Why DSPM Falls Short and How Risk Leaders Can Mitigate Security Gaps

Data Security Posture Management (DSPM) refers to the processes and tools that provide organizations visibility into their data assets, monitor risks, and help maintain regulatory compliance across multi-cloud, hybrid, and on-premises environments. DSPM gained prominence as cloud adoption surged and regulatory scrutiny intensified, promising to uncover shadow data, map access, and streamline controls. As summarized in a widely cited Gartner perspective, DSPM helps organizations locate unknown sensitive data and understand risk context across sprawling estates, but it is not a standalone solution for data protection or compliance excellence (see the Gartner DSPM overview).

In this post, we’ll explore where DSPM commonly falls short—spanning integration hurdles, data classification challenges, alert fatigue, and more—and offers practical steps risk leaders can take to close those gaps.

Executive Summary

Main idea: DSPM improves discovery and risk context but falls short on integration, classification precision, automation, and real-time coverage. Risk leaders should pair DSPM with interoperable controls, continuous discovery and monitoring, policy-as-code automation, and unified data governance to close protection and compliance gaps.

Why you should care: Unaddressed DSPM gaps raise breach likelihood, regulatory exposure, costs, and remediation times. Closing them reduces alert fatigue, accelerates audits, strengthens real-time response, and improves risk posture across multi-cloud, SaaS, and legacy environments—protecting business value and trust.

Key Takeaways

1. DSPM is necessary but not sufficient. It discovers and contextualizes sensitive data but leaves protection, response, and compliance gaps that require complementary controls, automation, and process change.

2. Interoperability drives risk reduction. Standards-based integrations with IAM, SIEM, DLP, and data catalogs prevent silos, accelerate time-to-value, and reduce vendor lock-in.

3. Precision classification and automation cut noise. Business-context classifiers and policy-as-code remediation reduce false positives, shorten dwell time, and strengthen audit readiness.

4. Continuously monitor dynamic, hybrid environments. Cover ephemeral containers, SaaS, multi-cloud, and legacy using agentless-plus-agent approaches and streaming analytics for near-real-time detection and response.

5. Governance and unified control planes amplify DSPM. Cross-functional ownership, metrics, and a Private Data Network unifying secure exchange, logging, and enforcement help close gaps at data movement boundaries.

Common Limitations of DSPM Technology

Understanding DSPM limitations is essential for CISOs and security leaders responsible for reducing risk without adding operational friction. The most prevalent DSPM limitations include integration issues across diverse environments, data classification challenges, limited automation for remediation, gaps in monitoring ephemeral data, cost and skill constraints, insufficient real-time response, blind spots in multi-cloud, and alert fatigue. Each one can create protection, compliance, or efficiency gaps—demanding compensating strategies and complementary controls.

Integration Challenges Across Diverse Environments

DSPM must mesh with Identity and Access Management (IAM), Data Loss Prevention (DLP), and SIEM tools across heterogeneous stacks, which introduces deployment complexity and fragile workflows. Market analyses note that stitching DSPM into existing pipelines and governance models remains a major barrier to time-to-value (see the DSPM solutions market analysis). When DSPM integrations are partial, incident response becomes fragmented; data policies drift; and ownership blurs across cloud, SaaS, and on-prem teams, leading to silos and slow, inconsistent remediation (as documented among common DSPM challenges). Vendor lock-in exacerbates these problems, making it hard to swap tools or normalize telemetry across platforms; Gartner’s guidance emphasizes standards-based, interoperable designs to avoid this trap (see Gartner Market Guide insights).

Integration challenges by environment

Difficulties in Accurate Data Classification

Data classification is the process of categorizing data based on its sensitivity, business value, and regulatory requirements to determine appropriate handling and protection mechanisms, such as access controls, encryption, retention, and monitoring. In practice, classification engines struggle without business context; false positives and false negatives proliferate; and sensitive data is mislabeled or missed, creating operational overhead and residual risk (see common DSPM challenges and DSPM mistakes and fixes).

Operational impacts of misclassification:

• Alert fatigue that desensitizes analysts

• Regulatory violations due to missed sensitive records

• Data leaks from unprotected, high-value information

Limited Automation for Risk Remediation

Remediation automation uses technology to apply predefined actions or workflows when security risks are detected, reducing manual effort and response time. Many DSPM tools still lack robust auto-remediation or guided workflows, resulting in slow, error-prone manual fixes after issues are detected (as reported in common DSPM challenges). The result: longer exposure windows, audit uncertainty, and a higher likelihood of repeat findings—making automation an imperative for both security and compliance outcomes.

Challenges with Monitoring Dynamic and Ephemeral Data

Ephemeral data refers to information stored temporarily in environments like containers or serverless functions, which may exist for only seconds or minutes and can evade traditional security scanning. In cloud-native architectures, sensitive data can be created and destroyed within a single deployment cycle; DSPM tools often miss these short-lived assets, leaving gaps (see common DSPM challenges).

A typical containerized data lifecycle and where visibility is lost:

1. Build: Images pull base layers; secrets or sample data sometimes slip into images.

2. Deploy: Containers start with environment variables, temp volumes, or init scripts.

3. Run: Services generate logs, caches, and in-memory or scratch data.

4. Scale: Autoscaling creates additional replicas with similar transient data.

5. Terminate: Containers exit, leaving ephemeral storage that may persist briefly.

6. Recycle: Images are updated; artifacts move through registries. Common visibility losses occur at steps 3–5, where transient volumes and short-lived logs are not continuously scanned.

Cost, Resource, and Skill Constraints

DSPM deployments require budget, talent, and organizational change. Costs, skill gaps, and cultural resistance often slow adoption and complicate rollout across IT and business units (see Gartner Market Guide insights). Typical hidden costs include:

• Specialist training and tuning of classification

• Integration development and maintenance

• Ongoing policy governance and change management

• Cloud egress, scanning, and storage overhead

• Program management for cross-functional alignment

Insufficient Real-Time Detection and Response

Real-time detection refers to the immediate identification of security risks as they occur, allowing for instant assessment and response. Many DSPM tools analyze on schedules or with batch scans, limiting near-real-time detection and extending dwell time. Without streaming analytics and automated enforcement, remediation is delayed and risk windows lengthen—diminishing DSPM’s value as a frontline control (as observed by Trend Micro on DSPM).

Illustrative comparison of response modes:

• Manual triage: periodic scans, human validation, ticket-driven fixes → minutes to days

• Automated workflows: streaming events, policy-as-code actions, closed-loop enforcement → seconds to minutes

Blind Spots in Multi-Cloud and Hybrid Architectures

DSPM emphasizes discovery and cataloging of shadow data, but unmanaged assets—like public buckets, service accounts, or unsanctioned SaaS apps—are routinely missed, especially across multi-cloud and hybrid estates (see what is DSPM and Trend Micro on DSPM). Common blind spots include:

• Unmanaged object stores with public or misconfigured ACLs

• Service accounts with overbroad permissions

• Ad hoc backups and exports in developer-owned buckets

• Poorly inventoried SaaS data containers and integrations

• Stale data copies in disaster recovery or test environments

Shadow data checklist starter:

• Public cloud storage (object stores, snapshots, backups)

• Dev/test sandboxes and CI/CD artifacts

• SaaS workspaces and connected app data

• Email, file shares, collaboration, and transfer tools

• M&A-acquired datasets pending integration

Alert Fatigue and False Positives Affecting Efficiency

“Alert fatigue occurs when high volumes of security notifications, especially false positives, overwhelm teams, causing genuine risks to go unnoticed or unaddressed.” False positives and false negatives from imperfect classification or context routinely drive noise and missed threats in DSPM programs (see common DSPM challenges).

Causes and effects of alert inaccuracies

The Impact of DSPM Gaps on Risk and Compliance Posture

DSPM gaps cascade into regulatory exposure and governance erosion. Incomplete discovery means sensitive data may be unencrypted or overexposed, undermining readiness for GDPR, HIPAA, and CCPA audits; missing lineage and event capture weaken audit trails; and slow remediation increases breach likelihood and reportable incidents (as outlined by Trend Micro on DSPM). For the business, these gaps translate into delayed incident response, higher audit and assurance costs, longer exception backlogs, and potential penalties—especially when sensitive data resides in shadow stores or transient cloud workloads.

Strategies for Risk Leaders to Mitigate DSPM Shortcomings

Closing DSPM gaps requires a comprehensive approach that aligns technology, people, and processes. The following strategies map directly to the limitations above and can be used as checkpoints against recognized frameworks and your enterprise control catalog.

Prioritizing Enhanced Integration and Interoperability

• Favor standards-based connectivity to IAM, SIEM, DLP, ticketing, and data catalogs; avoid proprietary connectors that inhibit portability (see Gartner Market Guide insights).

• Vendor evaluation questions:

  • Do APIs support event-driven integrations and webhooks?

  • Can the tool ingest and emit normalized metadata (e.g., OpenAPI, STIX/TAXII)?

  • How is identity context (users, service accounts) resolved across clouds?

  • What’s the migration path if we replace adjacent tools?

• For a unifying control plane, consider architectures that consolidate data movement, governance, and monitoring—such as a Private Data Network approach, like that offered by Kiteworks, that addresses DSPM’s missing link in enterprise security.

Implementing Continuous and Comprehensive Data Discovery

• Operate continuous scanning for sensitive, dark, and shadow data; one-time inventories are insufficient in dynamic environments (see the Gartner DSPM overview).

• Program setup steps:

  1. Define authoritative data domains and sensitivity tiers.

  2. Map data flows across cloud, SaaS, on-prem, and pipelines.

  3. Enable agentless discovery where possible; use targeted agents for legacy.

  4. Calibrate classifiers with business context and sample datasets.

  5. Establish SLAs for discovery-to-remediation across owners.

• In M&A or rapid replatforming, expect new blind spots; integrate discovery early in the integration plan (see M&A integration challenges).

• Benchmark AI-assisted tools on coverage breadth, classifier transparency, and cost of continuous scans.

Leveraging AI and Automation for Dynamic Threat Adaptation

• Use unsupervised machine learning to enhance discovery and classification amid evolving data types and usage patterns (see the DSPM solutions market analysis).

• Apply automation to triage, containment, and preventive controls; AI-driven analytics can adapt to new threats and reduce manual toil.

• Expected benefits:

  • Fewer manual errors and faster policy enforcement

  • Improved classification precision and context-aware alerts

  • Accelerated detection-to-remediation with closed-loop workflows

Fostering Cross-Functional Collaboration and Clear Governance

• Establish a cross-team data security board with IT, SecOps, Privacy, Risk, Legal, and business data owners; publish shared playbooks and RACI matrices (see DSPM mistakes and fixes).

• Clarify risk ownership for datasets and flows; tie owners to SLAs and metrics.

• Embed data governance principles in development and data lifecycle processes to reduce rework and audit exceptions.

Emphasizing Continuous Monitoring and Policy Adjustment

• Track core DSPM metrics—exposed sensitive data percentage, mean time to remediate, and control compliance rates—to continuously improve (see DSPM KPIs).

• Review policies monthly or quarterly; test classifier precision and response automation against real data samples and red-team scenarios.

• Use immutable audit trails and automated evidence collection to support both regulatory attestations and operational assurance.

Extending Coverage to Hybrid and Legacy Systems

• Choose tools that support mixed agentless/agent-based approaches to cover mainframes, file shares, and custom on-prem apps without sacrificing cloud agility (see DSPM product overview).

• Key vendor questions:

  • How do you normalize metadata from legacy stores?

  • What is the footprint and maintenance of agents, if required?

  • Can you enforce consistent policies across on-prem and cloud using the same policy-as-code?

Reducing Alert Fatigue Through Risk-Based Prioritization

• Implement risk scoring based on data sensitivity, access context, exposure paths, and business criticality to prioritize real incidents (see Gartner Market Guide insights).

• Regularly test for false positives at scale; tune classifiers and correlation rules by combining DSPM findings with IAM, DLP, and network telemetry.

• Integrate alert management with SOAR or case management to ensure consistent triage, suppression, and feedback loops.

The Future of Data Security Beyond Traditional DSPM

DSPM will remain foundational, but the future lies in AI-native security, real-time stream processing, and policy-as-code that enforce controls where data lives and moves. Expect solutions to emphasize holistic data governance, continuous risk assessment, and proven interoperability across evolving stacks—not just static inventories or periodic scans. Risk leaders should evaluate partners on their crisis readiness, adaptability, and ability to unify data controls across clouds, SaaS, and legacy—ideally through architectures that consolidate visibility, governance, and secure data exchange, such as the Private Data Network from Kiteworks.

How Kiteworks addresses DSPM gaps in practice:

• Unified, private data network centralizes secure file and data exchange (email, SFTP/MFT, APIs, web, and cloud connectors) to reduce shadow data and blind spots.

• Policy-as-code with real-time, event-driven enforcement and webhooks streamlines automated remediation and shortens dwell time.

• Built-in encryption, zero-trust access, and integrated AV/ATP/DLP/CDR inspection minimize exfiltration and misconfiguration risk.

• Immutable logging, granular metadata capture, and automated evidence collection strengthen audit readiness and compliance.

• Open APIs and interoperable connectors integrate with IAM, SIEM, DSPM, and data catalogs, avoiding lock-in and accelerating deployment.

• Hybrid and legacy coverage via gateways and connectors normalizes controls across cloud, SaaS, and on-prem systems.

• Risk-based prioritization and workflow orchestration reduce alert fatigue and focus teams on the highest-impact issues.

To learn more about securing confidential data beyond your DSPM solution, schedule a custom demo today.

Additional Resources

• Blog Post DSPM vs Traditional Data Security: Closing Critical Data Protection Gaps
• Blog Post DSPM for Law Firms: Client Confidentiality in the Cloud Era
• Blog Post DSPM for Healthcare: Securing PHI Across Cloud and Hybrid Environments
• Blog Post DSPM for Pharma: Protecting Clinical Trial Data and Intellectual Property
• Blog Post DSPM in Banking: Beyond Regulatory Compliance to Comprehensive Data Protection