional network perimeter are crucial to doing business in the modern world. But these advances have created new security vulnerabilities. To mitigate the security risks inherent in highly distributed, multi-cloud environments, organizations require a robust data security stance.
Data security posture management (DSPM) helps organizations proactively manage data security risks and threats by continuously assessing their data security posture, and identifying vulnerabilities and the controls necessary to reduce those risks. In this guide, we’ll define data security posture management, explain its core components, and explore the challenges associated with implementing it. In closing, we’ll share the essential features to look for in a data security platform that will effectively support your data security posture management program.
What Is Data Security Posture Management (DSPM)?
Modern organizations process and store a variety of sensitive data, including personally identifiable information (PII), protected health information (PHI), financial information, and business-critical intellectual property. That data is often spread out across a patchwork of databases, applications, SaaS solutions, and more.
Data security posture management is a security approach that focuses on securing sensitive data where it lives, and automating data discovery, classification, monitoring, detection, and protection tasks. As the volume and complexity of data increase, data security posture management helps businesses understand where their sensitive data resides, who has access to that data, how it’s being used, and what actions need to be taken to properly protect it.
Core Components of Data Security Posture Management
Data security posture management includes four key components, which work together to create a comprehensive, integrated process for protecting sensitive data.
1. Data Discovery and Classification
Without an accurate understanding of where sensitive data lives, protecting it is impossible. The goal of data discovery is to create an accurate inventory of all sensitive data and identify where it’s being stored. All data should be classified based on the regulatory frameworks that govern it. With this understanding, the business can more easily determine who should have access to the data and what protections should be in place to secure it.
2. Data Prioritization
Once sensitive data has been identified, it’s prioritized according to its level of sensitivity and the degree to which it’s currently vulnerable to compromise. Data prioritization helps security teams map out potential attack paths and see which data requires the most urgent attention.
3. Data Security Risk Remediation
Data security risk remediation begins with finding every vulnerability that exists within the environment. This process may involve using automated tools that perform regular checks against relevant industry data security standards such as the GDPR and SOC 2. It may also include creating custom risk detection rules tailored to the specific data security needs of the business.
4. Data Monitoring
Protecting sensitive data is an iterative cycle that involves continuously scanning for new data stores and detecting emerging threats to the security of existing data. Data monitoring is the process of overseeing how the organization is collecting, storing, and operationalizing its data. Monitoring is meant to provide teams with a holistic view of their data and how, why, and where the data is being used.
Data Security Posture Management vs. Data Security Platform
What’s the difference between data security posture management solutions and data security platforms? In addition to having similar acronyms – DSPM vs. DSP – both provide means through which to protect data. But there is an important point of differentiation.
Data security posture management can be thought of as complementary to a data security platform. The former enables functions like data discovery, classification, and monitoring for unstructured data, while the latter bolsters those capabilities for structured data, through data access control. This is key because organizations need a mechanism through which to identify their sensitive data, monitor its use, and put the right controls in place to protect it. DSPM only offers two of those three capabilities, leaving a gap in the data security spectrum.
Challenges Associated with DSPM Implementation
Data security posture management is an important part of a comprehensive data security strategy, but it can be challenging to implement. Here are three common reasons that a DSPM initiative may fail.
One of the primary challenges of data security posture management is the complexity involved in implementation. Setting up the necessary tools, customizing the configurations, and incorporating technology into existing security infrastructure can require a significant investment of time and resources. Additionally, without adequate experience or a comprehensive evaluation of the organization’s data, a team may overlook a data store or component of the infrastructure, leaving data open to compromise.
Automated data security solutions can generate false positives. When detection mechanisms are too sensitive, alerts may be triggered by legitimate actions or events. This can result in alert fatigue, increasing the workload for security personnel and eroding trust in the system.
Data security posture management may not provide comprehensive coverage of all possible attack vectors or vulnerabilities, necessitating the use of additional tools including threat intelligence, user behavior analytics, and/or penetration testing. In addition, some platforms focus only on data stored in public clouds, leaving sensitive data stored in other environments inadequately protected.
Key Features of a Data Security Posture Management Solution
A strong data security platform data is vital for implementing data security posture management. These five features help ensure organizations can adequately identify, protect, and monitor sensitive data.
Advanced Data Discovery and Classification
For the strongest security posture, you’ll need to discover and classify all sensitive data stored in all formats including structured, semi-structured, and unstructured data — without having to manually write complex rules and regex patterns. Your data security platform should be capable of working across petabytes of data, scanning the business’s entire data architecture and creating a data flow map that provides a complete view of how data is distributed across all cloud and on-premises data stores, SaaS providers, and applications. It should autonomously identify all sensitive data at risk of compromise due to inappropriate entitlements and patterns of data sharing that violate data security protocols.
Customizable Data Prioritization
Immediately resolving all data security risks isn’t a reasonable goal for even the most well-staffed security team. Your data security platform should help you identify the data presenting the greatest risks through the use of customizable risk indicators. You must have the ability to define what data is restricted or confidential in your unique context. Once sensitive data has been identified, your security solution should offer guided remediation with suggested next steps for protecting exposed data.
Robust Access Controls
A robust data security platform consistently enforces security policies across the full breadth of systems and applications that contain sensitive data stores. Some solutions offer templates developed using data security and privacy best practices informed by industry experts and relevant regulations such as the Center for Internet Security (CIS), California Privacy Rights Act (CPRA), and GDPR. These templates streamline the creation of custom access policies that satisfy individual business needs. Additionally, when risks are detected, the platform should have the capability of sending automated security alerts to relevant security personnel.
In today’s fast-paced, data-rich environment, new data sources and cloud accounts are created frequently. A good data security platform provides round-the-clock monitoring, scanning for both new and recently modified data, automatically assessing the data security posture of these assets, and generating alerts if needed. Another key component of continuous monitoring is the detection of those who have more permissions than they’re routinely using. When over-privileged users are detected, some platforms will automatically suggest role definitions that right-size a user’s permissions that align the level of access they have with those they require to complete their work.
While many legacy solutions have been updated to accommodate modern cloud infrastructure, cloud-native solutions were specifically built for the cloud. They’re easier to get up and running quickly and are typically simpler to use. Additionally, cloud-native platforms are managed by the provider, requiring no in-house maintenance resources.
Future-Proof Data Protection with Data Security Posture Management
Data security posture management is an essential part of a modern data security strategy. As the value of data grows and the consequences of mishandling data become more severe, forward-thinking organizations are prioritizing data security posture management. By investing in DSPM, companies can protect their critical data, safeguard their reputations, and maintain the trust of their customers. A strong data security platform aids in this initiative, automating data discovery, prioritization, risk remediation, and monitoring activities across the business’s entire operation.
Read Best Practices for Securing Sensitive Data to pinpoint whether your organization’s data security and access control practices are sufficient and learn best practices to take data security to the next level.