Mistake #1: Lack of Buy-In and Collaboration Between Data Teams
The Challenge: Siloed DSPM Efforts
DSPM projects often get siloed within security teams, leading to poor collaboration with key departments like data governance, compliance, and privacy. This approach overlooks the broader business impact of data use. Security teams may myopically focus on the technology, thinking, “This tool looks great; let’s implement it,” without considering how it aligns with the business’s data use goals or involving other stakeholders critical to the project’s success.
The Risk: Incomplete Risk Coverage and Poor Adoption
Without input from all relevant stakeholders, DSPM initiatives fail to address the full scope of data risks. This oversight leads to a disjointed security posture that misses critical vulnerabilities as well as over-restrictive controls that hinder data use. Comprehensive buy-in is essential; without it, employees may resist new processes, undermining the project’s success and exposing the organization to data breaches and compliance failures.
Actionable Tip: Foster Cross-Functional Collaboration
Secure buy-in from all stakeholders, including business units that own the data. Establish cross-functional teams to align DSPM with broader business objectives, making your data security efforts more comprehensive and effective.
Mistake #2: Classifying Data Differently in Different Environments
Every tool has its unique language for data classification. For example, one tool may tag emails as "Email," while another labels them as "Email ID." This inconsistency complicates security management across environments like on-premises, cloud, and SaaS platforms, making it difficult to consistently assess overall data risk and automate security controls.
The Risk: Increased Security Gaps and Compliance Issues
Inconsistent data classification makes operationalizing data controls difficult, which can result in data exposure and compliance failures. Without standardized classification, enforcing consistent data policies across the organization becomes nearly impossible, leaving critical gaps in your security posture.
Actionable Tip: Standardize Classification Frameworks
Implement a unified data classification framework that applies across all environments and regularly audit practices to maintain consistency. Ensure DSPM supports all environments, including public cloud, private cloud, SaaS, cloud data lakes and warehouses, streaming data and more.
Mistake #3: Not Thinking Beyond Data Classification
The Challenge: Focusing Solely on Classification
A common misconception is that DSPM is just about classifying data—knowing if it is PII, financial, or healthcare data. While classification is essential, it is only the beginning. Without understanding the broader context—how data is used, accessed, and protected throughout its lifecycle—the value of DSPM is significantly limited.
The Risk: Limited Insight into Data Risk and Usage
Classification alone does not reveal how data may be used, stored, or accessible in ways that increase data risk. This narrow focus can lead to poor security posture, non-compliance, and inefficient responses to security incidents. Data without context is just noise; understanding how data interacts within your systems is key to identifying real risks.
Actionable Tip: Strengthen DSPM with Contextual Intelligence
To effectively manage your security posture, you need more than just classification—you need full contextual data intelligence, including data’s usage by AI. By leveraging a knowledge graph, you can understand data from a variety of perspectives, including user entitlements, configuration posture, applicable regulations, data transfers, processes and more, turning raw metadata into actionable insights. This approach enables you to pinpoint real risks, streamline incident response, and ensure your data security posture is both proactive and resilient. For deeper insights, explore our whitepaper on the power of contextual data+AI intelligence.
Mistake #4: Not Testing for False Positives at Scale
The Challenge: Overwhelming Alerts and False Positives
Many organizations fail to test DSPM systems for false positives at scale, overwhelming security teams with excessive alerts. This leads to alert fatigue, where critical notifications are ignored, increasing the risk of missing genuine threats.
The Risk: Reduced Security Effectiveness and Team Burnout
Constant false positives undermine your security posture and can demoralize your teams. You risk missing significant security incidents if critical alerts are ignored due to fatigue. This not only weakens your security but also wastes valuable resources.
Actionable Tip: Use AI-based Classification to Minimize False Positives and Negatives.
Fine-tune your DSPM system to the sensitivity of different data types. For highly sensitive data, such as bank account details, prioritize minimizing false negatives. Regularly test and calibrate your system to ensure accuracy and that alerts are actionable.
The Challenge: Manual and Inefficient Response Processes
Implementing DSPM solely as a monitoring tool without automated response capabilities is insufficient. Manual remediation is slow, error-prone, and reactive. Without automation, security teams struggle to keep pace with threats, leaving vulnerabilities unaddressed for longer periods.
The Risk: Slow Responses and Unaddressed Vulnerabilities
Organizations relying on manual responses are often too slow, increasing the window of opportunity for attackers. This reactive approach can result in costly breaches and regulatory fines that could have been avoided.
Choose DSPM solutions with automated response capabilities, such as adjusting access controls, fixing misconfiguration, or quarantining files. Automation helps reduce manual workload and ensures faster, more reliable responses to security risks.
Key Takeaways: Best Practices for DSPM Success
- Secure Comprehensive Buy-In: Engage all stakeholders, including business units and data owners, to support DSPM efforts and drive data security best practices.
- Standardize Classification Across All Environments: Ensure your DSPM supports consistent classification across public cloud, private cloud, SaaS, data lakes, and more.
- Incorporate Data Context: Leverage a knowledge graph to enhance data understanding beyond simple classification, helping identify real risks.
- Refine Alerts with AI-Based Classification: Use AI to classify data and even fine-tune classification algorithms for specific data sensitivities, minimizing false positives and negatives.
- Automate Remediation and Workflow Orchestration: Integrate automation into your DSPM strategy to streamline response actions and enhance overall security effectiveness.
Conclusion: Building a Resilient DSPM Strategy
DSPM is a powerful tool for protecting your organization’s sensitive data, but its success hinges on thoughtful implementation. Treat DSPM as a business project that involves multiple stakeholders across the organization. By fostering collaboration, standardizing practices, understanding data in context, refining your alert system, and automating responses, you can strengthen your data security posture and effectively safeguard your organization.
At Securiti, we take a unique approach to help enterprises protect sensitive data across hybrid multi-clouds and SaaS applications. We enable this with a platform that includes a DSPM solution built into a broader Data+AI Command Center. Independent analysts and customers alike have validated our approach to data and AI security. Securiti has been rated the number one DSPM solution based on customer reviews on Gartner Peer Insights and by GigaOm for our unified platform and comprehensive data and AI security capabilities.
