Securing Your Crown Jewels With Data Security Posture Management (DSPM)

Welcome to the Big Bang Era of Data. New, data-obsessed companies are emerging while incumbents are battling to thwart business disruption. Companies are accumulating massive volumes of data in the cloud. This data powers hyper-scale, cloud-native applications fueled by powerful technologies such as Generative AI. The winners in this era will be the companies that harness the power of data while effectively managing data risk.

The Need for Data Security Posture Management

Unfortunately, cloud security solutions, from Cloud Security Posture Management (CSPM) to Cloud Workload Protection Platforms (CWPP), lack an understanding of your organization’s data. As a data security owner, your team is grappling for answers to several pressing questions:

• What data assets do you possess?
• What sensitive data must you protect?
• How do you prevent unauthorized data access?
• How do you prioritize the remediation of misconfigured data systems?
• How do you ensure consistent security & privacy controls across the data flows?

Data Security Posture Management (DSPM) is an emerging technology that helps organizations address these issues using a data-centric cloud security and privacy approach.

Technical Elements of a DSPM Program

To learn a practical data security posture management approach for your organization, consider a framework that includes five critical elements: data, identity, config, people, and flow.

Let’s explore the insights you need about these elements to implement a robust data security posture management program.

Discover & Catalog All Data Assets

The first step in managing data security posture is discovering all the data assets across environments. While your cloud provider may provide basic visibility into managed cloud data assets, it is challenging to identify all assets, especially various shadow and dark data assets that may be unknown to your IT teams. These assets are usually created over time during cloud migration projects with organizations running open-source or enterprise databases on cloud-hosted compute or when teams create copies of data assets for backup and experimental projects.

While DSPM solutions enable asset discovery in the public cloud, an even more comprehensive approach is essential to build a comprehensive inventory of data assets across all public and private clouds and SaaS applications. With developers adopting a DevOps mindset and using automation to rapidly deploy app infrastructure, keeping up with data asset growth becomes essential.

Gain Sensitive Data Intelligence

Much like how an insurer values the goods they cover, companies must also understand the sensitivity of the data they hold to plan their data security, privacy, and governance program. However, with organizations holding petabytes of data in structured, semi-structured, and unstructured formats across various clouds and data technologies, classifying data has traditionally been challenging due to scalability, consistency, and false positive issues. However, with advancements in AI techniques that leverage Natural Language Processing algorithms, organizations can now accurately classify sensitive data at scale with high accuracy. 

Besides classifying data in line with business policy and industry-specific regulations, organizations also need data about their data, i.e., metadata, to drive various programs. This includes insight into business, technical, security, and privacy metadata to guide internal teams around data management and enable users to find appropriate data for business use.

Prioritize Data Misconfigurations

While CSPM solutions are essential for an organization to understand its overall cloud posture risk, they are difficult to operationalize as they create alert fatigue and generate false positives. What differentiates a DSPM from CSPM is the former’s ability to correlate system misconfiguration insights with data classification tags and labels. By eliminating false positives and reducing the number of alerts security teams need to review, organizations can better scale their security efforts to improve the configuration posture of data assets containing sensitive information.

Govern Data Access by Identities

The Zero Trust architecture operates on the principle of least privilege. It requires that an identity only be granted access to the data it needs. However, orchestrating such precise control is nearly impossible without automation and data classification insights. For proper Data Access Governance (DAG), organizations need to identify which identities have what level of access to data and whether those access permissions align with actual data usage. By evaluating the difference between permitted access and actual usage, organizations can fine-tune data access permissions to improve access controls. But enforcing data controls is more than just granting or denying data access.

A more effective approach should enable organizations to mask sensitive data elements while enabling user access to remaining datasets. This enables organizations to implement secure data sharing and drive value-creation projects. An ideal DSPM helps accomplish this goal by orchestrating very targeted and granular data access controls based on attributes such as identity’s role, location, data sensitivity, regulation, residency, and more.

Honor People’s Data Privacy

Key to operationalizing people’s data privacy requires organizations to identify an individual’s sensitive and personal data stored by a company. However, most organizations realize that data is often scattered across multiple clouds and data systems, making mapping data to an individual’s identity very difficult.

A core component of a data security posture management solution is instantly discovering an individual’s data using a People Data Graph^TM. This automation enables organizations to operationalize various privacy operations, from monitoring data consent and cross-border transfers to automating data subject requests, breach impact analysis, and people notifications.

Ensure Consistent Data Controls Across Flows

Data flows dynamically across your company, making data security posture management challenging for an organization. To tackle this, organizations must map data processing activities and understand data lineage. This helps visualize how data moves between systems, track duplicates, and trace data transformations across files and tables. By mastering this process, organizations can assess whether security and privacy controls apply consistently across data flows.

Modern applications also leverage data streaming technologies such as Kafka and Amazon Kinesis to listen to real-time data and make informed decisions. Securing such data flows is critical to prevent sensitive data sprawl downstream to consumers of data. Enforcing streaming topic-level access controls and masking sensitive data is essential for meeting data privacy and security obligations.

Securiti: The Leader in Data Security Posture Management

Securiti's DataControls Cloud empowers Fortune 1000 companies to protect data everywhere, including multiple public clouds, data clouds, on-premises, SaaS applications, and data flows. Securiti has been recognized by analysts such as Gartner and Forrester for its leadership in Data Security and Privacy Management.