Unlock AI and Analytics Without Exposing Sensitive Data
Use data with confidence and give teams the access they need while keeping sensitive records controlled across BI, data science, and LLMs
Explore the TAMUNIO platformA Data-Centric Path to Safe AI
AI and analytics move fast, until sensitive data enters the picture. Programs need access, regulators need assurance, and security needs control. The result is too many copies, too much clear text, and too many delays.
Sensitive elements such as personally identifiable information (PII) and secrets are scattered across lakes, warehouses, notebooks, and prompts. Copying readable data into vector databases or LLMs multiplies risk and complicates compliance.
TAMUNIO protects first, then enables access that respects policy. Keeping originals tightly governed and letting the work happen on de-identified datasets that remain useful for development, analytics, and AI
How TAMUNIO Enables Safe AI and Analytics
- Discover and classify: Identify sensitive elements across lakes, warehouses, notebooks, and prompts so the right protections apply automatically.
- Protect at the source: Safeguard sensitive data before it is shared or moved so it arrives already secured.
- Work on de-identified datasets: Let analytics, ML, and AI operate on usable substitutes while originals stay tightly governed.
- Secure data in use: Use Confidential Computing for processing workloads that require originals, keeping control even while data is being computed.
- Unify visibility: Stream all activity into a single view and SIEM integration to answer who accessed what and why.
Move From Idea to Insight Without Widening Risk or Audit Scope
- Actionable insights: Analyze broader datasets while keeping sensitive details minimized and tightly controlled.
- AI operational agility: Give teams the freedom to innovate without adding approvals or exceptions, delivering faster insight with less friction.
- Lower breach impact: By reducing where readable data exists, the value of any stolen records is reduced, shrinking your exposure footprint.
- Simplify compliance: Centralized evidence supports audits, model governance, and privacy reviews, without the complexity.
- Consistency across platforms: Apply the same policies and oversight from on-prem to cloud to SaaS, avoiding drift and reducing rework as you scale.
Common Use Cases
Analytics and BI
Query de-identified datasets and reveal originals only when authorized.
Data Science and ML
Train and validate models without distributing readable customer or payment data.
GenAI and RAG
Secure data before embedding, filter prompts and responses, and control reveal with your own keys.
Environments and Operations
AI workloads running on protected data during migrations and releases.
Data Sharing and Third Parties
Share insights without raw identifiers; pseudonymize before external API calls, minimize readable data in transit, and revoke via key rotation when needed.