Synthetic Test Data Platforms
What’s the process to run a pilot with Tonic (security review, success criteria, timeline)?
12 min read
Most teams evaluate Tonic for the same reason: they need production-like data in dev and AI workflows, but they can’t keep copying raw production into lower environments. A pilot is the fastest way to prove that you can get high‑fidelity, compliant test data without slowing engineering down—or blowing up your security posture.
Quick Answer: A Tonic pilot typically runs 4–8 weeks and follows a clear path: initial scoping, security & compliance review, environment setup, implementation, and success evaluation. Along the way, we align on concrete success criteria (e.g., time‑to‑refresh, defect rates, AI data readiness) and work through your security review with SOC 2 Type II, HIPAA, GDPR support, and deployment options (cloud or self-hosted).
The Quick Overview
- What It Is: A structured, time-boxed evaluation where Tonic runs against your real schemas and workflows—under your security and compliance guardrails—to prove it can deliver safe, production-like data for development, testing, and AI.
- Who It Is For: Engineering, data, and security teams at organizations that handle sensitive data (PII, PHI, PCI, financial, customer) and need to validate Tonic in their own environment before a full rollout.
- Core Problem Solved: Proving you can replace unsafe production copies and brittle DIY masking with high-fidelity, privacy-safe data—without slowing releases or AI initiatives.
How It Works
The pilot is designed like any good engineering project: clear scope, tight feedback loops, and an exit criteria you can measure. You’re not “clicking around in a generic demo environment”; you’re seeing your own schemas, entities, and workflows transformed into usable, compliant data.
A typical process looks like this:
- Scoping & Design: Define target workflows, datasets, and success criteria.
- Security Review & Environment Setup: Complete security/compliance due diligence and choose your deployment model.
- Implementation, Tuning & Evaluation: Configure Tonic on your data, run dev/test/AI workflows, and measure impact against the agreed criteria.
Let’s break down each phase.
1. Scoping & Design
This is where we translate “we need safer test data” into a concrete pilot plan.
What happens in this phase
- Use case selection
- Pick 1–3 high‑value workflows, for example:
- Hydrating a staging environment for a key application.
- Powering QA/regression suites that are currently blocked on data.
- Preparing unstructured data (tickets, call logs, emails) for RAG or LLM evaluation.
- Generating a synthetic dataset for a new product or demo environment.
- Pick 1–3 high‑value workflows, for example:
- Data surface definition
- Identify systems involved:
- Relational DBs (Postgres, MySQL, SQL Server, Oracle, Snowflake, etc.).
- Data warehouses and lakes.
- Unstructured sources (PDFs, DOCX, email archives, logs) for Textual.
- Decide whether you’re:
- Transforming production data (Tonic Structural).
- Generating from scratch (Tonic Fabricate).
- Redacting/tokenizing unstructured data (Tonic Textual).
- Identify systems involved:
- Success criteria alignment
- Lock in quantitative and qualitative goals, such as:
- Time to produce a compliant test dataset (e.g., from days/weeks to hours).
- Developer productivity: number of test runs or environments refreshed per week.
- Quality: reduction in escaped defects tied to data realism.
- AI readiness: ability to safely ingest previously blocked data into RAG/LLM workflows.
- Lock in quantitative and qualitative goals, such as:
- Pilot plan & timeline
- Define:
- Scope (schemas, number of tables/files, target workflows).
- Owners (engineering, data, and security stakeholders).
- Approximate timeline (often 4–8 weeks, depending on security review and data complexity).
- Define:
Output of this phase
- A written pilot plan: use cases, data sources, success criteria, responsibilities, and target milestones.
2. Security Review & Environment Setup
Before you point a privacy product at sensitive data, security has to be comfortable. That’s by design.
Security & compliance review
Your security and risk teams typically validate:
- Certifications & posture
- SOC 2 Type II.
- HIPAA‑ready for healthcare data.
- GDPR-aligned processing practices.
- AWS Qualified Software for Tonic Cloud.
- Deployment options
- Tonic Cloud: Hosted, fully managed, no install required. Backed by the certifications above, with strict isolation controls.
- Self‑hosted: Deploy in your own VPC/data center if required by policy or regulation.
- Data handling & access
- How Tonic connects to source systems (network paths, IAM roles, service accounts).
- How credentials are stored and rotated.
- Logging and audit trails for data access and transformations.
- Encryption in transit and at rest.
- Data privacy controls
- How sensitive fields are detected (built‑in classifiers, NER-powered entity detection, custom rules).
- How transforms (masking, synthesis, tokenization) are configured and governed.
- How reversible techniques (e.g., format-preserving encryption, reversible tokenization) are controlled and audited.
We support this with:
- Security whitepapers and architecture diagrams.
- Data flow diagrams for your specific pilot.
- Answers to detailed questionnaires from security, compliance, and legal.
Environment selection & setup
You decide how to run the pilot:
- Cloud pilot: Fastest path; teams often go from contract to first dataset in days because “there was nothing for us to install.”
- Self-hosted pilot: Slightly more upfront work, but keeps the entire data plane in your environment.
Typical setup steps:
- Network & identity
- Configure VPC peering / private link / VPN as needed.
- Set up SSO/SAML and role‑based access control for Tonic users.
- Source & target connections
- Add connections to your databases, warehouses, or file stores.
- Configure where transformed/synthetic data will land (separate DB, schema, bucket).
- Access scoping
- Limit Tonic’s access to pilot‑specific databases or schemas.
- Optionally point at a small but representative subset of tables/files to start.
Output of this phase
- Security review completed (or at least clearly scoped and in motion).
- Tonic deployed and connected to pilot data sources and destinations.
- Access, identity, and audit logging configured.
3. Implementation, Tuning & Evaluation
This is where your team sees how Tonic behaves on your actual data—and whether it hits the bar for both realism and privacy.
3.1 Initial configuration and first run
Working with your engineers and data owners, we:
- Map sensitive data
- Use automatic detection to flag likely PII/PHI/PCI fields.
- Layer in domain context: custom sensitivity rules, tagging proprietary identifiers (e.g., customer IDs, account numbers, claim IDs).
- Choose transformation strategies
- Structured data (Tonic Structural):
- Deterministic masking or format-preserving encryption for IDs and keys.
- Synthetic generation for names, addresses, and other direct identifiers, preserving formats and distributions.
- Differential privacy controls when appropriate.
- Subsetting with referential integrity so you can shrink an 8 PB footprint down to a 1 GB dataset while keeping joins working.
- From-scratch synthetic (Tonic Fabricate):
- Use the Data Agent to describe target schemas and behaviors.
- Generate relational synthetic datasets, mock APIs, or realistic artifacts for demos and new environments.
- Unstructured data (Tonic Textual):
- Use NER-powered pipelines to detect entities (names, SSNs, addresses, emails).
- Apply redaction, irreversible masking, or reversible tokenization.
- Optionally swap in synthetic replacements to keep semantic context for RAG and LLMs.
- Structured data (Tonic Structural):
- Run an initial pipeline
- Execute a first “end-to-end” run against your pilot data:
- Connect → detect → transform/synthesize → write to target.
- Validate:
- Joins and foreign keys still work.
- Applications and test suites can connect and run.
- Sensitive fields are no longer re-identifiable under your risk model.
- Execute a first “end-to-end” run against your pilot data:
3.2 Tuning for utility and workflow fit
High‑fidelity test data is a balancing act between privacy and utility. The pilot is where you tune that balance.
- Utility checks
- Run application smoke tests against the Tonic dataset.
- Run key QA/regression suites to confirm:
- No broken foreign keys.
- No unexpected nulls or type mismatches.
- Performance and cardinalities resemble production.
- Privacy checks
- Validate that direct identifiers are removed or irreversibly transformed.
- Confirm that quasi‑identifiers have been sufficiently generalized or synthesized.
- Ensure reversibility (where used) is tightly controlled and auditable.
- Workflow integration
- Decide how Tonic fits your CI/CD:
- Scheduled environment refreshes.
- On‑demand dataset generation for feature branches.
- Data preparation ahead of AI experiments.
- Integrate via:
- UI for ad-hoc runs.
- REST API or Python SDK for automated pipelines.
- Snowflake Native App where applicable.
- Decide how Tonic fits your CI/CD:
Tuning cycles are fast—often measured in hours or days. Customers regularly see outcomes like “test data 75% faster” or “20x faster regression testing” once their pipelines are dialed in.
3.3 Evaluation against success criteria
Before the pilot ends, we explicitly measure outcomes against the criteria agreed in phase 1.
Common measurements:
- Speed & velocity
- Old vs. new time to refresh staging/QA.
- Number of test cycles per sprint before vs. after.
- Developer time saved (e.g., 600 hours reclaimed from manual data wrangling).
- Quality
- Defects attributable to “bad test data” pre‑pilot vs. in pilot environments.
- Ability to reproduce production bugs reliably in Tonic‑powered environments.
- AI readiness
- Volume of data newly eligible for RAG/LLM use because it’s de‑identified.
- Time to prep an AI training/eval dataset compared to manual redaction/tokenization.
- Risk & compliance posture
- Reduction in uncontrolled production data copies.
- Confirmation that dev/staging/AI workflows align with privacy policies and regulatory requirements.
Output of this phase
- A concrete evaluation on: did Tonic hit the speed, safety, and utility bar you set at the start?
- A recommended rollout plan if you decide to proceed (additional systems, teams, and workflows).
Features & Benefits Breakdown
| Core Feature | What It Does | Primary Benefit |
|---|---|---|
| High-fidelity de-identification & synthesis (Structural) | Transforms production databases into referentially intact, statistically realistic datasets, preserving cross-table consistency while removing sensitive details. | Enables developers and QA to test against production-like data without exposing PII/PHI, reducing escaped defects and staging friction. |
| From-scratch synthetic generation via Data Agent (Fabricate) | Lets teams describe the data they need and generates fully relational synthetic databases, mock APIs, and artifacts for dev, demos, and AI experiments. | Unblocks new products and environments without waiting on production access or approvals, accelerating experimentation and release cycles. |
| NER-powered redaction & tokenization for unstructured data (Textual) | Detects sensitive entities in documents, tickets, logs, and more, then applies redaction, reversible tokenization, or synthetic replacement before AI ingestion. | Makes unstructured data safe for RAG and LLMs while preserving semantic realism, so AI initiatives can move forward without manual redaction bottlenecks. |
Ideal Use Cases
- Best for replacing unsafe production copies in dev/staging: Because it keeps referential integrity intact while systematically de-identifying sensitive data, so your apps and tests behave like production without the risk of real identities in lower environments.
- Best for unblocking AI & analytics on previously off-limits data: Because it combines structured synthesis, unstructured redaction/tokenization, and strict governance controls to safely unlock data for RAG, LLM fine-tuning, and analytics—under the scrutiny of security and compliance.
Limitations & Considerations
- Pilot scope needs to be focused: Trying to transform your entire data estate in a first pilot dilutes outcomes. Start with 1–3 high‑value workflows; expand after you’ve proven impact. We’ll help you pick the right slice.
- Transform quality depends on available signal: For extremely sparse or low‑volume fields, statistical fidelity has natural limits. In those cases, we favor privacy and functional correctness over perfect distribution matching, and we’ll be explicit about the tradeoffs.
Pricing & Plans
Pricing for a pilot and ongoing use depends on:
- Scope of data and systems: Number of sources, tables, schemas, and unstructured repositories.
- Deployment model: Tonic Cloud vs. self‑hosted.
- Use cases: Structured de-identification/synthesis, from‑scratch generation, unstructured redaction/tokenization, or a combination.
We’ll outline pricing during the initial scoping conversation so you know what a successful pilot would roll into.
- Team / Department Plan: Best for engineering or data teams needing to de-identify or synthesize specific applications or pipelines, often starting with 1–2 systems and expanding as they prove value internally.
- Enterprise Plan: Best for organizations needing to standardize test data and AI safety across multiple business units, with centralized governance, SSO/SAML, and deployment flexibility (cloud or self-hosted) baked in.
Frequently Asked Questions
How long does a typical Tonic pilot take from start to finish?
Short Answer: Most pilots run 4–8 weeks, depending on your security review and data complexity.
Details:
If your organization can move quickly on security and procurement, you can often get from kickoff to meaningful results in ~4 weeks:
- Week 1: Scoping, success criteria, and environment selection.
- Weeks 2–3: Security review, environment setup, and first data runs.
- Weeks 3–4: Tuning, workflow integration, and evaluation.
Larger enterprises with more extensive security questionnaires or complex data estates may run closer to 6–8 weeks. The critical path is usually security review and internal coordination—not Tonic’s technical setup, which is typically straightforward.
What do security and compliance teams usually need to approve before a pilot?
Short Answer: They validate certifications, data handling practices, deployment model, and access controls, often using a standard vendor risk questionnaire.
Details:
Security teams generally focus on:
- Certifications & attestations: SOC 2 Type II, HIPAA readiness, GDPR-aligned controls, AWS Qualified Software for cloud.
- Architecture & data flows: How data moves between your environment and Tonic, especially for cloud deployments.
- Access & identity: SSO/SAML, RBAC, how admin and API access is granted and audited.
- Data protection: Encryption, key management, backup and retention policies, and controls on reversible techniques (e.g., format-preserving encryption, reversible tokenization).
- Compliance impact: Whether dev, QA, and AI workflows using Tonic data align with your privacy policies and regulatory obligations.
We come prepared with documentation and technical owners who can speak directly to your security, risk, and compliance teams to accelerate this phase.
Summary
Running a pilot with Tonic is an engineering exercise, not a leap of faith. You define the workflows that matter, security gets the visibility and control it needs, and we prove—using your real schemas and applications—that you can have both speed and safety:
- High‑fidelity, referentially intact data for dev, QA, and AI.
- Fewer uncontrolled production copies and privacy headaches.
- Faster release cycles and unblocked AI initiatives, backed by measurable outcomes.
Once the pilot confirms that Tonic works in your environment, expanding to additional systems and teams becomes a straightforward rollout, not another science project.