Data Protection
Protect the data itself, everywhere it goes.
Transform sensitive data at rest, in motion and in use so it’s usable for analytics and AI but reveals nothing if exposed.
How it works
Define the protection.Choose the enforcement.
Each field gets the protection its sensitivity and use call for. Each system gets the enforcement point that fits it. Both are governed by one central policy.
Sanitization
One-way by design
De-identifies real data or generates synthetic data, so it can be shared and analyzed freely.
Anonymization · Synthetic Data
Substitution
Recoverable for authorized principals
Swaps each value for a format-preserving equivalent that analysts and models can use.
Tokenization · Format-Preserving Encryption
Redaction
Stored data unchanged
Masks or filters data at query time, so each user or agent sees only what policy allows.
Masking · Row-level Filtering
Central policy
Protection and access rules
Applied through
Data Protectors
Data Security Gateway
Secure Query
Illustrative data states
Unprotected
000-00-1120
Original sample value
Protected
tok_M80B4
Example protected value
Masked by policy
XXX-XX-1120
Restricted view
Storage
Data Protectors
Applies protection directly at the datastore or within the application, enforcing central policy locally.
Network
Data Security Gateway
Secures cloud and SaaS traffic without installing a protector on every endpoint.
Query
Secure Query
Enforces policy at the database wire-protocol level, so existing drivers and BI tools work unchanged.
What you get
Keep data private and useful.
Usable in protected form.
Field-level methods preserve type, format, length and referential relationships, so protected data can still be queried and joined.
Analytics and AI on protected data.
Analytics, data science and model training run on protected datasets with accurate results and no exposure of real values.
No changes for users.
Analysts keep their drivers and BI tools, and applications keep their code.
Breaches expose nothing useful.
Protection applied at the source means anything an attacker reaches is a token with no exploitable value.
Smaller compliance scope.
Systems that only hold protected data can fall outside audit scope, which reduces compliance effort.
- AI/ML
- Safe training and eval datasets
- Privacy-protected data utility
- Data science and analytics
- Ad hoc queries on sensitive data
- Fully shareable insights and data
- Protected data in use
- App dev and testing
- Business dev and integration
Protection built into every field.
Travels with the data
Protection is applied to each data element, so it holds when data is copied, moved or exported to another system.
Protected while in use
Data stays protected as it’s queried, joined and analyzed, a stage most encryption tools leave exposed.
Foundational to AI security
The same protected data that secures analytics is what makes agents and inference safe downstream.
Supported uses
- AI agent context
- Secure analytics
- Compliance
Protection in every field
- Agent tools and context
- SaaS and REST services
- Cloud data warehouses
- Databases
- Documents
Use cases
The same protection for people and agents.
Data Protectors
One query, different results
Two services run identical queries against an SSN column. Granted callers see the real value and everyone else sees a token, with no code changes.
Data Protectors
Copilots that never see the real value
A support copilot reasons over an SSN-shaped token, and only the authorized rep sees the real value.
Secure Query
BI and data science on production data
Ad hoc SQL can’t be reviewed in advance. The gateway classifies every column referenced and protects the result set for each caller.
Secure Query
Agent-driven analytics
An agent writes a new query every run. Enforcement doesn’t depend on query shape, so none of them need review.
Anonymization
Research sharing without re-identification
Claims data goes to a research partner with identifiers suppressed and k-anonymity enforced, so no patient is re-identifiable.
Synthetic Data
Dev, test and fine-tuning with no real data
Artificial records match the schema and statistics of real data, leaving a model nothing real to memorize.
One query
SELECT c.name, c.ssn, SUM(o.total) AS spend
FROM customers c
JOIN orders o ON o.ssn = c.ssn
WHERE c.id = 4417
GROUP BY c.name, c.ssn;Authorized support rep
Clear- name
- John Smith
- ssn
- 078-05-1120
- spend
- $1,204.50
Real value
Support copilot
Token- name
- John Smith
- ssn
- 412-93-7586
- spend
- $1,204.50
SSN-shaped token, join still works
Analytics service
Masked · Token- name
- J**** S****
- ssn
- 412-93-7586
- spend
- $1,204.50
Name masked, join on the token still works