Key Takeaways:
- Coverage should be tested against an organization's data estate.
- False positives are the leading reason DSPM findings are ignored.
- Sensitivity means little without knowing who can reach the data
- AI coverage is a core requirement, not a roadmap item
Choosing the right DSPM platform requires looking beyond a standard feature checklist. Most providers deliver some version of the four common capabilities–data discovery, classification, access intelligence, and remediation workflows–and perform well on curated datasets. However, real results emerge when the tools are tested in live environments rather than a controlled sandbox, or when an audit forces security teams to produce concrete evidence.
In fact, according to a report commissioned by Capital One Software, most decision-makers trust their current security tech stack for adequate data protection, yet more than half report a lack of complete visibility into vulnerabilities.
Moreover, selecting the right DSPM solution has become harder and more complex as more providers now appear in analysts' reports. GigaOm's 2026 DSPM Radar report highlights this trend by assessing 25 vendors, more than double the number evaluated in the 2025 report.
Read on as the blog discusses the critical factors enterprises must watch for when navigating this crowded landscape, ask vendors the critical questions that a simple slide deck cannot answer, and select a strong DSPM solution that fits their unique needs.
Begin with the Data Estate Coverage Capability
Data security starts with a comprehensive understanding of an enterprise data estate. A modern DSPM tool must offer enterprises broad coverage across diverse data environments. Hence, look beyond tools built for a single environment or a limited set of environments, such as specific productivity suites, structured data stores, or cloud-only environments.
In the age of AI, enterprise data doesn't respect any boundaries. It now flows into and out of structured datastores, unstructured data accounts, SaaS environments, on-premises systems, streaming platforms, hybrid multi-cloud stores, and AI workflows. Missing even a single environment could lead to shadow data, forgotten buckets containing sensitive information, or overprivileged repositories.
Ask vendors:
- Which environments require APIs or connectors, and which ones offer native coverage?
- Does the discovery offer only a cloud-first estate, or does it also extend to on-premises systems?
- Does the tool discover shadow data and ROT data, such as in abandoned buckets, etc.?
- Can the platform also discover AI systems, model pipelines, or vector databases?
DataAI Command Platform, by Securiti AI, a Veeam company, offers built-in DSPM that delivers full contextual intelligence around data and AI from day one. The platform offers the broadest coverage across on-premise systems, IaaS, SaaS, hybrid cloud, streaming platforms, and AI workflows, leveraging hundreds of connectors, common grammar, and detection policies.
Evaluate Classification Engine for Context & Depth
Data classification is a core feature of every DSPM tool. However, the key distinction lies in how that classification engine works. Most engines still rely on outdated classification techniques such as regular expressions or simple pattern matching. Not only do these techniques result in high false positives, but they also cause inconsistent labeling and erode trust in the tool.
Strong DSPM solutions go beyond simple regex, leveraging advanced AI-powered techniques like NLP and exact data matching instead. Furthermore, classification should be driven by comprehensive data context and depth, including sensitivity, access controls, regulatory context, and business relevance. Evaluation criteria must rigorously measure precision, recall, and accuracy across multilingual datasets and image-based content, with tuning based on an organization's classification feedback.
Ask vendors:
- What is the measured false-positive rate of their classification engine?
- Is the customer required to supply examples for classification tuning?
- Can we add custom classifiers according to business requirements?
- Does custom classifier rollback involve re-running the scan?
DataAI Command Platform leverages 300+ OOTB classifiers across structured, semi-structured, and unstructured data assets to classify all types of personally identifiable information (PII), PHI, PCI, sensitive, and other regulated data. With AI-powered classification tuning, data teams do not need to rescan samples. The tuning engine uses classification feedback, such as false positives and false negatives, to improve accuracy and precision.
Prioritize Toxic Combinations to Neutralize High-Impact Threats First
Large enterprises hold millions of sensitive data points across diverse data systems, repositories, workloads, and now AI pipelines. Identity sprawl has also spiraled out of control, with Palo Alto’s 2026 Identity Security Landscape report highlighting that machine identities have outnumbered humans 109:1. Also, the rate at which AI agents (non-human identities) touch sensitive data has skyrocketed accordingly.
A long list of isolated findings does not carry real value, as not all over-permissioned buckets contain sensitive data, and not all sensitive data requires high-risk permissions. The cost of this noise is measurable: 51% of Security Operations Center (SOC) analysts feel overwhelmed by the high volume of alerts, and teams resolve only 49% of the alerts they receive in a workday.
Robust DSPM solutions should help security teams neutralize high-impact threats first. This not only reduces alert fatigue but also helps remediate critical threats immediately. To achieve that, the tool must go beyond sensitivity labels and link data, identities, and AI via a graph of relationships to identify toxic combinations. Contextual risk intelligence gives security teams the much-needed confidence to bypass low-value risks and focus on the ones that truly matter.
Ask vendors:
- What signals affect the risk score or rank apart from data sensitivity?
- Can the DSPM tool identify Toxic Combinations of Risk?
- Is the Graph capability built on top of the platform or integrated natively from day one?
- Can the tool track and identify which AI agents touched sensitive data?
The Data Command Graph capability, built natively from day one, is the engine that drives the DataAI Command Platform. The graph leverages an out-of-the-box toxic combination based on relationships, tests, and security research. As a result, security teams receive a single compound risk alert rather than isolated findings.
Consider DSPM Purpose-Built for AI Security & Governance
Generative AI adoption has skyrocketed in recent years as the technology promises significant value to enterprises of all sizes. However, organizations cannot ignore the fact that the same technology has significantly widened the attack surface. An AI Adoption and Risk report from 2025 highlights that 34.8% of corporate data sent to AI tools by employees is sensitive, which is three times the percentage reported in 2023.
In fact, the recent wave of AI agents has added to the growing complexity and challenges around AI governance. Gartner predicted that by 2028, Fortune 500 companies will have more than 150,000 AI agents, up from fewer than 15 forecasted for 2025.
Credible DSPM solutions must no longer be limited to traditional environments and instead be purpose-built for the AI landscape. Enterprise-grade tools must discover sanctioned and unsanctioned AI models, customer pipelines, and vector databases. It should also provide contextual insights into AI workflows, showing how sensitive data moves through these systems. In fact, AI-specific security posture management has become a defining benchmark; it was featured as a core evaluation criterion in the GigaOm Radar for DSPM (v3) report across 25 vendors.
Ask vendors:
- Is AI security posture a native capability or a third-party integration?
- Does discovery extend to vector databases, embeddings, or RAG pipelines?
- Are regulatory frameworks mapped for AI?
- What data controls apply before the AI models touch sensitive data?
The DataAI Command Platform tracks and creates a comprehensive inventory of active models across clouds and third-party services. For instance, the tool catalogs AI assets leveraging a model registry linked to the Data Command Graph. It helps provide context for AI models, including data processing activities and business systems that access the model. These capabilities are built to deliver the confidence enterprises need to safely adopt AI and innovate at scale.
Operationalizing findings is a critical challenge for organizations planning to integrate a DSPM into their tech stack. Discovering data and identifying risks that matter is half the picture. The loop can only be completed when there’s also a mechanism to take action against those risks. Many tools stop at dashboards and alerts, with deeper remediation workflows priced as add-on licensing.
A leading DSPM solution should offer native remediation workflows that quarantine exposed files, delete redundant and obsolete data, and push tickets into existing workflows.
Ask vendors:
- Which remediation measures require add-on licensing, and which ones are native to the tool?
- How does remediation work in complex scenarios?
- How do owners receive alerts?
- Which ticketing or workflow tools are supported?
The DataAI Command Platform, with built-in DSPM, delivers a no-code, low-code workflow orchestration engine for effortless remediation. The tool automatically triggers remediation workflows, leveraging hundreds of natively integrated applications. Security teams can further create programmable workflows tailored to their unique needs.
Conclusion
The right DSPM platform should offer enterprises comprehensive visibility into the data estate, scale as data volume grows, and provide security and governance teams with a single source of truth rather than multiple competing ones.
Securiti AI, a Veeam company, delivers the DataAI Command Platform™ to enterprises. Its DSPM capability offers extensive coverage across a multitude of data environments. Powered by the Data Command Graph™, the tool gives security teams insights into relationships among sensitive data, identities, policies, and AI systems, enabling them to act on a single real, compound finding rather than chasing multiple alerts.
In the 2026 GigaOm Radar for DSPM, Veeam's Securiti AI was named a Leader and Fast Mover, posting the report's highest scores across Key Features, Emerging Features, and Business Criteria.
Request a demo to see how Securiti secures Data+AI across the enterprise.
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