Pillar 01
Multi-Source Parity
Databases: Oracle, SQL Server, PostgreSQL. Files: CSV, JSON, XML—the same deterministic rules whether rows arrive over JDBC or bulk ingest.
Ninja has no face. Neither will your sensitive data.
Enterprise-grade anonymization for PostgreSQL, Oracle, and SQL Server on Azure—engineered for finance, banking, and insurance teams across the DACH region. GDPR-aligned controls that hold up under supervisory review.
Built for engineers. Trusted by the CISO.
Raise-ready
Enterprise Trust & DACH Compliance Technical maturity signals for accelerator committees — details to follow.
Across Swiss banking and continental insurance, we kept watching the same failure mode: teams treating sensitive data access like a “pinky swear”—handshake NDAs, heroic manual exports, and ticket-by-ticket exceptions instead of enforceable controls.
Risk and privacy officers weren’t blocking AI out of spite; they withheld production paths because nobody could show consistent minimization at the wire. Every pilot became a bespoke negotiation—slow, brittle, and impossible to audit under supervisory scrutiny.
AnonifyDB replaces that manual, high-risk posture with automated engineering certainty: deterministic policies, reproducible transformations, and SIEM-grade evidence—so innovation ships without gambling on tribal knowledge.
Dual operating modes, surgical or full-database masking, and one-click provisioning—built for regulated Azure estates, AI-assisted workloads, and audit-ready evidence in banking and insurance.
Pillar 01
Databases: Oracle, SQL Server, PostgreSQL. Files: CSV, JSON, XML—the same deterministic rules whether rows arrive over JDBC or bulk ingest.
Pillar 02
AES-256 encryption on connections. Sharp, frost-line isolation for workloads that cannot tolerate ambiguity.
Crucial
Sensitive data never leaves the client’s tenant.
Pillar 03
Expose real cohort patterns to models without identifiable subjects—velocity without dancing on the edge of GDPR fines.
Production-aligned masking for supervisory environments: dual operating mode (proxy vs. at-rest), programmatic delivery via API for agents and CI/CD, and one-click deployment into isolated targets—without sacrificing join fidelity or audit evidence.
ADB proxy (in-transit)
Real-time anonymization as data moves from the database to applications, APIs, or AI agents—sensitive values never reach downstream consumers in the clear.
At-rest anonymization
Permanent masking for dormant databases and bulk copies—ideal for structural twin environments where development teams need faithful schemas without production identifiers.
Partial control
Select specific tables or columns—for example email, IBAN, or national identifiers—while leaving analytical dimensions untouched for targeted minimization.
Full database masking
Complete anonymization of the estate with referential integrity preserved: the same logical ID maps to the same pseudonym across every table, so joins and lineage remain trustworthy.
Single action kicks off the governed pipeline: Scan → Plan → Execute. The platform provisions a fresh database or schema—such as an Azure SQL instance—and loads anonymized data directly into your non-production target so testers and AI sandboxes stay isolated from production.
Operational ergonomics for large estates: fewer runbooks, fewer hand-offs, reproducible drops for every sprint or model experiment.
5× faster engineering cycles
Versus synthetic-only data or blocked production paths.
Preserved logical graph
Oracle · SQL Server · PostgreSQL · CSV / JSON / XML pipelines.
Zero Risk posture
Identifiers stay off the wire to downstream AI systems.
Full Vision, Zero Risk
For lead engineers & platform security
AnonifyDB functions as a data delivery layer: provision governed, anonymized databases through contracts your toolchain already understands—without queueing weeks for manual compliance sign-off on every refresh.
Policy gates remain authoritative; execution becomes repeatable, observable, and safe for autonomous tooling.
One authenticated request can trigger the full scan → plan → execute pipeline and return a ready-to-use database endpoint—aligned with the same policies your CISO approved for batch and proxy paths.
Illustrative
POST /v1/instances/anonymized { "source": "prod_ledger", "policy": "dach_qa_v3" } → 201 { "connectionString": "Server=…;Database=qa_tw…" }
Whether the caller is an engineer in a terminal or an AI coding agent inside an autonomous loop (for example Cursor or GitHub Copilot-driven workflows), the integration surface is identical: request an environment, receive a fresh anonymized connection string and bounded credentials—no shadow exports.
Security architecture
Two masking depths for two threat models—chosen per workload, dataset, or agent trust boundary. Chief Information Security Officers retain policy ownership; lead engineers select the mode that matches refactoring vs. third-party AI exposure.
Table and column names stay recognizable so ORMs, stored procedures, and migration scripts keep meaning. Cell values are masked or pseudonymized—ideal when teams must refactor business logic against a truthful relational shape.
For external or opaque AI agents, obfuscate identifiers at both layers: payload and metadata. Table Customers becomes Entity_A; column Email becomes Attr_1—so the model gains no recoverable map to your business domain.
Use standard mode for internal engineering fidelity; escalate to structural mode when tooling crosses trust zones or when regulators expect proof that domain semantics never left your boundary.
Enterprise · supervisory-grade controls
Ship at FinTech cadence without outrunning legal: cryptographic evidence, incremental synchronization, interactive Trust-Check previews, live egress enforcement, and ephemeral compute—each tuned so engineering throughput and regulatory defensibility rise together.
Every anonymization run yields auto-generated, digitally signed compliance certificates—delivered as PDF artifacts bound to a cryptographic hash—so integrity and provenance are trivially verifiable.
The embedded ledger records exactly which transformations executed—for example synthetic replacement, salted hashing, truncation, or tokenization—column by column, policy version by policy version.
Why it matters: Converts “how do we trust this masking run?” into an artifact General Counsel, DPOs, and external auditors can file—without commissioning a forensic exercise after the fact.
Smart Delta Sync fingerprints production churn and applies transformations only to new or modified rows, then reconciles downstream dev and test targets—no wholesale table rewipes when a slice of the ledger moved.
Pairs with Ethereal Provisioning via API so autonomous workloads can pull governed increments without standing up brittle manual exports—purpose-built for hyperscale estates on Azure (and analogous footprints): fewer vCore-hours, leaner storage churn, and materially shorter pipeline windows versus naive full re-anonymization.
Why it matters: Keeps CI/CD loops fast and FinOps predictable when databases measure in terabytes—without relaxing the minimization bar.
An active control plane watches data exiting toward third-party AI endpoints—public LLM APIs, hosted copilots, or opaque agent runtimes—and continuously scores payloads against high-precision patterns.
If residual structures resemble IBANs, person names, government IDs, or mailbox addresses that slipped past upstream masking, the canary blocks or redacts the egress in real time—fail-closed before tokens reach an external inference tier.
Why it matters: Closes the last-mile gap where automation misconfiguration or emergent prompt injection could otherwise defeat weeks of governance design.
Security & Trust
Unidirectional Data Preview augments the interactive preview surface: administrators select live production rows and inspect—field-for-field—how those rows will materialize in the target database after policies execute, before any irreversible push.
Source · production
Preview · anonymized target
Same joins & distributions—no recoverable bridge back to source identifiers in this surface.
The moat — One-Way Masking: The architecture treats preview and target materialization as a one-way function. Neither the Trust-Check canvas nor the downstream anonymized database retains sufficient structure to reverse-engineer or de-anonymize production subjects—closing the analyst fantasy of “peek and rollback” that sinks lesser tooling.
“One API call, zero trace, total compliance.”
Developers and autonomous agents never touch a durable connection string. Instead they receive ephemeral access through Ethereal Provisioning to a Ghost database: an in-memory instance spun on demand inside your perimeter, fed only by governed masking pipelines and Smart Delta Sync where applicable.
When the session terminates, working sets evaporate—no residual pages, no orphaned replicas—preserving data-subject minimization narratives regulators actually accept.
Workflow
Authenticated lease describing scope, TTL, and policy bundle.
Ephemeral instance online
In-memory engine materializes masked projections for the session—no persistent SKU billed as permanent inventory.
Session ends → footprint erased
Lease expiry or explicit teardown destroys buffers and credentials; only Auditor’s Ledger hashes remain for supervisory replay.
Use cases · production narratives
Scenario-first snapshots of how teams adopt governed data without slowing autonomy—each pairing a concrete operational moment with the AnonifyDB control plane.
Scenario
An AI agent operating inside an autonomous development loop—think Devin-class tooling or a bespoke orchestrator—needs a fresh delta of production-shaped data at 02:00 to stress-test a newly discovered edge case. Humans are offline; ticketing lanes are frozen.
AnonifyDB · Solution
The agent invokes the AnonifyDB API; Ethereal Provisioning spins an ephemeral lease while Smart Delta Sync materializes a masked Smart Delta—only rows that moved since the last fingerprint—without human gatekeeping or compliance queue time. The loop keeps velocity; the ledger keeps evidence.
Scenario
A lead engineer must ship a bounded subset of production tables to an external outsourcing partner building a net-new surface area. Traditional workflows imply nights of manual scrubbing, brittle spreadsheets, and referential integrity breakage.
AnonifyDB · Solution
Instead of manual cleansing, the engineer selects the required schemas, invokes Export Anonymized, and ships a 100% policy-safe dataset that preserves foreign keys and cardinality narratives—referentially intact within minutes. Optional Trust-Check Preview confirms One-Way Masking outcomes before the partner ever receives bytes.
From attachment to anonymized consumption—three reproducible steps.
Securely link sources into the governed perimeter.
Define rules via Metadata—columns, entities, and policy scopes.
Real-time, context-preserving anonymization.
Finance, banking, and insurance in Germany, Austria, and Switzerland—where BaFin, FMA, and FINMA-aligned scrutiny makes anonymization a board-level control, not an engineering nicety.
Core banking, payments & risk analytics
Life, P&C, reinsurance & fraud models
Asset managers, FMIs & capital markets tech
Legal, audit & outsourcing governance
GDPR sovereignty means data stays within your governed boundary, processed under documented purpose and minimization—not repatriated through opaque AI prompts or shared inference tiers.
Raw production identifiers in LLM context or autonomous agents collide with Articles 5 and 25 obligations; supervisors treat “pilot exceptions” as institutional risk.
Against ‘BodySnatcher’ AI exposure: Copilots and agents that read live database streams can memorize or leak verbatim identifiers. Proxy-side and at-rest masking closes that aperture—models and scripts operate on pseudonymous structural twins, not exfiltratable identities.
Legal risk: Using raw production data for AI testing or LLM context is a high-risk GDPR violation. AnonifyDB converts that liability into reproducible, auditor-friendly controls.
Minimization, purpose limitation, and residency-aware processing anchored in EU and DACH supervisory expectations—with evidence that survives DPIA and outsourcing reviews.
Assume breach at every hop: authenticate callers, authorize scopes, and log access so security teams can prove who saw what.
Package and operate as a managed application so procurement, billing, and isolation match how enterprises already buy cloud software.