Find every store
Will discover stores in S3, Azure Blob Storage, Google Cloud Storage, relational databases and SaaS document platforms, sampling each one deterministically.
Coming soon
Know where sensitive data lives.
Understand who can reach it.
Connect data discovery, classification and access context to focus protection where exposure matters. Give security, platform and governance teams a shared view of sensitive-data risk.
Data exposure review
Data store
Sensitivity context
Access relationship
Investigation context
01 / Scope stores
Start with the data that matters.
- Store inventory
- Classification scope
Discuss the stores and classification requirements in your evaluation.
02 / Review access
Bring sensitivity and permissions together.
- Sensitivity label
- Permission relationship
The intended workflow connects data context with who can access it.
03 / Evaluate action
Agree a useful exposure review.
- Data owner
- Evaluation outcome
Define the ownership and evidence needed to act on a data exposure concern.
Find
Data discovery
Buckets, databases and SaaS documents.
Label
Validated classification
Personal, health, payment, financial, secrets.
Access
Effective permissions
Who can read each store, and how.
Flow
Lineage and residency
Where data moves, and where it may not.
The category
What is DSPM?
Cloud teams create data stores faster than anyone can catalogue them: a bucket for an export, a database snapshot for a test, a shared folder for a project. DSPM finds those stores, samples their contents to recognise sensitive data, and then asks the questions that decide breach impact. The output is an inventory of sensitive data ranked by risk, not a list of files.
Is it exposed?
Public access, missing encryption and absent audit logging on a store that holds regulated data.
Who can read it?
The human and workload identities with effective access, and whether they still use it.
Where does it go?
Flows between systems, and personal data leaving the region it is meant to stay in.
The design
How will SecurityVault's DSPM work?
Six steps, each designed to give the same answer on an unchanged store.
01 · Discover
02 · Classify
Label with proof
Will detect personal, health, payment and financial data, secrets and your own patterns. A high-sensitivity label will require the value to pass its checksum or structure check, not just match a shape.
03 · Map access
Resolve who can read
Will map effective permissions from IAM, bucket and database grants, flag over-privileged and unused access, and detect first-time access to a sensitive store or a bulk download.
04 · Trace
Follow the flow
Will track how sensitive data moves between systems and evaluate every store against the residency and retention policies you define.
05 · Rank
Fix what matters first
Will score each store on sensitivity, exposure, access breadth, encryption and audit logging, so the most consequential data is fixed first.
06 · Act
Route to an owner
Data-store risks will reach an owner with a deadline, and residency results will feed continuous compliance evidence for your data controls.
Correlation
Why should data risk sit on the same graph as code and cloud?
Data is the end of the attack path, so it is only meaningful alongside the path itself. SecurityVault is designed to place classified data stores in the graph that already links code, artefacts, cloud resources and identities.
01
Code to data
A vulnerable package in an image matters more when the workload running that image can read a store full of customer records.
02
Identity to data
An over-privileged service account becomes a priority the moment it can reach health or payment data. SecurityVault already models assumable roles and reachable data stores as graph edges.
03
Cloud to data
A public, unencrypted bucket is one cloud posture finding. The same bucket holding personal data in the wrong region will be one ranked, explained risk.
FAQ
DSPM questions.
- What is the difference between DSPM and DLP?
- Data loss prevention (DLP) watches data in motion and blocks a specific transfer. DSPM looks at data at rest: where sensitive data lives, who can reach it and how well it is protected. DSPM tells you which stores to fix; DLP stops a leak in the moment. The two complement each other.
- Will DSPM need to copy all of my data?
- The planned design uses samples rather than a complete replica. Classification still requires authorised access to sensitive content. Sampling scope, processing location and evidence retention must be reviewed before deployment.
- Which data stores is SecurityVault's DSPM designed to cover?
- Object storage in AWS S3, Azure Blob Storage and Google Cloud Storage, relational databases, and SaaS document stores such as Google Drive, SharePoint and Confluence.
- Can DSPM help protect data used to train AI models?
- That is part of the design. A training dataset will be scanned for personal, health and payment data before a training run, and a dataset without a declared source and consent basis will fail closed. See AI security posture management.
- When will DSPM be available?
- DSPM is in development. Contact us about the stores, classifications and access reviews you want included in a future DSPM evaluation.
Further reading: identity attack paths from assumable roles to data · cloud attack paths: chaining posture, workload and identity. Related offerings: SaaS security posture · AI security posture. Built for CISOs and GRC and audit teams.
Discuss your requirements
Tell us where your sensitive data lives.
Identify the data stores, sensitive-data categories and access questions that should shape your DSPM evaluation.