DPIA: Privacy Impact Assessment

1) What is DPIA and why is it needed

• Confirm the legality and proportionality of the processing.
• Identify and mitigate risks to entities (confidentiality, discrimination, financial/reputational harm).
• Embed privacy by design/default into architecture and processes.

2) When DPIA is mandatory (typical triggers)

• Large-scale profiling and automated solutions (fraud scoring, RG scoring, limits).
• Biometrics (selfie-liveness, face-match, face templates).
• Systematic monitoring of user behavior (end-to-end telemetry/SDK).
• Processing of vulnerable groups (children/adolescents, financially vulnerable).
• A combination of datasets allowing deanonymization/inference.
• Cross-border transmissions to countries with non-equivalent protection (in conjunction with DTIA).
• New technologies (AI/ML, graph models, behavioral biometrics) or a sharp change of goals.

3) Roles and Responsibilities (RACI)

Product/Business Owner - initiates DPIA, describes goals/metrics, risk owner.
DPO - Independent Review, Methodology, Residual Risk Validation, Surveillance Linkage.
Security/CISO - technical control, threat modeling, incident response plan.
Data/Engineering - data architecture, pseudonymization/anonymization, retention.
Legal/Compliance - processing grounds, processor contracts, cross-border transfer terms.
ML/Analytics - explainability, bias audit, model drift control.
Privacy Champions (by command) - collection of artifacts, operational checklists.

4) DPIA pattern: artifact structure

1. Description of processing: goals, context, categories of PD/subjects, sources, recipients.
2. Legal basis and proportionality: why this data is justified.
3. Risk assessment for subjects: scenarios of harm, probability/influence, vulnerable groups.
4. Mitigation measures: tech/org/contractual, before and after implementation.
5. Residual risk: classification and decision (take/reduce/recycle).
6. DTIA (when transferred abroad): legal environment, additional measures (encryption/keys).
7. Monitoring plan: metrics, reviews, revision triggers.
8. Conclusion of the DPO and, in case of high residual risk, consultation with supervision.

5) Evaluation method: probability × impact matrix

• Probability: Low (1 )/Medium (2 )/High (3).
• Impact: Low (1 )/Significant (2 )/Severe (3).

• 1-2 - low (accepted, monitoring).
• 3-4 - controlled (measures required).
• 6 - high (enhanced measures/processing).
• 9 - critical (prohibition or consultation with supervision).

Examples of harm scenarios: disclosure of PD, discrimination due to profiling, financial damage in ATO/fraud, harm to reputation, stress from aggressive RG interventions, "hidden" surveillance, reuse of data by third parties.

6) Catalogue of mitigation measures (constructor)

Legal/Organizational

Goal limiting, field minimization, RoPA and Retention Schedule.
Profiling/explainability policies, appeal procedure.
Staff training, four eyes for sensitive decisions.

Technical

Encryption in transit/at rest, KMS/HSM, key separation.
Aliasing (stable tokens), aggregation, anonymization (where possible).
RBAC/ABAC, JIT accesses, DLP, download monitoring, WORM logs.
Private computing: client-side hashing, restriction of joynes, diffusivity for analytics.
Explainability for ML (reason codes, model versions), bias protection, drift control.

Contractual/Vendor

DPA/usage restrictions, prohibition of "secondary targets," sub-processor registry.
SLA incidents, notifications ≤72 h, audit rights, processing geography.

7) Special cases for iGaming/fintech

Fraud scoring and RG profiling: describe logic at the level of signal categories, reasons for decisions, the right to review by a person; thresholds and "soft" interventions.
Biometrics (selfie/liveness): store templates, not raw biometrics; tests on a spoof set, a double circuit of providers.
Children/adolescents: "best interests," prohibition of aggressive profiling/marketing; parent consent for <13.
Cross-border payouts/processing: encryption before transmission, key allocation, field minimization; DTIA.
Combining behavioral and payment data: strict segregation of zones (PII/analytics), cross-joins only for DPIA exceptions and for stated purposes.

8) Example of DPIA fragment (tabular)

9) Integration of DPIA into SDLC/roadmap

Discovery: privacy-triage (are there triggers?) → DPIA decision.
Design: collecting artifacts, threat modeling (LINDDUN/STRIDE), selecting measures.
Build: privacy checklists, data minimization/isolation tests.
Launch: DPIA final report, sign-off DPO, trained DSR/incident processes.
Run: metrics, access audit, DPIA revision by triggers (new targets/vendors/geo/ML models).

10) Quality metrics and operational controls

DPIA Coverage: Proportion of risk treatments with relevant DPIA.
Time-to-DPIA: median/95th percentile from feature start to sign-off.
Mitigation Completion:% of implemented measures from the plan.
Access/Export Violations: unauthorized accesses/uploads.
DSR SLA and Incident MTTR for related processes.
Bias/Drift Checks: frequency of audits and results for ML solutions.

11) Checklists (ready to use)

DPIA start

• Goals and reasons for processing are defined.
• Classified data (PII/sensitive/children).
• Identified subjects, vulnerable groups, contexts.
• A map of streams and data zones is drawn.

Assessment and measures

• Harm scenarios identified, V/I, risk matrix.
• Selected measures: legal/technical/contractual; are fixed in the plan.
• A bias audit/exploit of models (if profiling is available) was carried out.
• DTIA conducted (if cross-border transmissions are available).

Finalization

• Residual risk calculated, owner fixed.
• DPO conclusion; if necessary - consultation with supervision.
• Revision metrics and triggers are defined.
• DPIA is located in the internal repository, included in the release checklist.

12) Frequent mistakes and how to avoid them

DPIA "after the fact" → embed in discovery/design.
Shift to security and ignore the rights of subjects → balance measures (appeals, explainability, DSR).
Generalized descriptions without specifics of data/streams → risk missing vulnerabilities.
No vendor control → DPA, audit, environment and key restriction.
No revision → Assign frequency and trigger events.

13) Artifact package for wiki/repository

DPIA template. md (with sections 1-8).
Data Map.
Risk Register.
Retention Matrix and profiling policy.
DSR procedure and IR plan templates (incidents).
Vendor DPA Checklist and list of sub-processors.
DTIA pattern (if there are transmissions).

14) Implementation Roadmap (6 steps)

1. Identify "high risk" triggers and thresholds, approve DPIA template.
2. Assign DPO/Privacy Champions, negotiate RACI.
3. Embed privacy-gate into SDLC and release checklists.
4. Digitize DPIA: a single register, reminders of revisions, dashboards.
5. Train teams (PM/Eng/DS/Legal/Sec), conduct pilots on 2-3 features.
6. Quarterly review of residual risks and KPIs, update of measures and templates.

Result

DPIA is not a tick, but a manageable cycle: risk identification → measures → residual risk verification → monitoring and revision. By integrating DPIA into design and operation (with DTIA, vendor control, explainability and metrics), you protect users, comply with regulatory requirements and reduce legal/reputational risks - without losing product speed and UX quality.