Use the data you can’t use.
You’re sitting on data that could power your most valuable AI: full of names, account numbers, transaction histories, claim details. Things you can’t afford to expose. Enigmata Anonymizer converts that data into something your AI can still learn from and score on, but that no longer identifies anyone. Your models train just as fast and score just as accurately as they would on the raw records, often better, on the CPUs and GPUs you already own. Regulators stop treating the result as personal data, so the projects compliance kept blocking can finally ship. The change runs one direction only. No one, including us, can reverse it.
| name | John Smith |
| acct | ACCT-4F2A-9C81 |
| ssn | 555-12-3456 |
| amount | $1,247.32 |
| name | a4b27fe1 |
| acct | 8c19d4b3 |
| ssn | f3a02e1d |
| amount | 7e3a91f4b2c8e1d5 |
8-10% faster wall-clock training reduction on representative workloads (internal benchmark).
Model accuracy on protected data is often better - never worse - than plaintext
Enigmata Anonymizer throughput.
Cryptographically irreversible: Not by us, not by a hacker, not by a subpoena.
How it compares
The honest framing of every other option.
| Approach | What you get |
|---|---|
| Masking and tokenization | You lose too much useful signal, and the original data still leaks through prompts and logs. |
| Synthetic data | The numbers drift from reality, and regulators won’t accept the lineage. |
| Secure enclaves (TEEs) | Locked to specific chips, and you still have to trust the runtime. |
| Fully homomorphic encryption | Roughly a million times slower than working on the real data. |
| Enigmata Anonymizer | Fast as plaintext. Same accuracy. Cannot be reversed. Runs on what you already own. |
Frameworks
Designed against the regulators your data lives under.
Personal-data protection regulation; Enigmata Anonymizer is designed against its anonymity standard.
Deletion mandates and consent withdrawals collide with trained-model retention.
Protected health information; Enigmata aligns with the Safe Harbor de-identification path.
California privacy law; Enigmata output meets the statutory "deidentified" definition.
CPRA-aligned state regimes adopting similar deidentification standards.
Financial institutions must protect non-public personal information.
Cybersecurity regulation for NY-licensed financial services entities.
Federal Reserve / OCC guidance on model risk management.
Internal-controls regime for financial reporting.
High-risk AI systems must document data governance and accuracy.
Voluntary AI risk-management framework increasingly cited by regulators.
Student-records privacy; deidentified records fall outside FERPA.
Personal-data protection regulation; Enigmata Anonymizer is designed against its anonymity standard.
Deletion mandates and consent withdrawals collide with trained-model retention.
Protected health information; Enigmata aligns with the Safe Harbor de-identification path.
California privacy law; Enigmata output meets the statutory "deidentified" definition.
CPRA-aligned state regimes adopting similar deidentification standards.
Financial institutions must protect non-public personal information.
Cybersecurity regulation for NY-licensed financial services entities.
Federal Reserve / OCC guidance on model risk management.
Internal-controls regime for financial reporting.
High-risk AI systems must document data governance and accuracy.
Voluntary AI risk-management framework increasingly cited by regulators.
Student-records privacy; deidentified records fall outside FERPA.
What this unlocks for you
Put your archives to work
Years of transactions, claims, contracts, and customer histories finally available for training and scoring. No more “we can’t, because of compliance.”
Share data without sharing your liability
Vendors, cloud AI services, partners, and data buyers can work with your data without ever holding anything personal. If they get breached, it isn’t your problem.
Stop running every AI project through privacy review
When the data the model sees can’t identify anyone, the months-long legal and compliance gate becomes a much shorter conversation.
No accuracy tax
Your models train as fast and perform as well as they would on the original data. Often better. No retraining tricks. No signal lost.
Why it matters
It can’t be reversed
Not by us. Not by a hacker who steals the output. Not by a court order forcing our hand. Once your data has gone through, the change is permanent and one-way.
Regulators don’t count it as personal data
Built to the anonymity standards in GDPR and aligned with HIPAA’s de-identification framework.
Runs on what you already have
The CPUs and GPUs you already own. The AI and data tools you already use. No new vendor lock-in, no specialized hardware, no model surgery.
Where it fits
Best fit: AI that scores, ranks, or flags. The aim: population-level signal without identifying any individual.
Fraud
Card-present and card-not-present fraud scoring across years of transaction history, without exposing card numbers, identities, or geo to the modeling stack.
AML
Suspicious-activity ranking on customer + counterparty data that would otherwise trip BSA review every time a notebook spins up.
Claims
Insurance claims-fraud and severity models on policyholder records that legal won’t let leave the system of record.
Underwriting
Credit and default models on full transactional and demographic features without expanding adverse-action audit scope.
Churn
Behavior-driven churn scoring on full customer histories without spreading PII into experimentation environments.
Medical imaging
Diagnostic vision models on protected ePHI-derived features; transformer attention runs on encrypted embeddings.
Vendor bake-offs
Run two or three modeling vendors against the same dataset without ever handing them raw records.
Data licensing
Sell anonymized feature sets to data buyers and retain enforceable terms via Enigmata Market + Enigmata Policy.
Key capabilities
- Irreversible, one-way privacy transform. No key escrow, no “re-identification under court order.”
- Built to the anonymity standards in GDPR and aligned with HIPAA’s de-identification framework.
- Runs on CPUs and GPUs you already own; integrates with the AI and data tools you already use.
- No accuracy tax: models train as fast and perform as well as on the original data, often better.
- Categorical bucketing for quantitative fields: replace a number with a signal-preserving band (comp tier, risk cohort, financial range) so your model keeps the predictive signal while the raw value never survives the transform.
The offer
Prove it in 60 days.
Prove it in 60 days. Pick the AI project that has been stuck the longest. Give us 60 days on your data. We’ll hit the benchmark you already care about, and produce the audit artifacts your CISO and legal team need to sign off.
- GDPR / HIPAA dossierDocumentation aligned with GDPR anonymity standards and HIPAA’s de-identification framework for the transformed dataset.
- CISO sign-off packThreat model, key-management attestation, data-flow diagram, and incident-response delta for the protected workflow.
- Model accuracy comparisonSide-by-side benchmark report: plaintext baseline versus Enigmata Anonymizer on the customer’s target metric.
- Re-identification auditAdversarial-test results: k-anonymity, l-diversity, and worst-case re-identification bounds on the protected output.
- Operational runbookIngestion pipeline, key-rotation schedule, lineage records, and audit-log hooks for the transformed dataset.