SPARKANALYTICS

AI-ready data. No real people.

We mask and pseudonymise production data so your apps and AI can be built and tested in pre-production on realistic data, without exposing a single customer.

Book a demo See how AI reacts

Nearly 10 years of enterprise data projects in telecom, banking, the public sector and healthcare regulation.

Customer record

Production
Name
Marko Petrović
Personal ID
0101985710023
IBAN
RS35 2600 0560 1001 6113 79
Phone
+381 64 123 4567
Customer key
C-104552

Real person. Can't go to test, a vendor or an AI tool.

AI multiplies copies of personal data

Before AI, a test database was one copy of production. Now the same data also lands in prompts, logs, vector stores and external AI tools, usually with weaker controls than production.

Production databaseprotected and audited
  • Pre-production and test
  • AI prompts
  • Logs and traces
  • Vector stores and embeddings
  • External AI services and vendors

€20M or 4%

Maximum GDPR fine, whichever is higher of global annual turnover (Art. 83).

Still personal data

Pseudonymised data stays personal data under GDPR, so every copy carries the same obligations (Recital 26).

Assessed case by case

European regulators don't assume AI models built on personal data are anonymous (EDPB Opinion 28/2024).

Your AI can't tell the difference

Masked data keeps formats, relationships and patterns, so the same AI question gets an equally good answer. Try both datasets.

Question to the AI assistant: Which customers in Novi Sad had failed payments last month? Draft an apology email to the top three.

    Illustrative example on synthetic data. In a pilot we prove it with an AI parity report: the same evaluation run on original and masked data.

    Your AI can't tell the difference. Your regulator can.

    From discovery to AI-ready environments

    Masking happens before data reaches a prompt, an embedding or a vendor. Your DPO says yes, and your AI keeps working.

    1. Discover

      Our accelerator finds personal data across your databases, including free text.

    2. Propose

      A masking or pseudonymisation rule for every field, approved by your DPO.

    3. Mask

      Rules applied consistently across every system and environment.

    4. Validate

      AI parity: the same evaluation on original and masked data.

    5. Repeat

      Every refresh runs the same audited process, with a report.

    Enterprise-grade masking at scale

    Built for large relational databases and high data volumes in regulated industries.

    Referential integrity

    The same person gets the same pseudonym in every table, database and document, so joins, RAG retrieval and agent flows keep working.

    Format-preserving

    Masked IDs, IBANs and card numbers stay valid, checksums included. When data must be reversible, we use encryption with keys that stay with you.

    Subset and mask

    Load a complete, consistent slice into test or AI environments instead of copying terabytes of production.

    Repeatable and auditable

    Central rules reused on every refresh, with reports your DPO and auditors can read.

    One masking policy across everything

    One layer covers your databases and AI pipelines. A customer masked in the core system is masked the same way in the warehouse and in the AI index.

    OracleSQL ServerDb2TeradataPostgreSQLMySQLSybaseInformixVector storesAI and RAG pipelines

    Our accelerator finds what manual analysis misses

    • Finds hidden personal data

      Including phone numbers and IDs typed into free-text columns.

    • Proposes the right rule

      Masking or pseudonymisation per data type, ready for approval.

    • Configures the platform

      Approved rules become the masking setup: discovery in days, not weeks.

    Scan result: CUSTOMER schema (example)
    ColumnDetectedRule
    FULL_NAMENamePseudonym
    JMBGPersonal IDFormat-preserving
    IBANBank accountFormat-preserving
    ADDRESSAddressShuffle
    NOTEPhone in free textRedact pattern

    Nearly 10 years in regulated, high-volume industries

    Successful masking projects on all major relational databases and large data volumes.

    Telecom

    Subscriber data across multiple environments.

    Banking

    Client and transaction data under banking regulation.

    Public sector

    Citizens' records at national scale.

    Health regulator

    Regulated medicines and health data.

    Beyond masking, we build databases and data warehouses, ETL pipelines and data lakes, data science and ML, LLM and RAG solutions, full-stack applications and DevOps.

    Where data make sense

    No man is an island, entire of itself; every man is a piece of the continent, a part of the main.John Donne

    In a world that still sees data as zeros and ones, impersonal and disconnected, we look for the meaning in it and connect it into a whole, where every piece finds its purpose.

    Masked data still makes sense. It just no longer points to anyone.

    We believe every data dream can become an insight. We are here to help.

    Start with a two-week AI data readiness assessment

    • Where personal data sits outside production and in AI pipelines
    • A masking and pseudonymisation plan
    • An AI parity pilot with a fixed estimate
    Book a demo info@sparkanalytics.net