Stores that need a look
16
Out of 38 data assets.
Data discovery
Discovery comes
before defense.
ClAIssify discovers data stores across connected environments, identifies sensitive information, and brings findings together so teams can see what was found and where it lives.
Who owns your data
7 of 38 stores have an owner, from an owner tag or one assigned here. A finding with no owner has nobody to fix it.
Findings by severity
Stores that still need a look, by their most serious finding
20100
Select a bar or severity above for details.
Access level
Who can reach your stores
50250
Select a bar to inspect the access count.
Spread across clouds
Shadow assets that are open to others, and the rest
10
Select the bar for cloud details.
How discovery works
From connected source to a clear inventory.
How data discovery works, in three stages. One: connect supported cloud storage, databases and endpoints; the sources available today are listed in the diagram and under source coverage. Two: AI-assisted discovery analyzes content in context to help identify what a finding represents. Three: review a data inventory where findings are organized by data type, classification and location. The examples shown are illustrative.
01
Connect your data sources
Connect supported cloud storage, databases and endpoints.
Available today
Cloud storage
AWS S3
Azure
Google Cloud
Databases
PostgreSQL
MySQL
MongoDB
DynamoDB- Azure SQL
- Cloud SQL
Endpoints
- Endpoint agent
Windows
macOS
Linux
See source coverage for the current list.
02
AI-assisted discovery
ClAIssify analyzes content in context to help identify what a finding represents, rather than treating every matching value as sensitive by itself.
- Reads the contentLooks at files and records in connected sources.
- Analyzes it in contextAI-assistedThe text around a value helps tell what it is: an identity number, or just an invoice reference.
- Identifies sensitive informationEach finding is given a data type, a classification and a location.
Context takes discovery beyond simple pattern matching: the same value can mean different things in different places.
03
Review a clear data inventory
Findings are organized by data type, classification and location, so teams can review what was found and where.
- Data type
- National identifier
- Class
- Identity data
- Location
- HR / Applicant record
- Data type
- Payment details
- Class
- Financial data
- Location
- Finance / Invoice archive
- Data type
- Health record
- Class
- Health data
- Location
- Clinic / Lab reports
Illustrative example
Findings example
What a finding looks like.
Every finding connects three things: what was found, how it is classified, and where it lives. These rows are made-up examples that show that relationship. They are not results from a customer.
Illustrative example
| Finding | Classification | Location |
|---|---|---|
| National identifier | Identity data | HR / Applicant record |
| Payment details | Financial data | Finance / Invoice archive |
Source coverage
Where ClAIssify can look.
Available today
Cloud storage
- AWS S3
- Azure
- Google Cloud
Databases
- PostgreSQL
- MySQL
- MongoDB
- DynamoDB
- Azure SQL
- Cloud SQL
Endpoints
- Endpoint agent
- Windows
- macOS
- Linux
Why data discovery matters
You can’t make informed data decisions without knowing what you have.
Sensitive information can sit in cloud storage, databases, and endpoints, often spread across teams and environments. Data discovery gives security, privacy, and compliance teams a clearer view of what information exists, how it is classified, and where it lives.
That view helps teams:
Focus protection where it matters. Identify connected stores containing personal, financial, health, or other sensitive information.
Prioritize review. See which findings are sensitive, exposed, or still need attention.
Track discovery coverage. Check which connected assets have been scanned and which remain pending.
Make governance more practical. Use a current inventory to guide access reviews, policies, and remediation work.
Cost of a data breach
$4.99 million
Average global cost of a data breach
The average global cost of a breach reached $4.99 million in IBM’s 2026 report, a 12% increase from the previous year. Breach costs can include investigation and response, disruption to business, and lost business.
This is an industry-wide average, not a direct estimate of the cost of incomplete discovery or a prediction of what any one organization will pay.
A clear inventory helps teams understand where sensitive data is and prioritize their next steps.
AI-assisted discovery
Go beyond a pattern match.
A sequence of digits could be an invoice value, a phone number, or an identity number. Its meaning depends on the information around it. ClAIssify uses AI-assisted context analysis to help identify what a potential finding represents, so teams can review results with more context than a pattern match alone provides.
From scan to useful finding
Read supported content
ClAIssify scans connected sources and analyzes supported files and records.
Interpret in context
AI-assisted analysis considers nearby information to help identify the kind of data a potential match represents.
Organize for review
Findings are grouped by data type, classification, and location, giving teams a practical way to understand what was found and where.
Keep coverage visible
Every connected asset remains visible in the inventory, including assets that haven’t been scanned yet. Teams can see what is complete and what still needs attention.
Illustrative example
Values obscured
The same value(a 12-digit value, obscured)
Context: Applicant record
Applicant ID
Identity number
HR / Applicant record
Context: Invoice archive
Invoice total
Invoice value
Finance / Invoice archive
Context: Contact list
Mobile
Phone number
Sales / Contact list
Each result is presented as a finding with its type and location.
Less manual sorting.
A clearer view of sensitive data across connected sources.
What you see
What ClAIssify helps you see.
- 01
Which connected stores contain sensitive information
See where in your connected sources sensitive information was found.
- 02
What type of information was found
Each finding has a data type and a classification.
- 03
Where the finding lives
Each finding shows the location it was found in.
- 04
Which findings may need review
Findings are brought together in one place, so teams can decide what to look at first.
Questions
Data discovery, briefly.
What is data discovery?
Data discovery is finding out what data you have and where it lives. In ClAIssify, you connect sources, ClAIssify identifies sensitive information in them, and the findings are brought together in one place.
How does AI help with discovery?
AI is part of how ClAIssify analyzes content. It uses the context around a value to help identify what the value represents, so a value is not treated as sensitive just because it matches a pattern.
What appears in the ClAIssify inventory?
Findings, organized by data type, classification and location, so teams can see what was found and where it lives.
Which data sources are supported today?
Today: cloud storage (AWS S3, Azure and Google Cloud), databases (PostgreSQL, MySQL, MongoDB, DynamoDB, Azure SQL and Cloud SQL) and endpoints (Endpoint agent, Windows, macOS and Linux). The Source coverage section above shows the current list.