6 DSPM Implementation Challenges and How to Overcome Them

Implementing data security posture management (DSPM) should be a straightforward maturity step: discover sensitive data, understand exposure, then reduce risk. But in enterprise environments, DSPM implementation challenges tend to show up quickly. Data is distributed across SaaS, cloud storage, databases, warehouses, endpoints and pipelines. Access is governed by APIs that can throttle under load. Ownership is fragmented across IT, security, data teams and business units. Then GenAI adds a new path for sensitive data to move outside governed systems.

#1: Technical and Environmental Constraints

DSPM visibility depends on what each platform can expose, and enterprise estates are rarely uniform. Scanning speeds vary with latency, connection quality, tenant architecture and the number of objects that must be enumerated. Even when read access is available, source APIs can limit what actions are possible, including whether you can delete, move or relabel content at scale. Continuous scanning can also trigger API throttling, which slows discovery and creates uneven coverage across systems. Microsoft’s Graph documentation is explicit that throttling exists to protect availability and reliability, and that limits can vary by scenario.  

For IT Ops, the impact is operational and political. Discovery timelines slip, remediation queues expand and stakeholders question whether DSPM is delivering value. The fix is not brute force. It is designing the rollout around platform constraints so discovery stays predictable and repeatable.

How To Overcome: Two-Phase Scanning Strategy

A two-phase scanning strategy improves speed and consistency while reducing throttling risk.

• Phase 1: High-speed metadata cataloging. Build a full inventory using lightweight calls that capture location, owner, sharing posture, identities with access, and exposure signals. This creates fast visibility into where risk is concentrated without demanding deep inspection everywhere.
• Phase 2: Targeted deep content inspection. Apply heavier inspection only where Phase 1 indicates elevated risk, such as public links, broad permissions, risky identities, anomalous access, or repositories known to contain regulated data.

Operationally, this approach shortens time-to-inventory, minimizes API pressure and concentrates compute where it changes outcomes. It also aligns with how most organizations reduce risk early: tightening permissions and links, fixing obvious oversharing and prioritizing the repositories that matter most.

This is where platform capabilities matter. Use DSPM tools that can coordinate job scheduling across sources, manage backoff automatically and correlate exposure signals across identity, repository and policy. Forcepoint DSPM is designed to support throttling-aware discovery and phased inspection so teams can scale visibility without turning scanning into a reliability problem.

#2: Scalability and Data Volume Challenges

Enterprise DSPM has to work across terabytes and petabytes, including structured data stores and sprawling unstructured repositories. Many implementations slow down because the organization underestimates the scope of unstructured and shadow data. A quick assessment may be feasible for a narrow set of systems, but it becomes unrealistic when the footprint includes multiple clouds, multiple tenants, legacy shares and high-churn collaboration content.

Volume creates three predictable bottlenecks:

• Scan queues grow and time-to-coverage stretches
• Prioritization becomes noisy, so teams confuse “large” with “high risk”
• Remediation cannot keep pace, leaving known exposures open longer than planned

How To Overcome: Data Hygiene and Scoped Discovery

Scaling DSPM starts by reducing unnecessary scope, then expanding in controlled phases.

• Reduce ROT data. Removing redundant, obsolete or trivial content lowers storage costs and reduces the attack surface that must be continuously assessed.
• Define a first-phase scope that matches operational capacity. Start with the repositories, environments, and identities most tied to risk, such as executive collaboration spaces, customer data systems, or repositories with broad external sharing.
• Use automated discovery to locate shadow data across multi-cloud and on-prem environments. Then bring the highest-risk locations into the managed perimeter first.

The key is connecting discovery to outcomes. DSPM best practices favor a rollout that shows exposure reduction early, then scales depth of inspection as the program proves value. Anchoring the program in concrete DSPM use cases keeps scope disciplined because every added repository is justified by the risk it reduces.

Forcepoint DSPM supports phased discovery across hybrid environments so IT Ops can prioritize high-impact repositories first, then expand coverage without losing control of timelines.

#3: Organizational Silos and Executive Buy-In

DSPM is not only a tooling deployment. It is a cross-functional operating model. Data owners sit in business units. Identity and access policies may sit with IT. Cloud security may be a separate team. Governance and compliance often have their own definitions of sensitivity and acceptable risk. Without alignment, DSPM findings become tickets that bounce across teams with no clear owner or service level.

Common failure modes are easy to spot: no shared definition of sensitive data, unclear remediation authority, dashboards that show growing counts of findings, and no credible story that risk is going down. Over time, DSPM becomes “security noise” instead of an operational control.

How To Overcome: Workflow Orchestration and Executive Dashboards

DSPM earns buy-in by showing progress and assigning accountability.

Start with executive reporting that focuses on trends leaders can interpret: fewer public links, fewer overly broad permissions, fewer sensitive data stores in unmanaged locations, and shorter remediation cycles. Then map workflows to the teams who can actually fix issues: repository owners, application owners, identity administrators, and governance leads.

This is also where implementation patterns matter. If you want perspective on how organizations implement DSPM at scale, use this resource to pressure test your operating model: organizations implement DSPM. In Forcepoint deployments, workflow capabilities and reporting help move from detection to remediation with assignment and closure metrics, not just findings.

If you are operationalizing DSPM with broader control-plane visibility and enforcement alignment, it can also help to see how to deploy DSPM in a visibility-to-control model.

#4: False Positives and Alert Fatigue

At enterprise scale, accuracy is the difference between a program that scales and one that collapses under its own output. Pattern matching and rigid rules create noise, especially in unstructured content where context determines sensitivity. When findings are noisy, teams tune down detection, ignore alerts, or stop trusting the platform. Real issues get buried, and remediation becomes triage.

How To Overcome: AI-Powered Contextual Analysis

Reducing false positives requires context. That means combining semantic analysis with policy logic and behavioral signals:

• Classification that accounts for meaning and surrounding context, not only keywords and regex
• Behavioral baselines for “busy but safe” activity, then alerting on anomalies that indicate true risk
• Tuning to business terminology and document types so policies match how your organization labels and uses information

Forcepoint’s AI Mesh technology is designed to improve classification fidelity by using multiple models and techniques so findings translate into action with less noise.

This is also the moment to clarify control roles. DSPM vs. DLP is not either-or. DSPM identifies where sensitive data lives and how it is exposed. DLP prevents sensitive data movement and exfiltration. The strongest outcomes happen when classification and context are consistent across both discovery and enforcement.

#5: The Remediation Gap

Discovery without remediation is visibility theater. Many organizations identify sensitive data and oversharing quickly, then realize remediation does not scale. Manual work creates backlogs. Fragmented tooling forces operators to switch contexts across consoles. Some fixes are limited by what a source API supports. Others require judgment because access changes can disrupt workflows.

The remediation gap leaves organizations in a risky middle state: they know where exposure exists, but cannot close it fast enough to matter.

How To Overcome: Automated Remediation

A scalable remediation model uses three tiers:

• Manual: Reserved for high-judgment cases that require an analyst decision
• Guided: Prebuilt playbooks that enable one-click fixes for grouped exposures by owner, repository, or business unit
• Automated: Policy-driven workflows for clear-cut issues like revoking stale guest access, removing public links, or tightening permissions that violate a defined standard

Automation should be pragmatic. When an API does not support an action, the workflow should route the task to guided or manual steps with clear ownership and tracking. When you evaluate best DSPM solutions, prioritize those that connect findings to remediation actions, support bulk fixes where appropriate, and provide evidence that risk is declining over time: best DSPM solutions.

Forcepoint DSPM is built to close the remediation gap with flexible workflow options that support manual, guided and automated remediation based on risk and operational constraints.

#6: GenAI Data Exposure Risks

GenAI introduces a new path for sensitive data to move outside governed systems. Users paste content into prompts, upload files for summarization, connect plugins, and share outputs in collaboration tools. Traditional DSPM programs were not built to account for prompt content, AI workspace artifacts, or indirect exposure through third-party integrations.

There is also a security dimension: prompt injection and other LLM risks can manipulate how systems behave and how outputs are trusted. OWASP lists prompt injection as a top risk category for LLM applications, underscoring why AI usage needs governance controls, not just acceptable use policies.  

How To Overcome: API-Based AI Platform Scanning

Modern DSPM programs extend discovery into AI-related systems and artifacts:

• Scan GenAI platforms via API where available to identify sensitive data in prompts and uploads
• Apply classification and policy so AI artifacts are governed with the same sensitivity logic as documents and records
• Enable retroactive cleanup because the first exposure often happened before a policy existed

GenAI usage is already creating a second data layer in most enterprises: prompts, uploads, generated summaries, and shared outputs that rarely follow the same governance path as the source systems they came from.  

GenAI has a way of turning everyday work into a new place where sensitive data can live and spread, from prompt text and file uploads to generated summaries that get copied into chats, tickets, and docs. As you start tightening those workflows, it helps to treat AI platforms like any other data environment and apply the same discovery and governance mindset you would elsewhere, including DSPM for AI applications.

Overcoming DSPM Implementation Challenges with Forcepoint

DSPM implementation challenges are manageable when you treat DSPM as an operational control, not a one-time deployment. The pattern that works is consistent: throttling-aware discovery, scoped rollout, workflow-driven ownership, higher-fidelity classification, and remediation that closes exposure.

Forcepoint DSPM supports that operating model. It helps teams inventory data quickly without turning scanning into a platform reliability issue. It supports phased discovery so IT Ops can prioritize high-impact repositories first. It improves classification accuracy through AI Mesh so findings translate into action. It also connects exposure to remediation workflows so programs show measurable reduction over time.

If you are weighing rollout approaches, start by pressure testing your requirements against what matters most in production, especially coverage, accuracy, and how quickly findings translate into fixes. That is the difference between deploying DSPM effectively and just getting a scanner running.

Request a demo to see how Forcepoint DSPM supports discovery, classification, and remediation in one workflow.