Data Security Posture Management (DSPM)

Modern enterprises face unprecedented challenges in securing data across complex cloud environments and hybrid infrastructures. Data Security Posture Management (DSPM) has emerged as a critical cybersecurity approach providing comprehensive visibility and control over sensitive data, regardless of location.

The proliferation of data in modern organisations is rapidly increasing the risks of valuable or sensitive data loss or compromise. These risks make data security the #1 problem for security stakeholders—especially those using legacy strategies for protection.

The rapid adoption of cloud services and the shift toward agile development have transformed how organisations store, process, and protect their data. Organisations must now manage data across multiple clouds, applications, and storage technologies. DSPM complements existing security tools like Cloud Security Posture Management (CSPM) by focusing on data protection rather than infrastructure security, creating a more complete cybersecurity strategy.

Data Security Posture Management (DSPM) represents a data-first approach to security that provides comprehensive visibility and control over sensitive data across an organisation’s entire digital ecosystem. While CSPM focuses on infrastructure security, DSPM specifically addresses data protection by helping organisations discover, classify, and secure sensitive information wherever it resides.

DSPM charts a modern path for understanding everything that affects the security posture of your data wherever it is, including in SaaS, PaaS, public or multi-cloud, on-prem, or hybrid environments. DSPM tells you where valuable or sensitive data is, who can access it, and what its security posture is.

DSPM enables organisations to maintain continuous awareness of their data assets and protection status through three key capabilities:

• Automated discovery and classification of sensitive data across environments
• Real-time monitoring of data access and usage patterns
• Risk assessment and remediation based on data sensitivity and exposure

Organisations rely on DSPM solutions that enable them to identify, classify, and reinforce sensitive data at scale. Through AI-powered technology, organisations can secure their data across SaaS, PaaS, public clouds, multi-cloud, on-premises, and hybrid environments while reducing human-centric risks.

One of the biggest cybersecurity questions is, “Where is our data?” You can’t begin to secure data until you know where it is—especially critical business, customer, or regulated data. As we’ve learned in this new era of agile, your data can be almost anywhere. Achieving better visibility is the first step in Data Security Posture Management.

The analyst and vendor communities describe various types of posture management. They all address two general questions: What are the issues, and how can we fix them? Data Security Posture Management is a relatively new prescriptive approach for securing data. As defined by Gartner in its Hype Cycle for Data Security 2022, “Data security posture management provides visibility as to where sensitive data is, who has access to that data, how it has been used, and what the security posture of the data store or application is.”

Gartner has designated DSPM with a “transformational” benefit rating, recognising it as an urgent solution for modern data security challenges.

DSPM operates through a systematic three-step process to secure organisational data:

• Discovery and analysis: The system automatically identifies and catalogues data locations across cloud services, SaaS platforms, and on-premises environments. It also detects shadow data stores and forgotten databases that could pose security risks.
• Risk detection: DSPM evaluates potential vulnerabilities by analysing access paths, user privileges, and data sensitivity. This process weighs factors like vulnerability severity, data classification, and resource configurations to prioritise remediation efforts.
• Continuous monitoring: The framework provides ongoing assessment of data security posture, ensuring compliance with regulatory requirements while adapting to new threats and changing business needs.

Learn more about Proofpoint’s DSPM solutions and leverage a platform that discovers and classifies sensitive data while escalating risks and minimising attack surfaces. Or see the data sheet for more details.

DSPM Discovers Where Data Is and Analyses What It Consists Of

The discovery of data location is a huge issue because of the nature of agile. In DevOps and model-driven organisations, the amount of structured and unstructured data that can be located almost anywhere is vastly larger and continually expanding.

In legacy scenarios, all the data was stored on-premises, which spawned the “Castle & Moat” network security model of restricting external access while allowing internally trusted users. The need for more flexible and agile computing has fragmented legacy architecture and resulted in significant amounts of data being moved to external locations operated by third parties. For security architects, practitioners, and those responsible for compliance, this titanic shift in data location, along with massive growth in data volumes, calls for a different approach to securing the data: hence, Data Security Posture Management.

The DSPM approach acknowledges that agile architectures are far more complex because data environments are not monolithic. For most enterprises, data is stored in many physical and virtual places: two or more cloud service providers such as Amazon, Microsoft, or Google; private clouds; software-as-a-service providers; platform-as-a-service and data lake providers like Snowflake and Databricks; business partners; LLMs; and, of course, on-premises servers and endpoints within an organisation.

Data isn’t just moving to more places. The velocity of data creation is soaring with the modern explosion of microservices, the growing frequency of changes, the acceleration of access for modelling, and the constant iterations of new code by DevOps. Some of the fallout for security include shadow data stores and abandoned databases, which lure attackers like honey draws bees.

Locating your data is just the beginning. Classification analysis is needed to help your team understand the nature of the data and determine levels of concern regarding data protection and monitoring requirements—especially for data subject to compliance mandates.

DSPM Detects Data at Risk and Prioritises Orders of Remediation

The second phase of the DSPM involves detecting which data is at risk. A precursor is identifying all systems and related operations running in your organisation’s environment. Detecting all infrastructure helps determine all the access paths to your data and which paths may require changes in access permissions or new controls for protection.

Access rights are challenging because structured and unstructured data can be found in many types of data stores: cloud-native databases, block storage, and file storage services. For each of these, your team must spot access misconfigurations, inflated access privileges, dormant users, vulnerable applications, and exposed resources with access to valuable and sensitive data.

If your organisation is coming up to speed on these issues, be aware that security teams must closely collaborate with data and engineering teams due to rapidly evolving application architectures and changes to microservices and data stores.

Access is not the only data risk—so is the nature of the data. Your teams must prioritise the data to enable ranking its importance and risk level. Is the data proprietary, regulated, valuable, or otherwise sensitive? Risk assessment is a composite of vulnerability severity, the nature of the data, its access paths, and the condition of its resource configurations. Higher risk means remediation becomes Priority One!

DSPM Remediates Data Risks and Prevents Their Reoccurrence

Securing data at risk entails remediating the associated vulnerabilities discovered during the Discovery and Detection phases of DSPM. In legacy scenarios, teams often focus on ensuring the classic perimeter, but a new, much broader scope of risks must be addressed in modern hybrid environments. Remediation requires a cross-functional approach. Depending on the scenarios, the team will need help with network and infrastructure operations, cloud configuration management, identity management, databases, LLMs, backup policies, DevOps, and more.

Data security is usually governed by controls provided by a particular service provider. However, the enterprise subscriber also shares a critical role in addressing several issues primarily related to configuration management:

• Identify where workloads are running
• Chart relationships between the data and infrastructure and related business processes to discover exploitable paths
• Verify user and administrator account privileges to identify users with overprivileged access rights and roles
• Inspect all public IP addresses related to your cloud accounts for potential hijacking

Since the major cloud service providers do not provide integrated, interoperable security and configuration controls for disparate clouds, your organisation must ensure that security access controls are properly configured for multi-cloud and hybrid environments.

The classic “Castle & Moat” cybersecurity model restricts external access and allows trusted internal users. While familiarity breeds comfort, security leaders should not take comfort in this business-as-usual approach.

We’ve seen a never-ending stream of successful attacks and data breaches, deeming Castle & Moat unreliable. It’s also misplaced because attackers aren’t going after your castle. Their real target is your data—in these days of agile, that could be almost anywhere. And how do you know the attackers are not already inside the castle?

Here are six reasons to put data at the centre of your security strategy instead of relying on a legacy Castle & Moat approach.

1. CI/CD Brings an Explosion of Deployments and New Changes

The constant change in business requirements has fuelled the need to automate the stages of application development. Continuous integration and continuous delivery (CI/CD) accelerate application development and make frequent changes to a codebase. If you’re not familiar with CI/CD, think of it this way: Your organisation’s application developers (or DevOps team) are deploying brand new functionality in your organisation’s apps not once a month or even once a week—think 5, 10, or 15 times or more every day.

DevOps team members are to be congratulated for their greater agility, but quick code turnarounds can and do add risks to data security. The risk of data leakage rises with the increased complexity and higher velocity of services and changes.

Data is especially at risk when DevOps constantly spins up instances and links to data repositories, especially when temporary buckets or forgotten copies of data are used for testing apps.

2. AI/ML Fuels the Need for More Access to Data for Modeling

Compared to legacy apps, machine learning (ML) workloads require enormous amounts of both structured and unstructured data to build and train models. As data scientists experiment with models and evolve them to meet new business requirements, new data stores are created for testing and training.

This constant movement of production data into nonproduction environments may expose it to potential exploits. Putting data at the centre of your security strategy helps ensure that controls are extended to wherever data is—whether inside or outside of production environments.

3. Microservices Drive More Services and Granular Data Access

The cardinal rule of football, basketball, baseball, and other ball games is to keep your eye on the ball. The same lesson applies to data security: Keep your eye on the data. Doing so was easier for legacy applications, which were built with a three-tier architecture and a single data store. In that scenario, protecting application data merely required protecting that one database.

Modern app development uses multiple microservices with their own data stores, which contain overlapping pieces of application data. This vastly complicates data security, especially as new features often introduce new microservices with more data stores.

The number of access paths to these data stores also increases quadratically over time. Continuously reviewing the security posture of these multiplying data stores and access paths by hand is impossible—and is one more reason for using automation to help keep the team’s eye on the data.

4. Data Proliferation Brings More Copies Into More Places

The proliferation of copies of data in different storage locations is a big issue for organisations using infrastructure-as-a-service (IaaS) and infrastructure-as-code (IaC) options. These architectures allow getting things done quickly, but “faster” often means there’s no one looking over your shoulder to apply security checks to the expanding data. Putting data at the forefront of your security policy will help provide the ability to automatically follow data to wherever it’s stored and automatically apply security controls to ensure the data is protected from unauthorised access.

Different systems often operate with distinct security protocols, creating visibility issues and inconsistent security policies across environments. Organisations must now handle an overwhelming volume of data, 80-90% of which is unstructured content across emails, spreadsheets, and various digital formats.

5. The Security of Cloud Infrastructure Suffers When Data Access is Misconfigured

Access authorisation is a pillar of data security. Without it, the data is a sitting duck for attackers. But what if authorisation controls are improperly implemented? Perhaps someone simplified or removed them to facilitate easy use by DevOps. Are controls consistently applied to data wherever it is?

According to Gartner analysts, most cloud breaches are caused by misconfiguring the cloud infrastructure (IaaS and PaaS). A data-first approach to security should ensure that access configurations for data are properly used wherever data is.

Of paramount concern are Advanced Persistent Threats (APTs), advanced cyber-attacks where attackers gain and maintain unauthorised access to networks for extended periods. These threats specifically target sensitive data exfiltration rather than causing system outages. The cloud data security gap has become particularly concerning, with cloud-based data breaches accounting for 45% of all breaches in 2023.

6. Privacy Regulations Require More Control and Tracking of Data

Compliance is a significant driver of data security. Examples include personally identifiable data for GDPR, payment account data, sensitive authentication data for PCI DSS, and personal health data for HIPAA. Non-compliance in protecting valuable and sensitive data like these can trigger substantial penalties.

For serious violations, the GDPR can impose fines of up to €20 million or 4% of global annual turnover. Similarly, HIPAA violations can result in penalties ranging from $100 to $50,000 per affected record. DSPM helps organisations automatically detect and classify data related to compliance requirements, mapping it to relevant regulatory benchmarks.

Data is your organisation’s most valuable asset. A data-first security policy should enable automatic discovery and classification of all protected data anywhere in the environment. Security teams must have 100% visibility into the location of valuable and sensitive data to ensure its protection. A legacy Castle-and-Moat approach to security will fall short in modern environments. For the reasons mentioned, adopting a data-first strategy for security is essential for keeping data secure. That’s the purpose of DSPM.

The DSPM platform will automate five domains of capabilities for assessing the security posture of data, detecting and remediating risks, and ensuring compliance. In general, it’s helpful to look for a DSPM platform that is agentless and deploys natively in any of the major clouds (AWS, Azure, GCP, or Snowflake) and against leading SaaS applications and on-premises databases and file stores.

The platform should provide 100% API access to easily integrate the use of any of your existing tools’ data required for using DSPM in your organisation’s environment. Naturally, the platform should also use role-based access control to keep the management of data security posture just as secure as the valuable and sensitive data should be. All of these will minimise roadblocks and make DSPM quickly productive for your teams.

Data Discovery with DSPM

Discovery capability answers the question, “Where is my valuable and sensitive data?” DSPM should discover structured, unstructured, and semi-structured data across an extensive array of data stores from major cloud providers and SaaS platforms, plus various enterprise applications like Snowflake, Salesforce, and Workday, as well as on-premises databases and file shares. This information and any other objects that could be at risk should be reported to security teams.

Data Classification with DSPM

Classification tells you if your data is valuable or sensitive and what kind of data it is. It answers questions like “Are there shadow data stores?” First and foremost, you want DSPM classification capability to be automated and accurate. If the platform cannot do this automatically and accurately, it defeats the purpose of doing DSPM in modern hybrid environments.

Automation must address a variety of classification capabilities:

• Analyse actual content in data stores (vs. object/table/column names)
• Provide classifiers out of the box (no customer-defined rules required; this slows you down!)
• Identify regulated data (GDPR, PCI DSS, HIPAA, etc.)
• Allow user definition of classifiers for proprietary/unique data
• Have classification identify valuable or sensitive data in newly added databases/tables/columns
• Notify the security team of the discovery of new valuable or sensitive data
• Scan data where it sits without any data leaving your organisation’s environment
• Sample data while scanning to reduce computing costs
• Detect valuable or sensitive data that uses proximity to increase accuracy
• Workflow to fix false positives when valuable or sensitive data is miscategorised

Access Governance with DSPM

Access governance ensures that only authorised users can access specific data stores or types of data. DSPM’s access governance processes will also discover related issues, such as: “Who can access what data?” or “Are there excessive privileges?” A platform’s automated capabilities should include identifying all users with access to data stores. It should also identify all roles with access to those data stores.

DSPM should also identify all resources that access those data stores. The platform should also track the level of privileges associated with each user/role/resource. Finally, DSPM must detect external users/roles with access to the data stores. All this information will inform analytics and help determine the level of risk associated with your organisation’s data stores.

Risk Detection and Remediation with DSPM

This domain is about functions of vulnerability management. Risk detection involves identifying potential attack paths that could lead to a breach of valuable or sensitive data. Legacy security typically focuses on the infrastructure supporting data (e.g., network gear, servers, endpoints, etc.).

DSPM focuses on detecting vulnerabilities affecting valuable and sensitive data and insecure users accessing valuable or sensitive data. DSPM also checks data against industry benchmarks and compliance standards such as GDPR, SOC2, and PCI DSS. The main idea is to visually map out relationships across data stores, users, and resources to guide investigation and remediation. The platform should enable building custom risk detection rules that combine valuable and sensitive data, access, risk, and configurations. It should support custom queries to detect and find potential data security risks unique to your organisation and environment.

Security teams should be given trigger notifications to specific assignees upon detection of risks. Related workflows should automatically trigger third-party products such as ticketing systems. To ease usability, modern graph-powered capabilities will visualise and enable queries to spot attack paths to valuable and sensitive data.

Compliance with DSPM

Modern organisations must comply with various laws and regulations governing valuable and sensitive data. For example, the European Union’s General Data Protection Regulation (GDPR) aims to ensure the rights of EU citizens over their personal data, such as names, biometric data, official identification numbers, IP addresses, locations, and telephone numbers. A tiered system of fines for non-compliance can be up to 4% of a company’s global annual turnover or 20 million Euros (whichever is greater).

Similar laws, such as the Health Insurance Portability and Accountability Act (HIPAA), Gramm-Leach-Bliley Act (GLBA), the Payment Card Industry Data Security Standard (PCI DSS), and the new California Consumer Privacy Act (CCPA), mandate securing specific types of valuable and sensitive data. DSPM must be able to automatically detect and classify all data within all your organisation’s data stores related to any relevant laws and regulations. It should automate mappings of your data to compliance benchmarks.

Your organisation’s stakeholders should receive a coverage heatmap on data compliance gaps, such as misplaced personally identifiable information (PII), shadow data, or abandoned data stores with valuable and sensitive data. Data officers should receive a dashboard and report to track and manage data compliance by region, function, etc. In addition to ensuring the security of regulated valuable and sensitive data, the platform should also simplify and accelerate documenting compliance for auditors.

Proofpoint’s DSPM solutions provide rapid time-to-value through in-place scanning and help security teams address the full spectrum of data security challenges across today’s complex data landscape. Learn more by contacting Proofpoint.

The core benefit of DSPM is accelerating your organisation’s ability to continuously keep all its data safe and secure wherever it is stored. Assessing and acting on your data security posture differs from other types of security posture, such as issues affecting the general cloud, applications, network, devices, identity, etc. Unlike these, DSPM focuses like a laser beam on your data.

As part of keeping your data safe and secure, DSPM specifically will help your security, IT operations, and DevOps teams to:

• Discover valuable and sensitive data (both structured and unstructured) across all your environments, including forgotten databases and shadow data stores.
• Classify valuable and sensitive data and map it to regulatory frameworks to identify the areas and amount of exposure and to track data lineage to understand where it came from and who had access to it.
• Discover attack paths to valuable and sensitive data that weigh data sensitivity against identity, access, vulnerabilities, and configurations—prioritising risks based on which are most important.
• Connect with DevSecOps workflows to remediate risks, particularly as they appear early in the application development life cycle.
• Identify abandoned data stores. Due to their lack of oversight, they are easy targets for attackers. For cost savings, they can often be decommissioned or transferred to more affordable storage repositories.
• Secure all your data in SaaS, PaaS, public or multi-cloud, on-prem, or hybrid environments.
• Secure your LLMs and AI systems to prevent unintended exposure of valuable and sensitive data.

DSPM platforms also seamlessly integrate with security and operational services from your organisation’s cloud service providers. These include major providers such as Amazon Web Services (AWS), Microsoft Azure (Azure), Google Cloud Platform (GCP), Snowflake, and other market-leading providers. DSPM provides a critical layer on top of the security and operational tools used within the provider’s cloud to ensure that data is classified and secured wherever it goes—across SaaS, PaaS, public or multi-cloud, on-prem, or hybrid environments.

The adoption of DSPM solutions, while beneficial, presents several significant challenges that organisations must address for successful implementation.

• Data complexity and scale: Managing and protecting data across various cloud platforms and on-premises systems creates substantial complexity. Organisations must handle diverse data types, formats, and locations while maintaining consistent security policies across their entire data landscape.
• Integration challenges: Organisations with legacy security systems often struggle to achieve seamless interoperability between DSPM and existing security solutions. This integration requires careful planning and testing to avoid disrupting current security incident response processes.
• User adoption and training: Employees may perceive new data security measures as obstacles to productivity, leading to resistance. Organisations need comprehensive training programmes and change management strategies to successfully adopt DSPM practices.
• Resource requirements: Implementing DSPM demands significant resources, including specialised expertise and infrastructure investments. The cost considerations have become particularly challenging for enterprises with extensive data environments across multiple clouds and locations.
• Continuous maintenance: Organisations face ongoing challenges in maintaining accurate data inventories and keeping security policies current. This includes managing outdated systems and unused data stores and maintaining proper access controls as organisational needs evolve.
• Technical configuration: Many organisations struggle with proper configuration management, including inadequate access controls, unprotected files and directories, and unnecessary feature access. If not properly addressed, these misconfigurations can create security vulnerabilities.

While these challenges may seem daunting, they can be effectively managed through proper planning and a phased implementation approach. Organisations that successfully navigate these obstacles position themselves to better protect their sensitive data and maintain a strong security posture.

A successful DSPM implementation requires a structured approach that aligns with organisational goals and security requirements. Here are the essential practices for maximising DSPM effectiveness.

Assess Current Security Posture

Begin with a comprehensive analysis of your existing data management practices and security controls. This assessment should identify gaps in current security measures and establish a baseline for improvement. Document your current data flows, storage locations, and security configurations to inform your DSPM strategy.

Choose the Right DSPM Solution

Select a DSPM solution that integrates seamlessly with your existing security stack and provides automated capabilities. Look for features that support comprehensive data discovery, classification, and risk assessment. The solution should scale with your organisation’s growth and adapt to evolving security requirements.

Prioritise Sensitive Data

Focus initial DSPM efforts on your most critical and sensitive data assets. Create a clear categorisation system based on data sensitivity and identify appropriate security configurations for each category. This risk-based approach ensures the most valuable assets receive immediate protection while building toward comprehensive coverage.

Foster a Culture of Security Awareness

Develop clear policies for data handling and ensure everyone understands their role in maintaining data security. Create new policies based on identified risks and establish clear procedures for enforcement. Regular training and communication help embed security awareness throughout the organisation.

Implement Automated Controls

Deploy automated security controls to reduce manual effort and ensure consistent policy enforcement. This includes implementing least privilege access, continuous monitoring, and automated remediation of security issues. Automation helps maintain security standards while reducing the workload on security teams.

Monitor and Adjust

Maintain continuous visibility through real-time monitoring and regular security assessments. Use DSPM dashboards and reporting features to track security metrics and adjust policies as needed. This ongoing process helps you identify new risks and opportunities to improve your data security posture.

While DSPM plays a crucial role in modern security architectures, it’s important to understand how it complements and differs from other security solutions. Each tool serves a specific purpose in the security ecosystem.

CSPM (Cloud Security Posture Management)

Cloud Security Posture Management focuses on securing cloud infrastructure configurations and ensuring compliance with security best practices. CSPM tools monitor cloud resources for misconfigurations, compliance violations, and infrastructure-related security risks.

While CSPM secures the infrastructure layer, DSPM takes a data-first approach by focusing on the actual sensitive data within these environments. DSPM provides deeper visibility into data usage patterns, access rights, and data movement across environments, complementing CSPM’s infrastructure-level security controls.

SIEM (Security Information and Event Management)

SIEM solutions aggregate and analyse security events from multiple sources across an organisation’s infrastructure to detect potential security incidents. These platforms excel at identifying suspicious activities and providing real-time threat detection.

DSPM differs by explicitly focusing on data security posture rather than event monitoring. Where SIEM tools track security events and logs, DSPM continuously assesses data risk exposure, classification, and protection status, providing a more comprehensive view of data security.

DLP (Data Loss Prevention)

DLP solutions prevent unauthorised data exfiltration by monitoring and blocking sensitive data movement across network boundaries. These tools primarily focus on preventing data breaches through policy enforcement at egress points.

DSPM extends beyond DLP’s preventive approach by providing continuous visibility into data security posture and risk exposure. While DLP controls data movement, DSPM offers a broader perspective on data security, including discovery, classification, risk assessment, and remediation across the entire data life cycle.

Various organisations across different sectors use DSPM to enhance their data security practices. Here are some of the primary users of DSPM:

• Enterprises with large data sets
• Cloud service users
• Regulated industries
• Technology companies
• Government agencies
• Small and medium-sized enterprises

DSPM Use Case 1: Automate Data Discovery and Classification Across All Data Stores

Two potential sources of valuable and sensitive data that are overlooked are shadow data stores and abandoned data stores. They often rest outside regular security controls, especially if they are ad hoc duplications made by data scientists and other data engineers for temporary testing and other purposes.

This DSPM use case especially benefits security teams by staying in lockstep with data and engineering teams to automatically discover, classify, and validate all data across all environments. The process includes inventorying structured and unstructured data across native databases, block storage, and file storage services.

DSPM Use Case 2: Prevent Data Exposure and Minimise the Attack Surface

Organisations pursue a hybrid cloud strategy because it enables innovation, which constantly evolves architectures and changes microservices and data stores. Security teams use DSPM to stay in lockstep with data and engineering teams to minimise data exposure and the associated attack surface.

The DSPM platform automatically identifies data at risk by continuously checking data stores—including abandoned or stale data stores, backups, and snapshots—and associated resources for misconfigurations. It detects vulnerable applications and exposed resources with access to valuable and sensitive data.

DSPM Use Case 3: Track Data Access Permissions and Enforce Least Privilege

Inappropriate access permissions enable the potential misuse or exposure of valuable and sensitive data, either accidentally by an insider or purposely by a bad actor. DSPM enables security teams to automatically get a simple, accurate view of access privileges for all data stores.

DSPM catalogues all users’ access privileges and compares these against actual usage to identify dormant users and those with excessive privileges. The resulting to-do list allows IT administrators to quickly correct excessive privileges or otherwise expunge dormant users whose accounts pose a potential risk to the data.

DSPM Use Case 4: Proactively Monitor for Compliance with Regulations

Various laws and regulations mandate data security compliance audits. DSPM security enables governance stakeholders to stay ahead of compliance and audit requirements via continuous checks against key benchmarks and associated controls. For example, PCI DSS Requirement 3 specifies that merchants must protect stored payment account data with encryption and other controls.

DSPM identifies stored payment account data and whether it is encrypted. Compliance activity like this is enabled by the platform’s data catalogue, access privilege intelligence, and risk detection capabilities—all of which illustrate valuable and sensitive data security posture and provide evidence for compliance audits.

DSPM Use Case 5: Enabled Use of AI

Generative AI and Large Language Models (LLMs) are introducing significant data security challenges. The lack of proper data classification poses a real risk of unintentionally processing and exposing valuable or sensitive information. The challenge is compounded by the proliferation of “shadow AI”—technologies deployed directly by business teams without IT oversight.

Such deployments can lead to inconsistent security practices and create vulnerabilities, as valuable and sensitive data might be used or accessed in ways that do not align with corporate data governance policies. DSPM implementation allows organisations to identify valuable and sensitive data before it’s fed into LLMs and generative AI applications so that proper steps can be taken to block or mask it or otherwise prevent unintended exposure.

Data Security Posture Management addresses critical security needs for organisations operating in complex multi-cloud environments and facing stringent compliance requirements. This approach provides granular visibility into sensitive data while automating risk remediation and aligning security practices with regulatory standards.

The leading DSPM platforms deliver AI-driven data discovery and contextual risk scoring while integrating seamlessly with existing security tools. Organisations should prioritise solutions that automate policy enforcement and maintain continuous compliance monitoring. These capabilities allow teams to protect sensitive assets without hindering operational efficiency.

DSPM solutions effectively reduce the attack surface by enforcing least-privilege access and mapping data across hybrid ecosystems. The right strategy turns data security into a proactive framework that adapts to emerging threats and dynamic business needs.