According to leading industry analysts like Gartner and GigaOm, a robust DSPM solution comes with the following components.
Discovery & Classification
Tracking data in complex cloud environments is fairly difficult, and protection is even more elusive. To put things into perspective, 53% of cybersecurity professionals lack visibility into their data stores, which puts the data of their customers, employees, and users at risk.
Data discovery is at the core of DSPM. The capability helps security teams scan their complex environments to identify data across a wide range of on-prem and cloud sources. For instance, DSPM discovers data in public clouds, such as AWS, GCP, or OCI; private clouds, including MongoDB, Oracle, or SAP; data clouds like Snowflake or Databricks; and SaaS applications like Slack or Salesforce. It ensures that no data, whether cloud-native, non-native, or shadow data, goes unnoticed.
Data discovery alone isn’t enough to protect data accurately. Organizations must be able to tell which data is sensitive, business-critical, or trivial. Here, classification helps build this picture accurately, allowing governance and security teams to find, manage, and protect data efficiently.
DSPM’s data classification capability allows teams to categorize data using out-of-the-box AI-powered classifiers and, oftentimes, customized fields. It classifies and labels data according to its sensitivity, such as confidential, public, or other types of data, or its regulatory context, such as protected health information (PHI), financial data, or personally identifiable information (PII).
Contextual Data+AI Intelligence
Data and AI are the twin engines that drive innovation. However, to innovate securely, organizations must gain deeper context around their data and AI assets in order to protect them. For instance, where the data resides across the environment, which roles, users, or identities access the data, what the configuration settings of sensitive datastores are, and how the data interacts with LLMs or AI agents.
However, challenges like data silos, manual processes, and conventional tools with limited scope create a thick barrier against contextual understanding of data.
DSPM with a centralized knowledge graph capability brings together scattered signals to offer actionable insights. It leverages tech stack integration, extensive data source connectivity, and contextual intelligence to provide a 360-degree view of data and AI. For instance, it provides answers to questions such as which data is sent to streaming applications or AI models, which identities or roles access it, what the security configurations are, and the compliance posture of the data.
Toxic Combinations of Risks
Imagine having a publicly exposed bucket, sensitive data identified by a classification tool, and a newly deployed AI model detected by an AI security tool. Viewing all these scenarios individually might appear as isolated, low risks. However, when these individual findings are combined, they highlight that an AI model has access to a publicly exposed bucket containing sensitive data. This creates a high-risk scenario where an external attacker could poison the data, and an AI model may use sensitive data without adequate security controls and entitlements.
This is called a toxic combination of risks. Security tools used in silos fail to deliver these rich insights into toxic combos since each tool generates findings within fragmented context. Consequently, security teams are rendered blind, unable to piece together benign alerts that could reveal critical security threats.
A robust DSPM solution with a centralized knowledge graph can correlate diverse metadata attributes, enabling security teams to prioritize the detection of toxic risk combinations. Teams can create custom risk rules by considering the business context and application requirements. This powerful capability of DSPM significantly improves the accuracy of risk identification, thereby reducing the number of false positives.
Security Posture Management
Security teams are often overwhelmed with cloud misconfiguration alerts. Misconfigurations could range from exposed storage buckets and overly permissive IAM roles to unencrypted databases across hybrid and SaaS environments. Without a sensitive data context, it’s not easy for security teams to prioritize these alerts, leading to alert fatigue and delayed responses. Traditional CSPM tools will flag thousands of issues, but they can’t tell you which ones are exposing sensitive data. This is a pressing issue, as misconfigurations account for 15% of the initial attack vectors in data breaches.
DSPM solves this by adding data context to security posture management. The solution continuously scans cloud and SaaS configurations, classifies findings by severity, and shows where sensitive or regulated data could be at risk. Custom policies also allow for the enforcement of best practices, providing real-time alerts on any violations. With ongoing monitoring of assets and configuration changes, DSPM enables proactive risk reduction, allowing security teams to effectively prioritize sensitive data risks and harden their cloud data security posture.
Related: How DSPM Reduces the Risk of Data Breaches
Data Access Intelligence & Controls
Internal teams, partners, external systems, and large language models (LLMs) all need access to unlock the innate value of data. Still, this access must be managed carefully to avoid exposing personal or sensitive data. However, it is easier said than done. A 2024 survey reports that 57% of organizations cite excessive access to data, resulting from overprivileged accounts, as one of the main data security challenges.
Data access governance starts with having a comprehensive view of sensitive data access. However, several obstacles hinder visibility, making it difficult for governance teams to establish robust access policies and controls. For instance, data silos and fragmentation, shadow IT, unstructured data, complex permission models, and siloed IAM systems make it significantly challenging to gain a centralized view of what users, roles, or identities have access to data. When teams lack visibility of sensitive data access, analyzing risky access patterns and establishing access policies accordingly becomes difficult.
To enforce robust access controls, DSPM solutions provide comprehensive visibility into who has access to data (structured and unstructured) and who is accessing it. Teams can use these insights to detect access risks and enforce robust access controls. DSPM also provides fine-grained, policy-based entitlements across structured and unstructured data. At the technical level, policies can be defined at the table, view, row, and column levels, allowing for highly precise privilege configurations, such as SELECT, MODIFY, or both. The dynamic column masking obfuscates sensitive fields, such as PII or financial data, in real-time, based on role or context, without blocking broader access to data for business use.
By combining access visibility, activity analysis, fine-grained enforcement, and privilege controls, DSPM solutions empower governance teams to manage data access with high precision. Consequently, organizations are better able to prevent sensitive data from being leaked or exposed to unauthorized users.
Data Flow Intelligence & Governance
Data isn’t limited to traditional systems and processing. In fact, it moves faster than ever due to near real-time processing through streaming environments like Kafka. The rapid generation of data and the complexity of the streaming environments create security and governance challenges for organizations, such as data sprawl, sensitive data exposure in Schemas or Topics, and overprivileged entitlements.
The DSPM solutions help organizations automate data maps to understand how data moves across systems and applications, is transformed, and interacts within environments. The solution does so through explicit and inferred lineage tracking, which leverages traditional methods, such as SQL parsing or dbt integration, as well as AI-powered techniques like data characteristic analysis, to track movement patterns and build relationships.
This comprehensive, structured and unstructured data lineage can help teams monitor how the data is accessed, used, changed, or transformed throughout its lifecycle. Teams can use these comprehensive insights to identify gaps in the security and privacy aspects of the data, such as data duplication or potential vulnerabilities, and apply robust policies or controls.
ROT Data Minimization
Studies report that dark or ROT (redundant, obsolete, and trivial) data makes up one-third of an organization's data landscape. This type of data, although often forgotten, accumulates in an organization’s environment over time, under the assumption that it may be useful in the future. This exposes the organization to various security, regulatory, operational, and financial risks. With genAI to the mix, ROT data can significantly hamper the efficacy of LLMs when it is introduced during the training or fine-tuning phase.
A modern DSPM solution leverages policy-driven frameworks to help security and data teams overcome the risk of ROT data buildup. The solution helps create a comprehensive data catalog, labeling files based on signals like retention age, business context, or activity levels. The solution also leverages advanced techniques, such as cluster analysis, to identify duplicate or near-duplicate data. With accurate classification, DSPM further highlights the data that might be violating any regulatory law or security standard.
AI Security & Governance
As data moves through various stages of AI development, it is exposed to multiple risks, including the exposure of sensitive data, oversharing, poor-quality training data, and excessive bias. The Lack of appropriate data and AI controls can significantly hamper the adoption of AI.
Modern DSPM solutions go beyond traditional data protection, offering robust AI security and governance capabilities as additional features. A DSPM solution with a built-in AI security component can help scan the environment to discover cloud-native and shadow AI models and AI Agents. It also provides context around data and AI interaction, and highlights risks such as hallucinations, exposure of sensitive data, model bias, and risky access permissions.
Some DSPM solutions come with advanced LLM firewall capabilities that filter AI inputs/outputs at various levels of interaction, including prompts, responses, and retrievals. These capabilities help secure Gen AI pipelines by filtering misinformation, malicious prompts, or PII phishing attacks.
Read more: Top 5 DSPM Use Cases for Optimal Data Security.
Compliance Automation
Maintaining compliance in today’s regulatory landscape is a growing challenge, especially as frameworks like GDPR, the EU AI Act, and NIST AI RMF evolve rapidly. Many organizations still rely on spreadsheets, manual processes, and siloed systems, making cross-framework reporting time-consuming and prone to errors. With region-specific requirements constantly shifting, staying compliant becomes a complex, manual burden that hinders agility and increases the risk of non-compliance, inefficiencies, and reputational damage.
A modern DSPM solution simplifies compliance through automated, end-to-end workflows. It centralizes compliance tracking, testing, and reporting across global frameworks, using pre-built controls mapped to key regulations. Features like automated compliance reporting, real-time monitoring, and Human-in-the-Loop attestation streamline evidence collection and validation. DSPM also factors in cross-border mandates, ensuring data sovereignty compliance at scale. By unifying these efforts, organizations can reduce risk, lower overhead, and transform compliance from a reactive checkbox exercise into a proactive, business-enabling function.
Managing vulnerabilities becomes more challenging as businesses expand across multiple cloud and SaaS platforms. An increasing amount of sensitive data is at risk, and security teams deal with dispersed data and inconsistent configurations. Manual remediation of misconfigurations, such as an unencrypted data store or overly privileged access permissions, not only drains resources but can also introduce errors or disrupt critical services. In high-stakes environments, automation must be precise, controlled, and aligned with business and compliance requirements.
A modern DSPM solution streamlines remediation by combining automated responses with policy-based controls. The solution detects posture gaps across environments, prioritizes vulnerabilities by severity and impact, and applies automated fixes where safe, such as securing network ports or fixing access permissions. For higher-risk issues, DSPM integrates with tools like ServiceNow or Jira to trigger alerts and review workflows. This hybrid approach ensures efficient remediation without compromising stability, reduces risk exposure, and frees teams to focus on strategic security initiatives.
Seamless Integration with Enterprise Stack
DSPM solutions do not operate in silos, rather they are integrated into existing enterprise security stacks such as SIEM, CNAPP, CSPM tools. Interconnectivity and communication with other tools are important for a unified context and the insights needed to act on critical alerts, perform proactive risk remediation, and conduct incident investigations. For instance, consider a publicly exposed EC2 instance that has admin access to an S3 bucket containing sensitive data. This creates a toxic combination of risks that cannot be effectively identified or mitigated without integration between DSPM and the Cloud-Native Application Protection Platform (CNAPP).
A robust DSPM solution offers seamless integration with a wide range of security tech stacks, such as SIEM tools, through APIs. This helps enterprises streamline their existing SecOps workflows, centralize monitoring and analysis, and enhance the efficacy of their security tools.
Breach Management
Data breach incidents tend to throw organizations into turmoil. It can be baffling and challenging for organizations to answer the myriad of questions arising from the breach. For instance, determining the scope of the breach, the volume of data impacted, identities affected, necessary remediation measures, mandatory notifications, and incident response report.
Organizations can seamlessly overcome these challenges with DSPM. Integrated with breach response management capabilities, DSPM solutions can help identify sensitive data across the environment, map the data to individuals, and determine data breach notification requirements based on residency. The solution can also automate remediation measures to respond to the breach immediately. For instance, encrypting exposed buckets, masking sensitive data, restricting access entitlements, and resolving misconfiguration issues.
Related: How DSPM Reduces the Risk of Data Breaches
Best Practices to Consider When Deploying DSPM
A structured approach to deploying DSPM is needed to ensure seamless implementation and security. The following are some critical steps enterprises may consider for effective deployment.
Things to Take Care of Pre-DSPM Deployment
- Determine Your Business Objectives First: You need to align business, IT, compliance, governance, security, and privacy teams on the objectives you want to achieve with DSPM implementation. By having a clear objective in view, you can better evaluate the DSPM capabilities your organization needs.
- Opt for a Solution That Meets Your Specific Business Needs: With a clear objective in mind, evaluate the solution based on the business requirements for data security. For instance, the most critical aspects of a solution to consider include support for automated controls, specific regulations, the ability to scale or customize, and the breadth of capabilities it offers compared to your unique business needs.
- Fine-tune Your DSPM Solution to Your Data Environment: Set up your DSPM technology according to your organization’s specific requirements and data environment. This involves configuring data discovery and classification processes, defining policies, setting up access controls, and defining monitoring parameters. Once deployed, initiate continuous monitoring to gather insights into your security posture and proactively remediate data+AI security risks..
- Integrate DSPM into Your Existing Security Stack: Integrate the tool into your existing security stack, which may include SIEM, IAM, and CNAPP, to optimize the efficacy of your existing security solutions.
To realize the full potential of a DSPM solution, it needs to be integrated with the existing enterprise security stack. Modern DSPM solutions are able to work effectively with various security tools to improve an organization’s data security posture. :
- IAM: DSPM can leverage identity and access management (IAM) integration to understand identity attributes, such as user job role, location, and departments, to help define scalable attribute-based access controls.
- DLP: Traditional DLP solutions rely on regex-based classification, which is not accurate. By integrating with DSPM solutions, DLP can classify data with high accuracy to prevent sensitive data exfiltration and reduce false positives.
- SIEM: DSPM's contextual data intelligence can greatly enhance security information and event management’s (SIEM) ability to prioritize threat detection and response based on sensitive data. The integration between the two solutions can further allow for a better view of a data risk posture and incident forensics.
- CASB: Cloud access security brokers (CASB) provide visibility and control access to cloud infrastructure. DSPM offers insights into the data within the cloud data stores, such as data sensitivity and usage.
- IDPS: DSPM’s integration with tools like intrusion detection and prevention systems (IDPS) can greatly help with improved alert context, reduce false positives with precise threat detection involving sensitive data, and enable data-centric threat detection.
What is the difference between DSPM and CSPM?
Cloud security posture management (CSPM) and data security posture management (DSPM) are two integral parts of data security. Although the terms CSPM and DSPM may sound similar, they offer different yet interconnected capabilities.
The scope of CSPM is focused on identifying and remediating misconfigurations, vulnerabilities, and compliance violations in cloud infrastructure (such as virtual machines or containers). The solution scans cloud infrastructure against industry best practices and security frameworks like NIST, CIS, GDPR, and PCI DSS to find gaps and proactively remediate risks.
For example, if an Amazon S3 bucket is publicly accessible due to a misconfiguration, a CSPM tool will always flag it and generate a high-severity alert. However, if that S3 bucket contains non-sensitive data, such as marketing images for a website’s front end, then making the data publicly accessible is a pretty normal behavior. Due to their lack of context around data, CSPM solutions can generate false positives, diverting security teams’ attention toward issues that have little to no risks..
DSPM complements CSPM with deep contextual intelligence about an organization’s data landscape spread across multi-clouds and SaaS applications. DSPM takes a “data-first” approach by prioritizing the discovery of sensitive data to identify potential data security and compliance risks.
In the example above, DSPM provides the data context to flag and generate a high-risk alert if the S3 bucket contains sensitive data, such as customer PII, that needs to be protected according to the company's security policy. Besides identifying and auto-remediating security misconfiguration risks, it also helps establish data access control policies. Organizations can streamline their security, governance, and compliance functions by gaining deep visibility into sensitive data and implementing data and AI controls at scale.
What is the Difference Between DSPM and DLP?
DLPs are good at detecting data being extracted from an environment. However, they are not good at data classification. DLP solutions use regex-based classification and generate classification labels that may not be accurate, leading to false alarms about stolen data. The solutions can integrate with DLP to provide more accurate, AI-based classification, helping organizations get the most out of DLP.
Related: Why DSPM is Critical: Key Differences from DLP & CNAPP
Mistakes to Avoid When Implementing DSPM
Below are five common mistakes that organizations should avoid in order to realize the full potential of a DSPM solution.
- Lack of stakeholder buy-in and collaboration between data teams.
- Inconsistent data classification across platforms.
- Depending solely on data classification and ignoring the broader data context.
- Increasing alert fatigue due to overwhelming false positives.
- Manually fixing issues instead of automated remediation.
To learn more about these mistakes and get actionable tips to avoid these pitfalls, read our detailed blog.
How Securiti Can Help?
Securiti’s Data+AI Command Center (rated #1 DSPM by GigaOM) delivers a unified approach to Data and AI security. It empowers organizations with comprehensive visibility and control across multicloud & SaaS environments by integrating advanced discovery, classification, contextual intelligence, data lineage, and access governance. Also, by providing deep intelligence and enforcing precise data + AI controls, the platform enables security teams to proactively remediate risks and strengthen their overall data security posture.
Schedule a demo to learn how Securiti addresses your organization’s unique data security, privacy, and governance needs with a unified Data + AI Command Center.
