DSPM vs DLP, and How They Help Against Insider Risk Management

If you’ve been looking for methods of securing your organization’s data, you must have heard the terms DSPM and DLP. While different in some respects, both serve an important purpose in data protection.

In this post, we’ll discuss what the two are, how they’re similar, and what sets them apart.

Let the DSPM vs DLP battle begin.

Understand the Differences Between DSPM and DLP

To understand the difference between DSPM and DLP, we must first understand what these terms mean.

What Is DSPM?

DSPM, which stands for data security posture management, is both a security framework and a process. It offers a holistic approach to keeping sensitive data safe through the following processes:

• Data mapping: Finding out what data you have and where it’s located
• Risk assessment: Categorizing data based on its sensitivity and risk levels to determine what level of protection it needs
• Access control: Determining who needs access to what data and the methods to enforce this
• Automated remediation: Implementing a pre-programmed security protocol when a security threat is detected

In short, DSPM provides greater visibility of your data and allows you to better manage and protect it. The framework often uses AI and automation to assess the risk to your information and recommends controls that strengthen your data security posture across all your environments, including the cloud, SaaS services, on-premise, and hybrid.

What is DLP?

Data loss prevention (DLP), as the name suggests, focuses on preventing the unauthorized disclosure of sensitive data. Its core components include:

• Sensitive data discovery: Identifying sensitive data stored within your organization
• Data classification: Classifying it according to sensitivity and risk
• Data protection: Implementing measures like redaction, masking, alerting, and deleting to keep sensitive data safe
• Data encryption and blocking: Ensuring only authorized people can view sensitive data when data transmission is not in line with your organization’s security policies

In short, DLP is the term for the combined processes and tools that secure sensitive data from breaches and leaks while providing visibility and control. It also helps your organization comply with local data security and privacy laws.

While DSPM helps you with your overall data governance and protection strategy, DLP specifically focuses on enforcing rules that keep data safe.

The Similarities Between DSPM and DLP

While both DSPM and DLP have different approaches, their goal is the same: to protect sensitive data. As such, they are quite similar in many ways, as they both:

• Start with sensitive data discovery within the organization.
• Classify and prioritize data based on its sensitivity.
• Aim to mitigate risk to sensitive data and prevent exposure or misuse.
• Rely on your organization’s data protection policies to guide their operations.
• Help your business comply with data security and privacy regulations, like the GDPR, CCPA, HIPAA, or PCI DSS.
• Integrate with other tools to strengthen your organization’s data security.

However, as we’ve seen, the way they operate is different. Let’s take a look at how they differ.

DSPM vs DLP: The Differences

Focus and Purpose

DSPM focuses on strengthening the overall security posture of your organization’s sensitive data. It can identify vulnerabilities that can be exploited, such as misconfigurations in your systems, excessive access permissions, or unencrypted data. Then, it offers recommendations to help you remediate these weaknesses.

DLP, on the other hand, enforces your business policies to prevent sensitive data from being leaked, shared, or accessed by those who aren’t authorized.

Scope of Operations and Risk Assessment

While both DSPM and DLP assess risk, the former focuses on the broader security posture.

As part of its holistic approach, DSPM looks at data across your entire ecosystem. That includes cloud storage, SaaS platforms, and databases, and it even scans emerging technologies like generative AI tools. It looks for gaps like access control, encryption, and policy enforcement.

However, DLP helps organizations protect data by preventing unauthorized access. It focuses on data in transit—emails, file transfers—or in use—in applications, for example. It can also look at data at rest in specific environments, but it has limited capabilities here.

Differences in Approach

DSPM follows a proactive approach to data security, identifying underlying risks and vulnerabilities before they become a problem. It offers a strategic framework to prevent security incidents, breaches, and leaks by mitigating the root causes.

DLP takes a more reactive approach. Its tools generally enforce policies in real time when specific actions occur. For example, if an employee is sending an email that includes sensitive data, a DLP solution might block it or redact the information.

However, it doesn’t address any underlying governance or systemic risks that might lead to the incident occurring in the first place.

DSPM offers a strategic focus, where it looks at the long-term impact of data security risks. DLP has a more real-time focus. It’s triggered when certain actions take place, and it reacts to prevent specific violations or incidents.

Data Discovery, Visibility, and Contextual Insights

DSPM solutions offer deep and continuous data discovery across diverse environments across your organization. It gives you comprehensive visibility into where your sensitive data lives and how it’s stored and interacts with different applications and systems.

In short, it also provides context, including information like who has access, its usage, whether it’s encrypted or not, and if it’s at risk due to misconfigurations or over-permissive access.

While DLP does have data discovery capabilities, they’re usually limited. It generally only looks at specific repositories or specific types of sensitive data. It also doesn’t offer any contextual insights into data risks.

Policy Enforcement and Risk Remediation

DLP tools will actively enforce policies set by your organization, in real time. They’ll block attempts to access or transmit sensitive data or redact it. They may also alert the policy enforcers, but they won’t help you create policies.

DSPM solutions don’t enforce policies. They do, however, point out weaknesses in security and ways to mitigate them. In short, they give you actionable insights into how you can make your data security policies—and posture—stronger.

Access Control Management

DSPM tool tells you who has access to sensitive data, if they need this access, and if the access settings are overly permissive. Since it offers a more strategic user experience, security architects and governance teams find it especially useful.

In contrast, DLP doesn’t analyze access permissions. What it does do is prevent unauthorized use or sharing of data based on the rules you’ve predefined. That’s why it’s generally used by IT and security operations teams who need to manage day-to-day enforcement of the policy configurations.

Approach to Compliance

Both DSPM and DLP help you stay compliant with regulations. The difference is in how they do it.

DSPM audits your sensitive data storage and management systems, access permissions, and system configurations, while DLP prevents data from leaving the confines of your organization’s environment or being misused in a way that violates regulations.

Integration With Broader Security Tools

We mentioned that both DSPM and DLP integrate with other security tools. However, where they differ is the type of tools with which you can integrate them. DSPM works well with broader governance frameworks, like security information and event management (SIEM), cloud security posture management (CSPM), and identity and access management (IAM) systems.

On the other hand, DLP can integrate with endpoint protection tools, email gateways, and other operational security tools for comprehensive security.

Adaptability to Modern Data Environments

DLP was originally designed for static systems that were located on-premise, which is in contrast with modern, dynamic operations. While modern DLP does offer extended functionality that supports cloud ecosystems—including SaaS tools and hybrid environments—it generally requires a great deal of customization to do so.

DLP is also somewhat unsuitable for new technologies like GenAI. It lacks the agility to cope with the unstructured sensitive data these systems create and process.

In contrast, DSPM was built for modern, dynamic data environments. It scales seamlessly across multi-cloud, hybrid, and SaaS infrastructures, handling decentralized and constantly evolving ecosystems with ease.

It is also capable of handling the challenges posed by GenAI. DSPM solutions identify the sensitive data created and processed by these systems and inform you of their security risks.

DSPM, DLP, and Insider Risk Management

According to the 2024 Insider Threat Report, the number of organizations that have reported insider attacks has increased from 66% in 2019 to 76% in 2024.

Both DSPM and DLP play an important role in managing insider risk management. However, before we delve into how they do so, let’s first take a look at what insider risk management is.

What Is Insider Risk Management?

As the name suggests, insider risk is any threat to the security and integrity of your sensitive data, intellectual property, or operational integrity that comes from within your organization. This threat isn’t always malicious—it can also be negligent or accidental.

Insider risk management (IRM) is the process of identifying and mitigating threats of data exfiltration and loss through insiders, including employees, contractors, or partners; in short, anyone with access to the company’s critical systems and information.

Part of IRM includes identifying behavior that could be risky and educating staff about it, as well as implementing controls to prevent data breaches and leaks.

Types of insider risks include:

• Negligent insiders: These are employees who don’t follow, or are lax about following, security protocols. They might leave unsecured laptops unattended, use weak passwords, or are careless about who they send sensitive information to. They aren’t malicious but don’t realize how their actions could have serious consequences.
• Malicious insiders: These actors intentionally steal information or sabotage systems for personal gain and can be dangerous, as they have legitimate access to data and applications.
• Compromised insiders: These insiders are those whose accounts or login credentials have been stolen, allowing bad actors to gain access to systems and information.
• Disgruntled employees: Any staff member who feels dissatisfied with the organization or holds a grudge against the company or a senior might want to seek revenge. They could leak data or cause damage to systems and applications in retaliation for being undervalued or treated unfairly. The motivation here is not financial but retaliatory.
• Privileged users: Such employees tend to have the highest access levels, which makes them especially at risk if they’re negligent, compromised, or malicious.

Examples of IRM include:

• Sensitive data leaks and spillage
• Violation of confidentiality
• Intellectual property (IP) theft
• Fraud
• Insider trading
• Regulatory compliance violations

DSPM and DLP for Insider Risk Management

How DSPM Helps With IRM

Proactive risk analysis and behavioral analysis: DSPM identifies all potential risks to sensitive information that insider threats could exploit, intentionally or unintentionally. It also monitors and analyzes user behavior and activity patterns to identify potentially anomalous access instances which could become opportunities for breaches.

Access governance and policy development

With DSPM, you get detailed insights on who has access to sensitive data and if they have a legitimate need for it. DSPM also flags any excessive permissions per the company’s policies, as those can lead to deliberate or inadvertent data leaks.

Comprehensive data visibility and documentation

DSPM solutions grant visibility across your data environment to help you understand where your sensitive data resides, how it’s stored, and who can interact with it. These solutions are particularly helpful in helping you find your shadow data, which is often a security risk since you can’t govern what you don’t know you have.

Data risk contextualization

In addition to visibility, DSPM also gives you insights into the context of data risks for a truly layered view. This allows you to prioritize your mitigation efforts as per the severity of the risk.

For example, if a dataset is accessible to employees who don’t need it—which goes against the principle of least privilege—and it’s also not encrypted, it’s more at risk than one that is encrypted, even if there are users with excessive permissions accessing it.

Cross-functional collaboration

Insider risk often involves multiple stakeholders, and all of them must be aligned to manage it. DSPM, with its data visibility and shared insights, promotes collaboration between the various departments, including IT, HR, and legal and compliance, for a more cohesive approach.

Sensitive data misuse in emerging technologies

DSPM can be especially useful for insider risk management within systems that use emerging technologies, such as SaaS platforms, cloud and multi-cloud environments, and GenAI. It identifies how data flows within such systems and responds appropriately if it detects misuse or unauthorized access by insiders.

Integration with enterprise risk management

DSPM works well with other, broader security frameworks and governance practices to give you a more resilient insider risk management strategy. This allows it to complement your enterprise risk management initiatives.

How DLP Helps With IRM

Real-time policy enforcement: Once you’ve defined the rules with the help of DSPM, DLP helps you enforce them. It will block activities such as emailing sensitive documents to a personal email address, uploading data to an unauthorized platform (such as Dropbox or Google Drive), or printing restricted documents.

Immediate threat detection and response

DLP monitors systems, networks, and applications in real time. If it detects any signs of insider threats, it immediately blocks or redacts sensitive information being moved. This keeps insider activities contained and mitigated to prevent potential data leaks or breaches.

Behavior monitoring and incident response

A large-volume data transfer can be a sign of suspicious activity, as can an unauthorized attempt to get into a restricted system. DLP monitors for any such suspicious activity and sends out an alert upon detection. It may also carry out preventative actions to address such behaviors for a quicker incident response time.

Data masking and redaction

Relying on the DLP system to respond to a data leak attempt, while useful, is risky because not all risks are obvious. To prevent a security incident in such a case, DLP helps you mask and redact sensitive information in documents, emails, and reports shared by insiders. As a result, even if it does fall into the wrong hands, the actual exposure of data is minimized.

Employee risk profiling

Certain employees pose a greater risk than others. It might be because they have more privileges, are more likely to be negligent, or have reason to be unhappy with the organization. Modern DLP systems can leverage analytics and behavior patterns to create risk profiles for each employee. This allows you to monitor high-risk individuals more closely.

Ensure Data Security With BigID Data Security Solution

You must have noticed the trend in this post—DSPM addresses strategy while DLP helps with execution and enforcement. Obviously, if you want a comprehensive data security program, you need both of these weapons in your arsenal.

Fortunately, BigID can help—the platform is known for its DSPM capabilities, but did you know it can also help you with an outcome-based DLP approach?

Learn more about how BigID enables comprehensive data security with DSPM and DLP. Schedule a 1:1 demo