Compare DSPM, DSP, DAM, and DAG platforms. See how 9 vendors stack up and get an RFP scorecard to choose the right data security platform.
The data security platform market splits into four categories: DSPM, DSP, DAM, and DAG. Each solves a different part of the problem, and most enterprises need more than one to close the gap between visibility and enforcement. This guide breaks down what each category actually does, maps nine major vendors against them, and gives evaluators a scorecard for running an RFP.
Buyers evaluating data security platforms run into four overlapping category labels, often used inconsistently by vendors and analysts alike. Understanding what each one actually covers is the first step to building an accurate shortlist.
Data Security Posture Management (DSPM) discovers and classifies sensitive data across an environment, then reports on exposure: who can access what, where data is overshared, and where policy doesn't match risk. DSPM is fundamentally a visibility and reporting layer. It tells an organization where the problems are. It does not fix them.
Data Security Platform (DSP) is a broader, less precise label that vendors apply to suites combining discovery, classification, and data-layer protection such as encryption, tokenization, or masking. DSP platforms often protect the data itself rather than governing who can access it, which makes them complementary to, rather than a substitute for, access governance.
Data Activity Monitoring (DAM), sometimes called Database Activity Monitoring, logs and audits data access at runtime. DAM tools have their roots in on-premises database security and excel at detecting anomalous or unauthorized activity after it happens. Most legacy DAM architectures were not built for cloud-native platforms like Snowflake and Databricks, which creates coverage gaps as enterprises migrate.
Data Access Governance (DAG) defines and enforces fine-grained access policy, in real time, across data platforms. DAG answers the operational question DSPM only reports on: not just who can access sensitive data, but whether they should, and what happens the moment they try. This is the category where posture becomes enforcement.
The distinction that matters most for evaluators: DSPM without DAG produces a better report, not a safer environment. Posture visibility identifies risk. Only enforcement closes it.
A useful RFP starts by testing whether a vendor's category label matches what the platform actually does at runtime. The following questions surface that gap quickly.
Does the platform enforce policy, or only report on it? Many DSPM tools stop at recommendations; ask for a live demonstration of a policy actually blocking or masking access, not a dashboard showing where it should.
How does the platform cover cloud-native data platforms? Snowflake and Databricks have become the default enterprise data layer. A platform that requires bolt-on connectors or agents for either one is working against, rather than with, how the environment is actually built.
What is the architecture: agent-based, proxy-based, or native and agentless? Agent and proxy architectures add operational overhead, latency, and a growing footprint as environments scale. Native, agentless architectures integrate directly with platform-level policy engines.
Does the platform extend to AI agents and non-human identities? Data access increasingly comes from AI agents, service accounts, and pipelines rather than human users alone. A platform built only for human access review will miss this category of risk entirely.
What does time-to-value actually look like? Ask for a specific number, not a range: how long from contract signature to the first enforced policy in production.
How is pricing structured as data volume and platform count grow? Some platforms price per data source or per connector, which can make multi-platform, multi-cloud environments unexpectedly expensive to scale.
Evaluators comparing multiple vendors benefit from scoring each one against the same fixed criteria rather than relying on vendor-led demos alone. The following scorecard structure covers the dimensions that matter most.
Score each vendor on each row, and weight the rows according to which categories matter most for the organization's environment. A vendor that scores well on discovery but poorly on enforcement is a DSPM tool wearing a DAG label.
The nine vendors most commonly shortlisted in enterprise evaluations sit in different places across the four categories. Few offer meaningful coverage of more than one.
Most vendors on this list specialize in one category and partner or integrate to cover the others. TrustLogix's differentiation is architectural: a single, proxyless, agentless platform that spans discovery, enforcement, and AI agent governance without requiring a second tool to close the gap between reporting and action.
A unified data security platform closes the loop between the four categories instead of forcing the buyer to stitch them together. It discovers sensitive data continuously, the DSPM function, and enforces least-privilege policy on that data in real time, the DAG function, across every platform where the data lives, including cloud-native environments like Snowflake and Databricks.
Enforcement extends to where risk is moving fastest: AI agents, pipelines, and non-human identities acting on data at machine speed, not just human users reviewed on a quarterly cycle. This is what runtime authorization means in practice. Policy isn't just defined; it's enforced at the exact moment access is requested, whether the requester is a person or an agent.
Posture without enforcement is just a better report. A unified platform is the difference between knowing where the risk is and closing it.
Immuta and TrustLogix both enforce fine-grained access policy on structured data, which puts them in direct competition for data access governance evaluations. The differences that matter most to buyers are architecture, AI agent coverage, and whether posture monitoring is built in or bolted on.
TrustLogix operates natively within each data platform's own policy engine, agentless and proxyless, so policies are enforced without an intermediary layer sitting between the user and the data. Immuta's enforcement model has historically relied more heavily on platform-specific integrations and, in some deployments, proxy-based query interception. Buyers should ask each vendor directly how policy is enforced on their specific target platforms, since architecture affects latency, operational overhead, and how quickly new platforms can be onboarded.
TrustLogix extends policy enforcement to AI agents and non-human identities alongside human users, governing access at the point where AI pipelines and agents touch sensitive data. Immuta's core strength is human and application access governance on structured data; buyers evaluating AI agent governance specifically should confirm how far each platform's non-human identity coverage extends today, not on a roadmap.
TrustLogix combines DSPM-style discovery and posture visibility with enforcement in a single platform. Immuta's platform is enforcement-centric; organizations that need continuous discovery and posture reporting alongside policy enforcement should confirm whether that requires a separate module or a third-party integration.
A proxyless, agentless architecture generally reduces the operational footprint required to onboard new data sources and platforms. Buyers should ask both vendors for a specific time-to-first-enforced-policy number in a comparable environment, rather than relying on general claims from either side.
Immuta is a strong fit for organizations with an established structured-data governance program that doesn't yet need to extend policy to AI agents or non-human identities. TrustLogix is the stronger fit for organizations that need discovery, enforcement, and AI agent governance unified in one platform, particularly those running Snowflake or Databricks natively.
Securiti and TrustLogix approach data security from different starting points. Securiti's roots are in privacy and DSPM breadth across many data types; TrustLogix's strength is deep, enforced access control on structured data and AI agent governance.
Securiti is primarily a DSPM and privacy management platform, with wide coverage across structured, unstructured, and SaaS data for discovery, classification, and privacy compliance workflows. TrustLogix is positioned as a unified DSPM plus DAG plus AI data security platform, with enforcement, not just discovery, as a core capability from the start.
TrustLogix's structured data enforcement is built to operate natively within platforms like Snowflake and Databricks, applying fine-grained, attribute-based and role-based policy directly at the platform level. Securiti's broader data-type coverage, spanning unstructured and SaaS sources, comes with less depth specifically on structured data policy enforcement. Buyers whose primary risk sits in structured, cloud-native data platforms should weigh this depth difference carefully.
TrustLogix governs AI agent and non-human identity access to data as a core capability, resolving the human identity behind agent requests so access reviews have the evidence they need. Buyers should confirm directly with Securiti how their AI governance capabilities handle agent-to-data access at runtime, versus discovery and classification of AI-related data assets.
A platform with broader data-type coverage often carries more configuration surface area to manage. TrustLogix's narrower, deeper focus on structured data and AI agent governance is generally faster to operationalize for enterprises whose primary risk is concentrated in a small number of cloud data platforms rather than spread across many data types.
Securiti is a strong fit for organizations whose primary need is broad privacy and DSPM coverage across many data types and SaaS applications. TrustLogix is the stronger fit for organizations whose risk is concentrated in structured, cloud-native data platforms and who need runtime enforcement and AI agent governance, not discovery alone.
IBM Guardium and TrustLogix both provide data activity monitoring, but they differ fundamentally in architecture. Guardium relies on customer-managed, tap-based infrastructure built for on-premises databases. TrustLogix is agentless and cloud-native, covering on-prem, hybrid, and cloud environments from a single control plane. The right choice depends on where an organization's data currently lives and where it's headed.
IBM Guardium is one of the most widely deployed database activity monitoring tools in enterprise security, and it has long been a dependable default for large, static, on-premises database environments. Its tap-based architecture captures database traffic reliably for organizations that have not yet begun migrating to the cloud.
Guardium's architecture requires provisioning and managing taps, collectors, aggregators, and a Central Manager, all running as customer-managed infrastructure. Every component generates ongoing compute, storage, and network costs, and for a mid-size enterprise those costs can rival or exceed the license itself.
Guardium also lacks native support for Snowflake and Databricks, two of the most widely adopted cloud data platforms. Connecting either requires external tooling, which adds operational overhead and creates coverage gaps.
Cloud migration compounds the problem. Organizations running hybrid environments end up maintaining parallel infrastructure, taps and collectors for on-prem databases alongside a separate set for cloud databases, doubling the footprint while data is still in transit. Scaling isn't automatic either: every new cloud database requires additional tap, collector, and aggregator capacity, added manually.
Guardium's logs also require enrichment before they're useful for incident response. Security teams commonly export Guardium logs to a separate service to add user, IP address, and OS context before forwarding to a SIEM, resulting in a three-vendor architecture just to get actionable security context from database activity.
A cloud-native, agentless platform eliminates the infrastructure Guardium requires. There are no taps, collectors, or aggregators to provision. Adding a new database, whether on-prem, in Snowflake, or in Databricks, means creating one user, and log ingestion begins immediately.
Security context (user, IP address, OS, time, application) is built directly into monitoring policies rather than requiring a separate enrichment layer. Alerts route to the SIEM as a standard output. The deployment model is a single SaaS instance or one data plane instance in the customer's environment, compared to twelve or more customer-managed components in a standard Guardium enterprise deployment.
The cost difference has two parts: the license savings from replacing Guardium, and the infrastructure savings from eliminating the cloud compute, storage, and network costs required to run its tap-based architecture. For organizations running Guardium in a hybrid cloud environment, the infrastructure cost is often not tracked separately from broader cloud spend and can rival the license cost once accounted for.
Organizations evaluating a move away from Guardium should assess four things before extending its footprint further into the cloud:
TrustLogix identifies and remediates data access risks in under 30 minutes, with no agents and no infrastructure to provision, and with native support for Snowflake and Databricks from day one.
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