Find the Risk. Fix the Risk.
Product
Most data security platforms stop at discovery by data store. They only tell you where your sensitive data lives when you click into each one, and offer no clear path to doing something about it. The result is a gap in both visibility and protection that most security teams have learned to live with -- but should not have to. Two new capabilities close that gap: the Data Explorer, which surfaces risk across all your data stores at scale through plain language search, and the Policy Builder, which turns security intent into deployed protection. Together they make the full loop -- from finding risk to remediating it -- accessible to the people accountable for both. The platform uses models from multiple leading AI providers, including Claude from Anthropic, for the reasoning work that makes both possible.
The DSPM category has converged on a single deliverable: the data inventory. Find the data, classify it, surface a risk score. What happens next is left to your team -- and usually to your engineers.
Most platforms will tell you where sensitive data lives, but only when you navigate into each data store individually. There is no way to ask a question across your entire environment and get a single, unified answer. And even when a finding surfaces, turning it into a policy that actually does something -- scoped correctly, configured for the right connector, with the right action attached -- requires a separate workflow that most security and compliance teams cannot complete without engineering help.
This is the gap the category has failed to close. Knowing what is exposed and being able to act on it are treated as two different problems, solved by two different tools, often by two different people.
They should not be.
Data Explorer: Surfacing Risk Across Everything, at Once
Security teams can now ask questions like "which resources containing PII are externally accessible?" or "where does financial data live that contractors can reach?" and get answers searched across every connected data store at once, at a scale that was not previously practical.
The AI layer handles the heavy work: understanding what is being asked, mapping it to the right catalog concepts across connectors, and executing the search in a way that does not require choosing which data sources to query or knowing how each is structured. Results come back with the model's reasoning surfaced alongside them -- what it understood, which terms it matched, how it scoped the query -- so results can be verified before anyone acts on them.
"We have data in Snowflake, in Salesforce, in half a dozen other systems. If I want to answer a specific question about where contractor-accessible PII lives, that's a data engineering request. It goes in the queue. I get an answer back in two weeks, if I'm lucky. By then the environment has already changed." — CISO, Financial Services
In a security context, a result that misrepresents scope is worse than no result at all. Surfacing the reasoning is not a feature. It is the minimum bar for AI-assisted search to be trustworthy.
Policy Builder: From Security Intent to Deployed Protection
Finding the risk is one step. Closing it is another.
Security and compliance teams can now describe what they want to protect in plain language -- the data type, the condition that constitutes a violation, the action to take -- and get back a complete, structured policy workflow ready for review. The policy appears in the Policy Builder as a node graph showing the connector, trigger conditions, filters, and actions. It can be adjusted before it is saved. Nothing runs without human approval.
This matters because policy generation is high-stakes. A policy that misinterprets an intended action -- scoping too broadly, triggering on the wrong condition -- has real consequences. The platform uses models selected specifically for their ability to follow complex instructions and surface ambiguity rather than silently resolve it. Models from multiple leading AI providers, including Anthropic's Claude, are selected based on the reasoning demands of each task.
Coming shortly, Simulation will let teams preview exactly how many resources a policy would affect before it runs -- so the move from intent to protection carries full visibility at every step.
"I'm tired of tools that just point fingers. They tell you — hey, you've got a lot of bad stuff. Good luck with that. There's no path from the finding to the fix." — Global CISO, Retail Services
Seeing Risk and Closing It
The gap between knowing what your data is exposed to and having protection in place is where most security programs lose ground. It is an organizational problem as much as a technical one -- the people closest to the risk rarely have the tools to act on it directly.
The Data Explorer and Policy Builder change that dynamic. Finding risk and responding to it becomes accessible to the teams accountable for both, without the technical overhead that has historically stood in the way.
To see it in your environment, book a call.
FAQ
How do I search for sensitive data across cloud and SaaS environments at enterprise scale?
The challenge at scale is not query syntax -- it is executing search across massive, distributed data stores without prohibitive performance costs. AI-powered search handles the scope and translation automatically, returning results across all connected sources with the model's reasoning surfaced so you can verify what was searched and why.
How can security and compliance teams build policies without engineering involvement?
Describing a policy in plain language -- what to protect, what constitutes a violation, what action to take -- now produces a complete, reviewable policy workflow. The result is displayed visually for review and approval before anything is saved or deployed.
What should I look for in an AI-powered DSPM platform?
Look for platforms where the AI explains its reasoning alongside its conclusions, and where human review is required before any action runs. Results and policies that cannot be inspected before acting on them introduce more risk than they reduce.
How does a data security platform help compliance teams, not just security engineers?
Compliance and GRC teams carry accountability for data risk without always having the technical access to query a catalog or configure a policy engine. Natural language interfaces make both self-service -- no engineering handoff required.
Can AI models be trusted to operate in environments with sensitive data?
It depends on how they are used. AI models here handle reasoning tasks -- understanding queries, generating policy structures, explaining results -- not data storage or retention. Model selection, transparency design, and mandatory human confirmation before any action runs are all part of how this is built responsibly.
How do I know a policy will not affect more data than intended?
Simulation, launching shortly, lets teams preview the blast radius of any policy before it runs -- showing exactly how many resources would be affected. This makes the move from a plain-language description to deployed protection one that can be verified at every step.
from our blog
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