Data tokenization

1) What it is and why

Tokenization - replacing sensitive values ​ ​ (PII/financial) with unclassified tokens, from which it is impossible to restore the source without access to a separate service/keys. In iGaming, tokenization reduces the radius of exposure to leaks and the cost of compliance, simplifies work with PSP/KYC providers, and allows analytics and ML to work with data without direct PII.

• Minimize storage of "raw" PII/financial data.
• Limit PII delivery by services and logs.
• Simplify compliance (KYC/AML, payments, privacy, local laws).
• Maintain data suitability for analytics/ML through stable tokens and deterministic schemas.

2) Tokenization vs encryption

Encryption: reversible conversion; protects during storage/transit, but the secret remains in the data (you need a key).
Tokenization: the source is replaced with a reference identifier (token); the original is stored separately (vault) or not at all (vaultless FPE/DET).

Combining: PII → token, the original in the safe is encrypted with HSM/KMS; token in products/logs, detokenization only in the "clean zone."

3) Types of tokenization

1. Vault-based (classic):

Source ↔ Token Mapping Store.
Pros: flexible formats, easy detokenization, access control and auditing.
Cons: Security deposit box (latency/SPOF) dependency, scaling and DR require discipline.

2. Vaultless/cryptographic (FPE/DET):

Format-preserving encryption (FPE) or deterministic encryption (DET) without mapping tables.
Pros: no safe, high performance, stable tokens for joynes.
Cons: key rotation and recall are more difficult, fine-tuning crypto parameters.

3. Hash tokens (with salt/pepper):

One-way conversion for mappings (match/link) without reversibility.
Pros: cheap and fast; good for de-dup in MDM.
Cons: no detokenation; collisions and attacks without reliable salt.

4) Tokenization objects in iGaming

KYC: passport/ID, document number, date of birth, address, phone number, email, selfie biometrics (template or storage ID from the vendor).
Payments: PAN/IBAN, wallets, crypto addresses (including check amounts/format).
Account/contacts: full name, address, phone, e-mail, IP/Device ID (with reservations).
Operational analytics: complaints, tickets, chats - text fields are edited/masked + tokenized in links.
Logs/trails: blocking PII; allow tokens/hashes.

5) Architectural patterns

5. 1 Zones and routes

Restricted: token safe, HSM/KMS, detokenation, strict RBAC/ABAC.
Confidential/Internal: Business Services, Analytics/ML; work only with tokens/aggregates.
Edge (Edge/PSP/KYC): integrations; PII gets either immediately into the safe, or remains with the vendor and is replaced by the supplier's reference token.

5. 2 Contracts and schemes

Data Contracts describe: where PII is prohibited, where a token is allowed, the type of token (format, length, FPE/UUID), validation rules and version compatibility.
Schema Registry: labels' pii: true ',' tokenized: true ', field sensitivity class.

5. 3 Determination and Joyns

For stable joins between domains, use deterministic tokens (FPE/DET) or persistent pepper hashes.
For UI/support - random opaque tokens + audit requests for reverse conversion.

6) Keys, safes and detokenization

Key storage: KMS/HSM, rotation, rights delimitation, double control.
Token safe: failover cluster, replication between regions, "break-glass" procedure with multifactor confirmation.
Detokenization: only in the "clean zone," according to the principle of least rights; temporary access tokens (Just-In-Time) and mandatory auditing.
Rotation: schedule for keys (crypto-shredding for revocation), re-tokenization policies, "dual-read" period.

7) Integrations: KYC/AML, PSP, providers

KYC providers: keep only tokens on their records/files; source scans - either from the vendor or in the offline storage of the "clean zone."

PSP: PAN never hits the kernel; use the PSP token + your internal token for cross-system communications.
AML/sanction lists: matches via PSI/MPC or via hashes with agreed salts at the regulator/partner (by policy).

8) Tokenization & Analytics/ML

Features are built by tokens/aggregates (example: frequency of deposits on a token payer, geo by token-IP, repeated KYC by token-ID).
For texts: NLP edition of PII + entity replacement.
For markup and A/B: the registry flags invalid PII features; policy-as-code in CI blocks PR with PII in vitrines.

9) Access policies and auditing

RBAC/ABAC: role, domain, country, purpose of processing, "for how long"; detokenization only upon request with justification.
Magazines: who and when requested detokenization, in what context, for what volume.
DSAR/deletion: we find related entities by token; when deleting - "crypto-shred" keys and cleaning the safe/backups according to the schedule.

10) Performance and scale

Hot-path: synchronous tokenization at the input (ACC/payments), token cache with TTL in "gray" zones.
Bulk-path: asynchronous retro-tokenization of historical data; "dual-write/dual-read" mode for the migration period.
Reliability: asset-safe, geo-replication, latency budget, graceful-degradation (temporary masks instead of detokenization).

11) Metrics and SLO

Coverage: The proportion of fields with 'pii: true' that are tokenized.
Zero PII in logs: percentage of logs/trails without PII (target - 100%).
Detokenization MTTR: average time to complete a valid application (SLO).
Key hygiene: timeliness of key rotation, uniqueness of pepper by domain.
Incidents: number of violations of PII policies and their closing time.
Perf: p95 tokenization/detokenization latency; availability of safe/aggregator.
Analytics fitness: the proportion of showcases/models that have successfully switched to tokens without quality degradation.

12) RACI (example)

Policy & Governance: CDO/DPO (A), Security (C), Domain Owners (C), Council (R/A).
Safe/keys: Security/Platform (R), CISO/CTO (A), Auditors (C).
Integrations (KYC/PSP): Payments/KYC Leads (R), Legal (C), Security (C).
Data/ML: Data Owners/Stewards (R), ML Lead (C), Analytics (C).
Operations and auditing: SecOps (R), Internal Audit (C), DPO (A).

13) Artifact patterns

13. 1 Tokenization Policy (excerpt)

Scope: which data classes are to be tokenized; exclusions and justifications.
Token type: vault/FPE/DET/hash; format and length.
Access: who can detokenize; application process, logging, access lifetime.
Rotation: key graph, crypto-shred, backfill/dual-read.
Logs: PII ban; penalties and playbook incident.

13. 2 Passport of the field to be tokenized

Field/Domain: 'customer _ email '/CRM

Data Class: PII/Restricted

Token type: DET-FPE (domain saved), length 64

Purpose: dedup/joyns, proxy communications

Detokenization: prohibited; only allowed for DPO by DSAR case

Related artifacts: contract, schema, DQ rules (mask, format)

13. 3 Starting checklist

• Contracts and schemas marked 'pii '/' tokenized'
• Safe/HSM deployed, DR/BCP plans ready
• CI linters block PII in code/SQL/logs
• Test suite: lack of PII in logs/hoods, correctness of format masks
• Coverage/Zero-PII/Perf dashboards configured
• Trained teams (KYC/Payments/Support/Data/ML)

14) Implementation Roadmap

0-30 days (MVP)

1. Inventory of PII/financial fields and flows; classification.
2. Selection of critical paths (KYC, payments, logs) and type of tokens (vault/FPE).
3. Deploy a safe with HSM/KMS, implement tokenization at the KYC/PSP input.
4. Enable linters/log masking; Zero-PII monitoring.
5. Tokenization policy and detokenization process (applications, audits).

30-90 days

1. Retro tokenization of stories in CRM/billing/tickets; dual-read.
2. Deterministic tokens/hashes for MDM and analytics; adaptation of joynes.
3. Rotation of keys on schedule; dashboards Coverage/Perf/SLO.
4. Integration with DSAR/deletion (by token and graph).
5. Playbook of incidents and exercises (table-top).

3-6 months

1. Extension to providers/partner channels; reference tokens from external vendors.
2. Inclusion of PSI/MPC for non-PII sanctioned matches.
3. Full window/ML coverage on tokens; rejection of PII in production logs and tracks.
4. Compliance audit and annual recertification of processes.

15) Anti-patterns

"Tokens in logs, originals - also in logs": logging without masks/filters.
Detokenization on the application side "for convenience" without audit.
Single/pepper key for all domains and regions.
No key rotation and crypto-shred plan.
FPE without format/alphabet control → failures in third-party systems.
Tokenization without changes in analytics/ML → broken joyns and metrics.

16) Connection with neighboring practices

Data Governance: policies, roles, directories, classification.
Origin and data path: where tokens are created/detokenized, PII trace.
Confidential ML/Federated Learning: Training on Tokens/Aggregates, DP/TEE.
Ethics and reducing bias: proxy PII exclusion, transparency.
DSAR/Legal Hold: delete/freeze by tokens and keys.
Data observability: Zero-PII in logs, freshness of token streams.

Result

Tokenization is not "cosmetics," but a basic layer of security and compliance. The correct architecture (zones, safe/HSM, deterministic tokens for analytics), strict processes (accesses, audits, rotation) and discipline in the logs make the platform leak-resistant, and the data useful without unnecessary risks.