Description

What's DSPM?

 Data Security Posture Management identifies sensitive data concerning multiple clouds and services. This then judges how threatened the data is by security violations and the possibility of receiving a violation-based regulatory non-compliance. Thus, DSPM brought forth dynamic insights and automation, which assist security teams in resolving risks around data safety and compliance measures to stop them from peeking over those same holes again. Gartner, an industry analyst, first coined the term DSPM in its 2022 Hype Cycle for Data Security. The term is popularly referred to as " data-first " security, as it turns the protection model of other cyber technologies and practices inside out . We protect the data directly rather than protecting the devices, systems, and applications that house, move, and process data. However, it must be noted that DSPM complements many other solutions within an organization’s security technology stack.

 

Importance of DSPM 

The majority of security technologies work on the principle of protecting sensitive data from unauthorized access to the network, and on behavioral monitoring for detection and repudiation actions by unauthorized or authorized users, application programming interfaces (APIs), Internet of Things (IoT) devices, or any other entities.

 

1- These have advanced data security and threat detection, and response-far better. However, fast-tracking cloud computing, agile cloud-native development, and working with artificial intelligence (AI) and machine learning (ML) have introduced data security risks and vulnerabilities not always addressed by otherwise-arguably helpful technologies; such gaps can expose organizations to data breaches and regulatory compliance violations. 

 

2- Among these data risks, the foremost one is shadow data backup, copy, or replication into a data store that is not monitored, managed, or governed by the same security teams, policies, or controls as the original data. For instance, DevOps teams create and destroy dozens of new stores daily for iterative development and testing, and with these, copy sensitive data. One wrong configuration could mean that data in any or all of these stores is now vulnerable to unauthorized access. 

 

3- AI- or ML-modeling data requirements following the same trend contribute badly to this situation by almost encouraging organizations to open data access to many more users with comparatively little knowledge of proper data security and governance. Also, the risks spread with increased adoption of multicloud (use of cloud services and applications from a multitude of providers) and hybrid cloud (infrastructure that combines and orchestrates public cloud and private cloud environments) environments. 

 

According to the Netsquad Cost of a Data Breach Report 2023, 82% of data breaches involved data stored in cloud environments, while 39% of the breached data was stored across various computing environments: private cloud, public cloud, hybrid cloud, and on-premises.

 

This is what defines DSPM:

 To find sensitive data in the organization, assess the security posture, remediate the vulnerabilities according to the security objectives or compliance needs, and put in place safeguards and monitoring to prevent the repetition of such vulnerabilities.

 

Most DSPM products do not require an agent; they would not need to install a separate application software program into every asset or resource during monitoring and protection but also for higher degrees of automation.

 

Whether security specialists agree on the minor details, DSPM contains at least four features:

 

• Data finding

 

• Data classification

 

• Risk assessment and prioritization

 

• Remediation and prevention.

 

Data discovery

The data discovery function of the DSPM solutions constantly scans everywhere sensitive data assets. 

Every type of data, every kind of data, store-structured and unstructured data; Cloud storage-file, block storage, and object storage-for example, storage services associated with specific cloud services, cloud apps, or cloud service providers.

 

Data classification

Generally speaking, data classification separates data by predetermined conditions. It means classifying data according to sensitivity for every asset in the framework of DSPM.

 

1) Level of sensitivity of data-whether it is PII, confidential, trade secret, or other.

 

2) Who, under which terms, conditions, and criteria, should be authorized to access data?

 

3) How, and through which methods, the data would get stored, accessed, handled, and consumed.

 

4) If that data has some regulations under which it might fall- e.g. Health Insurance Portability and Accountability Act (HIPAA), the Payment Card Industry Data Security Standard (PCI DSS), the EU's General Data Privacy Regulation (GDPR), California Consumer Privacy Act (CCPA), and many more such data protection/data privacy regulations.

 

Risk assessment and prioritization

From the DSPM viewpoint, each data asset can be expected to possess several vulnerabilities and thus be capable of offering priority in its exposure to them. Therefore, this is how the most common DSPM vulnerabilities seem:

 

Misconfiguration

Misconfiguration refers to any missing or incomplete security control settings for an application or system that put an organization’s data at risk for unauthorized access. Most prominently cited misconfiguration-related issues have been unsecured storage of data in the cloud; however, others include failures to apply security patches and missed data encryption. Misconfiguration is widely accepted as the most pernicious risk to cloud data security, and at the heart of the majority of data loss or leakage incidents.

 

Overentitlements (or overpermissioning)

Overentitlements connote giving users an excessive level of data access privileges or permissions beyond what is required for their work. Overentitlements may arise due to misconfigurations, but these may also be introduced through an improper or careless elevation of entitlements by administrators (or maliciously by a potential insider), or the failure to revoke temporary permissions upon the expiration of their justification.

 

Data flow and data lineage issues

Data flow analysis is the constant tracking of data across all environments, locations, and each person having access to those settings. This would then act as a complementary source of information about other infrastructure vulnerabilities, thus marking down prospective attack paths toward sensitive data.

 

Security policies and regulatory violations

Data Security Posture Management (DSPM) solutions match the existing security settings of the data with their organization’s data security policies and the data security requirements that could have been established by any regulations that the organization would have been subject to. This requires an evaluation of where data is inadequately protected and where the organization becomes an actionable risk of non-compliance.

 

Remediation and Prevention

The offering under DSPM contains reports and dashboards that present vulnerabilities in real-time with severity ratings, thereby allowing security and risk management teams to prioritize their effort into remediating the most critical issues. Many DSPM solutions also provide remediation guides with step-by-step lab instructions or incident-response playbooks for handling current risks or data-security threats.

Some DSPM solutions will automatically modify the application or system configurations, access-control settings, and security-software settings to provide additional protection against impending data exposure. The others can be integrated into DevOps workflows to remedy potential security risks early on in the application lifecycle.