Data Security Posture Management: A Comprehensive Analysis

Abstract

Data Security Posture Management (DSPM) has rapidly ascended as an indispensable discipline within the contemporary cybersecurity landscape, specifically tailored to confront the intricate challenges inherent in safeguarding sensitive data across increasingly complex, distributed, and dynamic environments. This comprehensive report embarks on an in-depth analytical journey into DSPM, meticulously exploring its foundational definition, its evolutionary trajectory from conventional security paradigms, and its multifaceted core functionalities. Furthermore, the report delves into the profound strategic significance of DSPM in the context of modern data governance, regulatory compliance, and proactive threat mitigation. A critical element of this exposition involves a granular delineation of DSPM’s distinct characteristics and its synergistic yet differentiated relationships with adjacent cybersecurity concepts, such as Data Loss Prevention (DLP), Cloud Security Posture Management (CSPM), and Cloud Native Application Protection Platforms (CNAPP), thereby furnishing a nuanced and holistic understanding of its pivotal role within the broader cybersecurity ecosystem.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

1. Introduction

In an epoch defined by unrelenting digital transformation, the exponential proliferation of data, and the pervasive adoption of cloud-native architectures, organizations globally confront an unprecedented magnitude of challenges in securing their most vital asset: sensitive information. The very fabric of modern enterprise operations — spanning from customer relationship management and intricate supply chains to proprietary intellectual property and financial transactions — is underpinned by data. Consequently, the protection of this data is not merely an IT imperative but a fundamental business continuity and trust prerequisite. Traditional, perimeter-centric security measures, once the bedrock of enterprise defense, are proving increasingly inadequate and porous when confronted with the realities of hybrid infrastructures, multi-cloud deployments, ubiquitous Software-as-a-Service (SaaS) applications, and the escalating sophistication of cyber threats. These conventional approaches, often focused on securing network boundaries, endpoints, and server infrastructure, frequently overlook the intrinsic security posture of the data itself, irrespective of its location or the specific infrastructure upon which it resides. This critical gap has led to a paradigm shift in cybersecurity philosophy, giving rise to Data Security Posture Management (DSPM) as a pivotal, data-centric strategy. DSPM offers a comprehensive, continuous, and proactive methodology to mitigate these evolving challenges, transcending conventional security models by placing the data at the absolute epicenter of its protection framework.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

2. Defining Data Security Posture Management (DSPM)

DSPM is a specialized and advanced cybersecurity discipline fundamentally oriented towards the continuous monitoring, rigorous assessment, and proactive enhancement of an organization’s overall data security posture. At its core, DSPM represents a data-centric approach to security, moving beyond infrastructure or network boundaries to focus directly on the identification, precise classification, meticulous protection, and ongoing governance of sensitive data across an organization’s entire digital footprint. This encompasses data residing in diverse environments, including on-premises data centers, private clouds, public cloud platforms (Infrastructure-as-a-Service, Platform-as-a-Service, Software-as-a-Service), hybrid architectures, and even less structured data repositories like file shares and collaboration tools. The primary objective is to ensure unwavering compliance with an ever-expanding array of regulatory standards and to fortify defenses against unauthorized access, data exfiltration, tampering, and catastrophic data breaches.

Unlike predecessor security paradigms that primarily concentrated on securing endpoints, network segments, or application layers, DSPM adopts a holistic and pervasive data-centric methodology. This means that the security controls and visibility mechanisms are applied directly to the data itself, rather than solely to the containers or conduits through which it travels or is stored. This shift is crucial because, in modern distributed environments, data can move freely, be replicated, or be accessed from myriad locations by various users and applications. DSPM’s philosophy acknowledges that the inherent sensitivity and regulatory obligations of data remain constant, regardless of its transient or static location. It seeks to answer critical questions such as: ‘Where is our sensitive data located?’, ‘Who has access to it, and what is their effective access?’, ‘Is it exposed to unnecessary risk?’, ‘Is it compliant with relevant regulations?’, and ‘How can we continuously monitor and improve its security state?’. By providing answers to these questions in an automated and continuous fashion, DSPM enables organizations to proactively identify vulnerabilities, remediate misconfigurations, and enforce robust data protection policies throughout the entire data lifecycle – from creation and storage to processing, sharing, archival, and eventual deletion.

The ‘posture’ aspect of DSPM is particularly significant. It implies not a static, point-in-time snapshot, but a dynamic, continuously evaluated state of security. Just as a physical posture can be adjusted and improved, an organization’s data security posture is subject to constant vigilance and refinement. This continuous assessment accounts for changes in data location, access patterns, user permissions, threat intelligence, and evolving regulatory landscapes, ensuring that defenses remain resilient and relevant against a perpetually shifting adversarial environment.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

3. Emergence and Evolution of DSPM

The genesis of Data Security Posture Management can be directly attributed to the confluence of several transformative shifts in enterprise IT and the cybersecurity landscape over the past decade. The concept itself was formally introduced and gained significant industry recognition when Gartner featured it prominently in its 2022 Hype Cycle for Data Security. This inclusion underscored a growing industry consensus regarding the pressing need for a unified, data-centric strategy to manage data security effectively across increasingly complex, distributed, and highly dynamic environments.

Historically, cybersecurity efforts were largely defined by a ‘moat-and-castle’ analogy, where the focus was on building strong perimeters around an organization’s on-premises network. Firewalls, intrusion detection systems, and secure gateways were the primary defenses, designed to keep threats out and sensitive data in. However, the rapid and widespread adoption of cloud computing services – encompassing Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS) – fundamentally shattered this traditional perimeter. Data began to reside outside the corporate network in third-party cloud provider infrastructure, often managed by vendors, making it difficult for organizations to maintain direct control and visibility over their sensitive information.

Coupled with cloud adoption, the sheer volume, velocity, and variety of data exploded. Big data analytics, IoT devices, mobile computing, and collaboration platforms led to an unprecedented data sprawl. Sensitive data, such as Personally Identifiable Information (PII), Protected Health Information (PHI), financial records, and intellectual property, became distributed across numerous databases, object storage buckets, file shares, and SaaS applications, often without consistent security controls or adequate visibility. This phenomenon, frequently referred to as ‘shadow IT’ or ‘dark data,’ further exacerbated the problem, as sensitive information could exist in places unknown to IT and security teams.

Furthermore, the regulatory landscape became significantly more stringent and globalized. Major data protection regulations like the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and the Health Insurance Portability and Accountability Act (HIPAA) imposed severe penalties for data breaches and non-compliance, pushing organizations to adopt more robust and auditable data security practices. These regulations often mandate specific requirements for data classification, access control, encryption, data residency, and the rights of data subjects.

Traditional security models, struggling under the weight of these changes, proved inadequate for several reasons:

• Lack of Data Visibility: They couldn’t provide a unified, real-time view of where sensitive data resided across hybrid and multi-cloud environments.
• Ineffective Access Controls: While they controlled network access, they often failed to provide granular control or continuous monitoring of who had effective access to what specific data at the data layer itself, leading to over-privileged accounts.
• Reactive Posture: Many traditional tools were reactive, focusing on threat detection after an incident rather than proactive risk assessment and prevention at the data layer.
• Configuration Drift: Cloud environments are highly dynamic, leading to frequent misconfigurations that expose data, which traditional tools were not designed to detect effectively.

DSPM emerged as a direct response to these shortcomings. By providing a holistic, data-centric view of an organization’s data security posture, DSPM empowers organizations to proactively identify, assess, and mitigate risks associated with data exposure, misconfigurations, and non-compliance. It aligns seamlessly with the principles of Zero Trust, which dictates that no user, device, or application should be inherently trusted, and access should be granted on a ‘least privilege’ and ‘verify continuously’ basis, especially concerning sensitive data.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

4. Core Functionalities of DSPM

Data Security Posture Management solutions are engineered to deliver a suite of interconnected capabilities that collectively enable a comprehensive and proactive approach to data security. These core functionalities address the entire lifecycle of sensitive data, from its discovery to its continuous protection and governance.

4.1 Automated Sensitive Data Discovery and Classification

At the very foundation of any effective DSPM strategy lies the ability to accurately and comprehensively discover and classify sensitive data. This is not a trivial task given the vast quantities of data generated, stored, and processed by modern organizations, often spanning hundreds or thousands of data repositories across diverse environments (on-premises databases, data warehouses, object storage buckets, file shares, SaaS applications, developer environments, collaboration platforms, etc.).

The process of automated sensitive data discovery involves sophisticated scanning and analysis techniques to identify data that falls under specific categories of sensitivity or regulatory concern. Modern DSPM solutions leverage a combination of advanced methods:

• Pattern Matching and Regular Expressions (Regex): Identifying common patterns associated with sensitive data, such as Social Security Numbers (SSNs), credit card numbers (PCI DSS), email addresses, or specific document IDs.
• Keyword Analysis: Detecting specific terms or phrases that indicate sensitive content, such as ‘confidential,’ ‘proprietary,’ ‘patient record,’ or ‘financial statement.’
• Machine Learning (ML) and Natural Language Processing (NLP): These advanced techniques enable DSPM solutions to understand the context of data, rather than just matching patterns. ML models can be trained to recognize unstructured sensitive data within documents, emails, chat logs, or source code, even if it doesn’t conform to a strict pattern. NLP helps in interpreting the meaning and intent of text to classify data more accurately.
• Fingerprinting and Exact Data Matching (EDM): Creating digital ‘fingerprints’ of known sensitive documents or datasets. This is particularly useful for identifying copies of highly sensitive internal documents, intellectual property, or specific customer lists.
• Metadata Analysis: Inspecting file attributes, tags, schema names, and database column names for indicators of sensitivity.
• Data Structure Analysis: Understanding the structure of databases, tables, and fields to infer the type of data they contain (e.g., a column named ‘customer_SSN’ is a strong indicator).

Once discovered, data is meticulously classified based on its sensitivity level (e.g., public, internal, confidential, highly restricted) and its regulatory requirements (e.g., PII subject to GDPR, PHI subject to HIPAA, financial data subject to PCI DSS). Automated classification is paramount because it ensures consistency, scalability, and reduces the significant risk of human error inherent in manual classification. This precise classification serves as the cornerstone for applying appropriate security controls, access policies, and compliance measures. Without accurate discovery and classification, an organization operates in the dark, unable to effectively protect what it doesn’t know it has, or where it resides.

4.2 Access Governance

Effective access governance within DSPM is the critical mechanism by which an organization dictates and controls who (users, applications, services) has access to sensitive data, under precisely what conditions, and for what purpose. This functionality moves beyond mere network authentication to focus on the granular entitlements at the data layer itself, ensuring that access privileges are aligned with organizational policies, regulatory mandates, and the principle of least privilege (PoLP).

Key aspects of DSPM-driven access governance include:

• Continuous Entitlement Discovery and Mapping: DSPM solutions continuously scan and map all identities (human and machine), roles, and permissions associated with data assets. This includes permissions granted directly to files, folders, database tables, object storage buckets, and also permissions inherited through groups, roles (e.g., AWS IAM roles, Azure RBAC roles), and policies.
• Effective Access Analysis: It’s not enough to know what permissions are assigned; DSPM determines effective access – what a user or service can actually do with the data, considering all inherited permissions, nested groups, and conflicting policies. This often reveals ‘ghost users’ or over-privileged accounts that pose significant risks.
• Principle of Least Privilege (PoLP) Enforcement: DSPM helps organizations identify and remediate instances where users or applications have excessive permissions beyond what is required for their legitimate job functions. For instance, a developer might have read-write access to a production database containing PII when only read access to anonymized data is necessary.
• Just-in-Time (JIT) and Just-Enough-Access (JEA): Advanced DSPM implementations support the enforcement of temporary, time-bound access for sensitive operations, automatically revoking permissions once the task is complete, thereby significantly reducing the attack surface.
• Anomaly Detection in Access Patterns: By baselining typical access behaviors, DSPM can detect unusual or suspicious access attempts, such as a user accessing data outside their normal working hours, from an unusual location, or attempting to access highly sensitive data they’ve never touched before. This often integrates with User and Entity Behavior Analytics (UEBA).
• Remediation Workflows for Access Misconfigurations: When over-privileged access or misconfigurations are detected, DSPM can initiate automated or semi-automated remediation actions, such as revoking specific permissions, adjusting role assignments, or triggering alerts for manual review and approval. This integrates with Identity and Access Management (IAM) systems and Privileged Access Management (PAM) solutions.

Effective access governance minimizes the risk of insider threats (malicious or accidental), reduces the impact of compromised credentials, and ensures adherence to compliance requirements that mandate strict access controls, such as GDPR Article 32 (Security of processing) and HIPAA’s administrative safeguards.

4.3 Risk Assessment at the Data Layer

Risk assessment at the data layer is a core tenet of DSPM, moving beyond generic infrastructure risks to focus specifically on the vulnerabilities and threats directly impacting sensitive data. This function involves continuously evaluating the security posture of discovered and classified data by analyzing a multitude of factors, ultimately quantifying and prioritizing risks to enable targeted mitigation strategies.

DSPM performs detailed risk assessments by considering:

• Data Sensitivity: The classification level of the data (e.g., highly confidential PII) directly impacts its risk score. More sensitive data exposed to risk warrants higher priority remediation.
• Exposure Status: Is the data publicly accessible? Is it exposed to the internet? Is it accessible from external networks? Examples include publicly exposed S3 buckets, misconfigured network access to databases, or sensitive data in unauthenticated web applications.
• Access Controls and Permissions: As identified by access governance, excessive permissions, dormant accounts with access, or misconfigured authentication mechanisms significantly elevate risk.
• Encryption Status: Is sensitive data encrypted at rest and in transit? Unencrypted sensitive data, especially in cloud storage or backups, is a major vulnerability.
• Data Residency and Sovereignty: Is data stored in a geographical region compliant with regulatory mandates? Violations of data residency rules pose compliance risks.
• Configuration Drift and Misconfigurations: DSPM detects security misconfigurations in data stores and their surrounding infrastructure that could lead to data exposure. This includes insecure default settings, lack of versioning, improper logging, or weak security policies associated with storage services.
• Compliance Gaps: The system automatically maps identified data risks to specific controls and requirements of relevant regulatory frameworks (e.g., GDPR, HIPAA, PCI DSS, ISO 27001). This allows organizations to identify and prioritize risks that directly impact their compliance posture.
• Threat Intelligence Integration: By correlating internal data posture risks with external threat intelligence, DSPM can provide a more accurate and contextualized risk assessment, highlighting which exposed data assets are most likely to be targeted by current attack vectors.
• Vulnerability Context: Understanding if the data is stored on systems with known unpatched vulnerabilities can further refine the risk score.

The output of this continuous assessment is typically a prioritized list of data-centric risks, often visualized through a dashboard. This prioritization considers not only the likelihood of an event but also the potential impact of a breach or exposure of specific sensitive data. By focusing on risks directly at the data layer, DSPM enables organizations to implement precise, effective, and targeted security measures. For instance, rather than simply identifying an open port (an infrastructure risk), DSPM identifies that the database behind that open port contains unencrypted PII with excessive user access, thus escalating its criticality and guiding remediation efforts more effectively.

4.4 Continuous Monitoring and Remediation

Continuous monitoring is the perpetual vigilance mechanism within DSPM, essential for maintaining a robust data security posture in dynamic environments. It involves the real-time or near real-time observation of data, access patterns, configurations, and overall security events to detect anomalies, policy violations, and potential security incidents as they unfold. Remediation, then, is the subsequent, critical phase of taking prompt corrective action to address identified vulnerabilities and mitigate active threats.

Key aspects of continuous monitoring within DSPM include:

• Data Activity Monitoring: Tracking all interactions with sensitive data, including reads, writes, modifications, deletions, and data transfers. This establishes a baseline of normal data usage.
• Access Pattern Monitoring: Continuously observing who accesses data, when, from where, and how. Deviations from established baselines or policy are flagged as suspicious.
• Configuration Change Monitoring: Alerting on any changes to data store configurations, security policies, encryption settings, or access controls that could introduce vulnerabilities or violate compliance rules.
• Policy Violation Detection: Automatically identifying instances where data or its access violates predefined security policies or regulatory mandates (e.g., sensitive data being moved to an unapproved region, or an unencrypted copy being created).
• Anomaly and Threat Detection: Leveraging behavioral analytics (UEBA) and threat intelligence feeds to identify unusual data access, suspicious user behavior (e.g., large data downloads by a non-privileged user), or indicators of compromise (IoCs) related to data assets. This can include detecting ransomware activity or data exfiltration attempts.
• Security Logging and Auditing: Generating comprehensive audit trails of all data-related security events, providing crucial evidence for compliance reporting, forensic investigations, and post-incident analysis.

Upon detection of a risk, anomaly, or policy violation, DSPM initiates remediation processes. These actions can vary in their degree of automation and can include:

• Automated Policy Enforcement: Instantly applying security policies, such as automatically encrypting newly discovered unencrypted sensitive data, or quarantining data that violates residency rules.
• Access Revocation/Modification: Automatically revoking over-privileged access, suspending suspicious accounts, or adjusting permissions to enforce the principle of least privilege.
• Configuration Correction: Automatically reverting misconfigured settings in cloud storage buckets or databases to a secure baseline, or flagging them for immediate manual correction.
• Alerting and Notification: Generating immediate alerts to security teams, incident response platforms (SIEM, SOAR), and relevant stakeholders, providing detailed context about the incident, the affected data, and the recommended remediation steps.
• Quarantining or Isolating Data: In severe cases, DSPM might trigger actions to isolate compromised data, prevent further access, or initiate deletion workflows for non-compliant data.
• Integration with Incident Response: Seamlessly integrating with Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) platforms to streamline incident investigation and response workflows.

This continuous loop of monitoring, detection, and remediation is what defines the dynamic nature of DSPM, enabling organizations to maintain a consistently strong data security posture and respond rapidly to emerging threats and vulnerabilities.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

5. Strategic Importance of DSPM

In the contemporary data-driven and compliance-intensive business landscape, Data Security Posture Management has transcended from a desirable security feature to an indispensable strategic imperative. Its comprehensive capabilities address critical organizational needs, directly contributing to resilience, trustworthiness, and sustained operational integrity.

5.1 Ensuring Regulatory Compliance

The global regulatory environment surrounding data privacy and security has become extraordinarily complex and punitive. Regulations such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), the California Consumer Privacy Act (CCPA), the Payment Card Industry Data Security Standard (PCI DSS), and numerous industry-specific frameworks impose stringent requirements on how organizations collect, process, store, and protect sensitive data. Non-compliance with these regulations can lead to staggering financial penalties, which can run into millions of dollars or a significant percentage of annual global revenue (e.g., up to 4% for GDPR), as well as severe reputational damage and legal liabilities.

DSPM plays a crucial role in navigating this regulatory labyrinth by providing the necessary visibility and control. By continuously monitoring data location, classification, access controls, and encryption status, DSPM helps organizations demonstrate adherence to specific regulatory articles and controls. For instance, DSPM directly supports GDPR Article 32 on the ‘Security of processing’ by ensuring appropriate technical and organizational measures, including the ability to ensure the ongoing confidentiality, integrity, availability, and resilience of processing systems and services. It helps identify data residency violations, where sensitive data might be stored in a jurisdiction that violates specific regulatory requirements. Through automated auditing and reporting capabilities, DSPM significantly streamlines the process of demonstrating compliance during audits, minimizing the effort and risk associated with regulatory scrutiny.

5.2 Protecting Sensitive Information

Beyond regulatory mandates, the primary strategic importance of DSPM lies in its fundamental ability to safeguard an organization’s most critical assets: its sensitive information. This encompasses a broad spectrum of data types, including customer PII, employee records, financial transactions, proprietary intellectual property (trade secrets, patents, source code, research data), strategic business plans, merger and acquisition details, and competitive intelligence. A data breach involving any of these categories can have devastating consequences, extending far beyond immediate financial losses.

DSPM proactively protects this information by identifying and remediating vectors of attack that could lead to unauthorized access or exfiltration. By pinpointing exposed sensitive data, correcting over-privileged access, enforcing encryption, and continuously monitoring for anomalous activities, DSPM drastically reduces the attack surface. In the event of an attempted breach, DSPM’s real-time monitoring and alerting capabilities enable rapid detection and response, minimizing the dwell time of attackers and the potential scope of data compromise. The consequences of failing to protect sensitive data can include significant financial costs for remediation, legal fees, customer notification, and credit monitoring, alongside a profound loss of customer trust, brand erosion, and a competitive disadvantage. DSPM acts as a proactive shield against these existential threats.

5.3 Enhancing Data Visibility

One of the most pervasive challenges in modern data security is the lack of comprehensive data visibility. Organizations often struggle to answer fundamental questions like ‘Where is all our sensitive data?’, ‘How much of it do we have?’, ‘Who owns it?’, and ‘How is it flowing across our environment?’. This ‘dark data’ problem is exacerbated by the rapid proliferation of data across hybrid cloud, multi-cloud, SaaS applications, and shadow IT instances.

DSPM addresses this challenge head-on by providing a unified, real-time, and continuously updated inventory of all data assets. Its automated discovery and classification capabilities illuminate previously unknown or unmanaged repositories of sensitive information. This enhanced visibility extends to understanding data residency, data lineage (how data moves and transforms), and effective access paths. With a clear understanding of where sensitive data resides, how it is categorized, and how it is being accessed and used, organizations gain unparalleled situational awareness. This visibility is not only crucial for security teams but also empowers data governance initiatives, data privacy officers, and business leaders to make informed decisions about data lifecycle management, risk mitigation, and strategic resource allocation. It transforms opaque data environments into transparent, manageable assets.

5.4 Strengthening Zero Trust Architectures

DSPM is a natural and essential complement to Zero Trust security frameworks. The Zero Trust model, founded on the principle ‘never trust, always verify,’ requires rigorous authentication and authorization for every access request, irrespective of whether the request originates inside or outside the traditional network perimeter. While Zero Trust mandates verification for who is accessing what resource, DSPM adds the critical layer of understanding what data is contained within those resources, its sensitivity, and its risk profile.

By providing continuous visibility into sensitive data locations, effective access paths, and data-centric risks, DSPM enables a more granular and intelligent enforcement of Zero Trust principles. It ensures that even authenticated users are granted the absolute minimum necessary access (least privilege) to specific sensitive datasets, and that this access is continuously re-evaluated. If an authenticated user attempts to access sensitive data that violates policy or exhibits anomalous behavior, DSPM can detect and respond, acting as a crucial enforcement point within a Zero Trust architecture. DSPM fundamentally shifts the security focus from securing networks to securing the data itself, which is the ultimate goal of Zero Trust.

5.5 Driving Operational Efficiency and Reducing Costs

Beyond direct security benefits, DSPM contributes significantly to operational efficiency and cost reduction. Manual processes for data discovery, classification, access review, and compliance auditing are notoriously time-consuming, prone to human error, and expensive. DSPM automates many of these tasks, freeing up valuable security and IT personnel to focus on more strategic initiatives.

By providing a centralized view of data risks and a streamlined remediation workflow, DSPM reduces the complexity and duration of incident response. It minimizes the ‘swivel chair’ effect, where security teams must manually correlate information from disparate tools. Furthermore, by proactively identifying and mitigating risks, DSPM helps prevent costly data breaches, avoiding associated fines, legal fees, and reputational damage that can run into millions of dollars. The ability to demonstrate continuous compliance through automated reporting also streamlines audit processes, reducing the time and resources expended on preparing for and undergoing regulatory assessments.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

6. Differentiating DSPM from Related Concepts

The cybersecurity landscape is replete with acronyms and overlapping functionalities, leading to potential confusion regarding the distinct role of DSPM. While DSPM shares common goals with other security solutions, it possesses unique characteristics and a specific scope. Understanding these distinctions and interdependencies is crucial for architects and practitioners to build cohesive and effective security programs.

6.1 Data Loss Prevention (DLP)

Data Loss Prevention (DLP) is a set of tools and processes designed to ensure that sensitive data is not accessed, used, shared, or exfiltrated by unauthorized individuals or entities. DLP primarily focuses on monitoring and controlling data in motion (network traffic), data in use (on endpoints), and sometimes data at rest (storage). Its core strength lies in detecting and preventing unauthorized data transfers, often by inspecting content for sensitive information (e.g., credit card numbers, PII) and enforcing policies that block, encrypt, or alert on suspicious activities.

Key Distinctions from DSPM:

• Primary Focus: DLP’s primary focus is egress control – preventing sensitive data from leaving the organization’s defined boundaries or being used inappropriately by insiders. It acts as a gatekeeper for data movement.
• Scope: DLP is typically enforced at network egress points, email gateways, endpoints, and sometimes specific cloud services. It’s often concerned with what data is going where.
• Mechanism: DLP solutions often rely on content inspection, context analysis, and predefined policies to detect and block data transfers that violate security rules.

DSPM vs. DLP:

While both DSPM and DLP aim to protect sensitive data, their scope and approach differ significantly. DSPM provides a more foundational and proactive approach, addressing the entire security posture of data, regardless of its movement status. It identifies where sensitive data resides, who has access to it (even if it’s not currently moving), and what risks it is exposed to due to misconfigurations or excessive permissions. DLP, on the other hand, is a control mechanism that consumes the intelligence provided by DSPM. DSPM can inform DLP policies by accurately classifying data and identifying its locations, allowing DLP to be more targeted and effective in preventing exfiltration. DSPM answers ‘Where is my sensitive data and what state is it in?’, while DLP answers ‘How do I stop this specific sensitive data from leaving or being misused?’ They are complementary; DSPM establishes the secure context, and DLP enforces protections on data in transit or use within that context.

6.2 Cloud Security Posture Management (CSPM)

Cloud Security Posture Management (CSPM) solutions are designed to identify and remediate security risks and compliance violations within an organization’s cloud infrastructure. CSPM primarily focuses on the configuration of cloud resources (e.g., AWS S3 buckets, Azure Virtual Machines, Google Cloud IAM roles, network security groups, serverless functions) to ensure they adhere to security best practices, organizational policies, and regulatory requirements. It continuously scans cloud environments for misconfigurations, such as publicly exposed storage, overly permissive IAM policies, unencrypted resources, or weak network settings.

Key Distinctions from DSPM:

• Primary Focus: CSPM’s primary focus is on the security of the cloud infrastructure and its configurations. It ensures that the underlying cloud environment itself is securely configured.
• Scope: CSPM operates at the infrastructure layer, inspecting IaaS and PaaS settings, network configurations, and identity and access management settings within cloud provider environments.
• Mechanism: CSPM solutions use APIs to inspect cloud provider configurations, compare them against predefined security benchmarks (e.g., CIS Benchmarks), and identify deviations.

DSPM vs. CSPM:

CSPM and DSPM are distinct but highly synergistic. CSPM addresses the security of the container or environment where data resides, while DSPM focuses specifically on the data itself within those containers. A classic example illustrating their relationship is an AWS S3 bucket: a CSPM tool might identify that an S3 bucket is publicly accessible (a configuration risk). A DSPM tool would then examine the contents of that publicly accessible S3 bucket. If the DSPM discovers that the bucket contains unencrypted PII, then the CSPM-identified configuration risk becomes a critical data security risk identified by DSPM. Without DSPM, an organization might fix an ‘open S3 bucket’ but might not know what sensitive data was exposed or its actual business impact. DSPM provides the crucial data context to the infrastructure risks identified by CSPM. CSPM is infrastructure-centric; DSPM is data-centric. Together, they offer a powerful defense by ensuring both the environment and the data within it are secure.

6.3 Cloud Native Application Protection Platform (CNAPP)

Cloud Native Application Protection Platform (CNAPP) is an emerging, comprehensive security approach that unifies various cloud security capabilities into a single platform. Gartner introduced the CNAPP concept, which typically integrates functionalities such as Cloud Security Posture Management (CSPM), Cloud Workload Protection Platform (CWPP), Cloud Infrastructure Entitlement Management (CIEM), and potentially elements of network security and API security. The goal of CNAPP is to provide end-to-end security for cloud-native applications throughout their lifecycle, from development to production.

DSPM vs. CNAPP:

DSPM is a critical, specialized capability that can be integrated into or complement a broader CNAPP strategy. While CNAPP aims to cover the entire cloud-native application lifecycle, it may or may not include a dedicated, robust DSPM module. Many CNAPP vendors are now incorporating DSPM capabilities into their offerings, recognizing the paramount importance of securing the data itself within cloud-native applications and services. When integrated into a CNAPP, DSPM provides the essential data context—discovering sensitive data, assessing its risks, and managing access to it—which enhances the overall effectiveness of the platform’s security posture management, workload protection, and entitlement management functions. Therefore, DSPM can be seen as a specialized, deep-dive data security layer that strengthens the holistic protection offered by a CNAPP.

6.4 Data Governance and Data Management

While not directly security tools, Data Governance and Data Management are closely related organizational disciplines that significantly benefit from DSPM. Data governance establishes policies, processes, and responsibilities for managing data, including its quality, usability, integrity, and security. Data management encompasses the practical execution of these policies, covering aspects like data storage, data integration, data warehousing, and data lifecycle management.

DSPM’s Relationship:

DSPM provides the security ‘muscle’ and visibility that empowers effective data governance and management. By accurately discovering and classifying sensitive data, and by continuously monitoring its security posture, DSPM provides the factual basis upon which governance policies can be built and enforced. For instance, DSPM identifies where sensitive data is located (crucial for data residency policies), who has access to it (informing access policies), and how it’s being used (informing data retention and deletion policies). It acts as a critical enabler, providing the data-centric intelligence required to implement and audit comprehensive data governance frameworks effectively.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

7. Implementing DSPM: Best Practices

Effectively implementing Data Security Posture Management requires a strategic, phased approach, integrating technological solutions with robust organizational processes and a clear understanding of data assets. The following best practices provide a roadmap for organizations embarking on or enhancing their DSPM journey.

7.1 Comprehensive Data Inventory and Mapping

The foundational step for any DSPM implementation is to achieve a complete and accurate understanding of an organization’s data landscape. This involves:

• Discover All Data Assets: Systematically identify and catalog all data repositories across the entire environment – on-premises databases, data warehouses, file shares, network drives, public cloud storage (S3, Azure Blob, Google Cloud Storage), private cloud instances, SaaS applications (Salesforce, Microsoft 365, Google Workspace), development environments, and even shadow IT instances. This must include both structured (databases) and unstructured (documents, emails, media) data.
• Automated Discovery Tools: Rely heavily on automated DSPM tools that can connect to various data sources via APIs or agents, performing deep scans to identify actual data content, not just metadata. Manual inventory is unsustainable and prone to significant inaccuracies.
• Data Flow Mapping: Beyond static inventory, understand how sensitive data moves throughout the organization – from creation to storage, processing, sharing, and archiving. Map data lineage to identify potential choke points, vulnerabilities, and compliance implications.
• Regular Updates: Data environments are dynamic. The inventory must be continuously updated to reflect new data creation, changes in storage locations, and deletion of data. This is an ongoing process, not a one-time activity.

7.2 Automated and Contextual Data Classification

Once data is discovered, accurate classification is paramount. It dictates the appropriate security controls, compliance requirements, and risk prioritization.

• Define Classification Policies: Establish clear, organizational-wide policies for data classification (e.g., Public, Internal, Confidential, Restricted, Highly Restricted) based on sensitivity, regulatory requirements (PII, PHI, PCI, GDPR), and business impact.
• Leverage Advanced Classification Technologies: Utilize DSPM solutions that employ a combination of machine learning, natural language processing, regex, keyword matching, and exact data matching to classify data accurately and at scale. These tools should be able to infer context, not just match patterns.
• Maintain Granularity: Classification should be granular enough to apply precise controls. For instance, knowing a database contains ‘customer data’ is less helpful than knowing a specific column contains ‘customer PII (SSN, credit card number, date of birth)’.
• Integrate with Data Governance: Ensure classification tags and metadata are integrated with broader data governance frameworks and tools, allowing for consistent application across the data lifecycle.

7.3 Establish and Enforce Strict Access Control Policies

Access controls are a critical layer of defense against unauthorized data access and breaches. DSPM enhances traditional access management by focusing on effective access at the data layer.

• Principle of Least Privilege (PoLP): Implement and continuously enforce PoLP, ensuring users and service accounts have only the minimum necessary permissions to perform their duties. DSPM tools help identify and remediate instances of over-privileged access.
• Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC): Design and implement robust access policies based on roles, attributes, and contextual factors (e.g., location, time of day, device type).
• Just-in-Time (JIT) and Just-Enough-Access (JEA): For highly sensitive data or critical operations, implement temporary, time-bound access that is automatically revoked after use.
• Regular Access Reviews and Audits: Conduct periodic, automated reviews of all data access entitlements, especially for sensitive data. DSPM simplifies this by providing a clear view of effective permissions and highlighting anomalies.
• Integration with IAM/PAM: Integrate DSPM with existing Identity and Access Management (IAM) and Privileged Access Management (PAM) solutions to ensure consistent identity management and control over privileged accounts.

7.4 Conduct Regular Audits and Risk Assessments

DSPM is not a static solution; it requires continuous assessment and adaptation.

• Automated Risk Assessments: Leverage DSPM’s capabilities to continuously scan for data risks, including data exposure (public S3 buckets), misconfigurations (unencrypted databases), compliance violations (data residency), and excessive access.
• Prioritize Risks: Implement a risk scoring mechanism that considers data sensitivity, exposure level, potential business impact, and exploitability to prioritize remediation efforts effectively.
• Compliance Mapping: Map identified risks directly to specific controls within relevant regulatory frameworks (GDPR, HIPAA, PCI DSS). This streamlines compliance reporting and ensures targeted remediation.
• Security Audits: Supplement automated assessments with independent security audits, penetration testing, and red team exercises to validate the effectiveness of DSPM controls.

7.5 Implement Continuous Monitoring and Automated Remediation

Real-time visibility and rapid response are hallmarks of effective DSPM.

• Real-time Data Activity Monitoring: Monitor all data access, modification, and movement events across all repositories. Utilize behavioral analytics (UEBA) to detect anomalous activities that could indicate an insider threat or compromised account.
• Configuration Drift Detection: Continuously monitor changes to data store configurations and security policies, alerting on any deviations from the secure baseline.
• Automated Alerts and Workflows: Configure DSPM to generate immediate alerts for high-priority risks or incidents. Integrate with Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) platforms to automate incident response workflows.
• Policy-Driven Remediation: Where feasible and safe, implement automated remediation actions based on predefined policies. Examples include automatically encrypting unencrypted sensitive data, revoking excessive permissions, or isolating compromised data stores.

7.6 Integrate with Existing Security Ecosystem

DSPM should not operate in a vacuum. Its effectiveness is amplified when integrated with other security tools.

• SIEM/SOAR: Send DSPM alerts and audit logs to SIEM for centralized logging and correlation, and leverage SOAR for automated incident response playbooks.
• CSPM/CNAPP: Share data-centric risk context with CSPM and CNAPP tools to provide a more holistic view of cloud security, bridging the gap between infrastructure risks and data risks.
• DLP: Inform DLP policies with DSPM’s precise data classification and location intelligence, making DLP more effective in preventing data exfiltration.
• IAM/PAM: Synchronize identity and access data and leverage PAM for managing privileged access to sensitive data stores.

7.7 Define Clear Policies and Incident Response Plans

Technology alone is insufficient. Robust policies and well-defined processes are essential.

• Establish Clear Data Security Policies: Develop comprehensive policies covering data classification, access control, encryption, data retention, and incident response, ensuring they are well-communicated and understood across the organization.
• Develop Data-Specific Incident Response Plans: Create or update incident response plans to specifically address data breaches and data-centric security incidents, detailing roles, responsibilities, communication protocols, and remediation steps.
• Regular Training and Awareness: Conduct ongoing security awareness training for all employees, emphasizing data handling best practices, recognizing social engineering attempts, and understanding their role in maintaining data security.

By systematically adopting these best practices, organizations can build a robust, proactive, and resilient data security posture that protects sensitive information throughout its lifecycle, minimizes risk, and ensures continuous compliance.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

8. Challenges in DSPM Adoption

Despite the clear advantages and strategic imperative of Data Security Posture Management, organizations often encounter a range of significant challenges during its adoption and operationalization. These challenges stem from the inherent complexities of modern IT environments, resource limitations, and the dynamic nature of cybersecurity threats.

8.1 Complexity and Diversity of Data Environments

One of the most formidable challenges is the sheer complexity and heterogeneity of contemporary data environments. Organizations rarely operate within a single, homogeneous data landscape; instead, they manage:

• Hybrid and Multi-Cloud Infrastructures: Data resides across on-premises data centers, private clouds, and multiple public cloud providers (AWS, Azure, Google Cloud), each with its own APIs, security models, and configurations.
• SaaS Application Proliferation: Sensitive data is increasingly stored within third-party SaaS applications, where direct visibility and control are limited by the vendor’s offerings.
• Data Sprawl and Silos: Data is fragmented across myriad databases (relational, NoSQL), object storage buckets, file shares, data lakes, data warehouses, and collaboration platforms. These data silos often lack consistent tagging, metadata, or security controls.
• Unstructured and Semi-structured Data: A significant portion of enterprise data is unstructured (documents, emails, media files) or semi-structured (logs, sensor data), making automated discovery and classification more challenging than with highly structured data.
• Legacy Systems: Older systems, often critical for business operations, may not easily integrate with modern DSPM solutions or lack the necessary APIs for deep data inspection.

This immense diversity makes it incredibly difficult to achieve a comprehensive data inventory and consistent classification, leading to potential blind spots and inconsistencies in security posture management.

8.2 Resource Constraints: Skills and Budget

Implementing and effectively managing DSPM requires substantial resources, which can be a significant hurdle for many organizations:

• Skilled Personnel Shortage: There is a global shortage of cybersecurity professionals with expertise in data security, cloud security architectures, and advanced analytics. Deploying and tuning sophisticated DSPM solutions, interpreting their findings, and orchestrating remediation efforts demands a highly specialized skill set.
• Budgetary Limitations: DSPM solutions, especially those with advanced AI/ML capabilities and broad coverage, can represent a significant investment. Organizations may struggle to secure the necessary budget for licensing, integration, and ongoing operational costs.
• Time and Effort for Integration: Integrating DSPM tools with existing security ecosystems (SIEM, IAM, DLP, CSPM, SOAR) and various data sources can be a complex, time-consuming, and resource-intensive endeavor, often requiring custom development or extensive API configuration.

8.3 Evolving Threat Landscape and Attack Vectors

The adversarial environment is in constant flux, posing continuous challenges to DSPM effectiveness:

• Sophisticated Attack Techniques: Cybercriminals and nation-state actors are employing increasingly sophisticated techniques, including zero-day exploits, advanced persistent threats (APTs), supply chain attacks, and AI-driven social engineering.
• Insider Threats: Both malicious and accidental insider actions pose a constant risk. DSPM must be adept at detecting subtle anomalies that indicate compromised accounts or disgruntled employees.
• Ransomware and Data Extortion: Modern ransomware attacks often involve data exfiltration before encryption, turning data protection into a critical first line of defense against extortion.
• Cloud-Specific Threats: The unique nature of cloud environments introduces new attack vectors, such as exploiting misconfigured serverless functions, container vulnerabilities, or API flaws that can lead to data exposure.

DSPM solutions must continuously evolve their detection mechanisms, threat intelligence integration, and remediation capabilities to keep pace with these rapidly changing threats.

8.4 Accuracy of Discovery and Classification (False Positives/Negatives)

While automated discovery and classification are powerful, achieving perfect accuracy is challenging:

• False Positives: Over-aggressive classification can lead to a high volume of false positives, where non-sensitive data is flagged as sensitive. This can cause ‘alert fatigue’ for security teams, leading to ignored legitimate alerts, and unnecessary application of stringent controls.
• False Negatives: Conversely, missing genuinely sensitive data (false negatives) leaves critical information unprotected, creating dangerous blind spots. This can occur with highly unique data, encrypted data, or data embedded in complex unstructured formats.
• Contextual Ambiguity: The sensitivity of data can often be context-dependent. A name and address might be public in one context but highly sensitive in another (e.g., patient records). DSPM solutions must be intelligent enough to interpret context.

Fine-tuning DSPM policies and algorithms to minimize both false positives and negatives requires continuous effort, data scientist expertise, and iterative refinement.

8.5 Organizational Silos and Lack of Data Ownership

Effective DSPM requires cross-functional collaboration, which is often hindered by organizational silos:

• Disparate Teams: Security teams, IT operations, data owners, legal, and compliance departments often operate independently, leading to misaligned priorities and communication gaps.
• Unclear Data Ownership: In many organizations, particularly with legacy systems or shadow IT, clear data ownership is lacking. Without designated data owners, it becomes difficult to establish accountability for data security, approve access policies, or drive remediation efforts.
• Resistance to Change: Implementing DSPM often requires changes to existing processes, access patterns, and even development workflows, which can be met with resistance from various stakeholders.

Breaking down these silos and fostering a culture of shared responsibility for data security is paramount for successful DSPM adoption.

8.6 Data Volume and Velocity

The sheer scale and speed at which data is generated and modified in modern enterprises present an enormous challenge for DSPM solutions:

• Performance Impact: Scanning and analyzing massive datasets in real-time or near real-time without impacting operational performance of critical systems is technically demanding.
• Storage and Processing: Storing and processing the immense volume of metadata, access logs, and audit trails generated by continuous monitoring requires significant infrastructure and efficient data management capabilities within the DSPM solution itself.
• Scalability: DSPM platforms must be highly scalable to handle ever-increasing data volumes and expanding environments without degradation in performance or accuracy.

Addressing these challenges requires careful planning, selecting robust DSPM solutions, and fostering a collaborative organizational approach to data security. Overcoming these hurdles is essential for realizing the full potential of DSPM in safeguarding an organization’s most valuable asset.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

9. Future Directions in DSPM

As the data landscape continues its relentless evolution, driven by emergent technologies and an escalating threat surface, Data Security Posture Management is poised for significant advancements. The future trajectory of DSPM will be characterized by deeper integration, enhanced intelligence, greater automation, and a more proactive stance against sophisticated threats.

9.1 Deeper Integration with Artificial Intelligence (AI) and Machine Learning (ML)

The capabilities of AI and ML are already being leveraged in current DSPM solutions, but their integration will become even more pervasive and sophisticated:

• Predictive Risk Analytics: AI models will move beyond simply identifying current risks to predicting future data security vulnerabilities based on patterns, historical data, and emerging threat intelligence. This will enable organizations to implement proactive rather-than-reactive security measures.
• Intelligent Data Classification: ML algorithms will achieve even higher levels of accuracy and context awareness in classifying sensitive data, minimizing false positives and negatives across vast, unstructured datasets. They will be able to dynamically adjust classification based on data usage patterns and evolving regulatory interpretations.
• Automated Policy Generation and Optimization: AI will assist in automatically generating optimal security policies based on discovered data, its context, regulatory requirements, and organizational risk tolerance. It will also continuously analyze policy effectiveness and suggest optimizations.
• Advanced Anomaly Detection and Behavioral Analytics: ML-driven User and Entity Behavior Analytics (UEBA) will become more nuanced, identifying highly subtle deviations from baselines, distinguishing between legitimate and malicious activities with greater precision, and correlating seemingly disparate events to uncover complex attack chains involving data.
• Contextual Data Protection: AI will enable DSPM to understand not just what the data is, but who is using it, why, from where, and on what device, allowing for highly dynamic and contextual access control and protection decisions.

9.2 Enhanced Automation of Remediation Processes

While current DSPM solutions offer varying degrees of automated remediation, the future will see a significant expansion in this capability:

• Self-Healing Data Security: DSPM platforms will increasingly implement ‘self-healing’ mechanisms, automatically correcting misconfigurations, revoking excessive permissions, or encrypting unencrypted sensitive data without requiring human intervention, adhering to predefined runbooks and risk thresholds.
• Automated Incident Response Orchestration: Tighter integration with SOAR platforms will enable DSPM to trigger comprehensive incident response playbooks automatically, encompassing not just data remediation but also isolating affected systems, notifying relevant stakeholders, and collecting forensic evidence.
• Policy-as-Code Implementation: The ability to define data security policies as code will facilitate faster deployment, consistency, and automated enforcement across diverse infrastructure environments, particularly in DevOps and cloud-native contexts.

9.3 Broader and More Dynamic Compliance Frameworks

DSPM will evolve to handle an even wider array of compliance requirements and provide more dynamic, real-time compliance posture management:

• Automated Evidence Generation: DSPM solutions will become more adept at continuously gathering and presenting audit-ready evidence for a broad spectrum of regulatory requirements (GDPR, HIPAA, PCI DSS, ISO 27001, SOC 2, FedRAMP, etc.), significantly reducing the manual effort involved in compliance audits.
• Data Residency and Sovereignty Enforcement: With increasing global regulations around data residency, DSPM will offer more sophisticated capabilities for monitoring and enforcing data location rules, automatically flagging or preventing data from crossing geographical boundaries in violation of policy.
• Customizable Regulatory Mappings: Organizations will have greater flexibility to define and map internal policies to specific regulatory controls, tailoring DSPM’s compliance features to their unique needs.

9.4 Proactive Threat Intelligence Integration

The integration of external threat intelligence will move beyond simple alerts to more predictive and contextual insights:

• Risk Prioritization with Threat Context: DSPM will integrate real-time threat intelligence feeds to contextualize and re-prioritize data risks. For example, sensitive data exposed to a vulnerability that is actively being exploited in the wild will be assigned a significantly higher risk score.
• Attack Path Visualization: Combining DSPM’s data visibility with network and identity insights will allow for visualization of potential attack paths to sensitive data, enabling organizations to proactively block these paths before they are exploited.

9.5 Shift-Left Security for Data

Embracing DevSecOps principles, DSPM will increasingly ‘shift left’ into the development lifecycle:

• Secure by Design Data: Integrating DSPM capabilities into CI/CD pipelines will allow developers to identify and remediate data security risks and misconfigurations before applications and data stores are deployed into production. This includes scanning code for sensitive data hardcoded, insecure API calls, or non-compliant data handling practices.
• Early Identification of Data Exposure: Proactive scanning of development and testing environments for sensitive data and misconfigurations will prevent these issues from propagating to production, where remediation is far more costly and impactful.

9.6 Contextual Data Security and Data Fabric Integration

• Unified Data Security Layer: DSPM will become a foundational layer within a broader ‘data fabric’ or data mesh architecture, providing consistent data security policies and enforcement across diverse data sources and consumption patterns.
• Business Context Integration: Future DSPM solutions will more deeply integrate with business context – understanding the criticality of specific data to business processes, key performance indicators, and revenue streams – to provide more intelligent risk prioritization and protection strategies.

The future of DSPM is bright, promising a more intelligent, automated, and proactive approach to data security that will be critical for organizations to navigate the complexities of digital transformation securely and compliantly.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

10. Conclusion

Data Security Posture Management (DSPM) represents a fundamental and necessary paradigm shift in how organizations approach data security in an increasingly interconnected and data-dependent world. No longer sufficient are perimeter-focused defenses or infrastructure-centric security models; the distributed nature of modern data, coupled with the relentless evolution of cyber threats and stringent regulatory mandates, demands a data-centric approach that places the protection of information itself at the forefront. DSPM emerges as the critical discipline that fulfills this requirement.

Through its core functionalities – automated sensitive data discovery and classification, granular access governance, continuous risk assessment at the data layer, and proactive monitoring with automated remediation – DSPM empowers organizations to achieve unprecedented visibility and control over their most valuable digital assets. It moves beyond reactive threat responses to proactive risk mitigation, systematically identifying, prioritizing, and addressing vulnerabilities that could lead to data exposure, breaches, or non-compliance. DSPM’s strategic importance is undeniable, serving as a cornerstone for regulatory adherence, safeguarding proprietary and sensitive information, bolstering data visibility across complex environments, and significantly strengthening the efficacy of Zero Trust architectures.

While the adoption of DSPM presents challenges, including the inherent complexity of diverse data environments, resource constraints, and the perpetually evolving threat landscape, the imperative to overcome these hurdles is clear. As DSPM continues to integrate with advanced AI and Machine Learning capabilities, automate remediation processes, expand its coverage for compliance frameworks, and shift security left into development pipelines, its role will only grow in significance. It will enable organizations to not only protect their data but also to leverage it confidently, foster customer trust, and maintain operational resilience in the face of an ever-present and sophisticated threat landscape. DSPM is not merely another security tool; it is an foundational strategy for building a secure, compliant, and trustworthy digital future.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

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