Nigeria Data Protection Act (NDPA 2023) Compliance Programme

The Problem and Why It Mattered

I designed and implemented a compliance programme under Nigeria's Data Protection Act 2023 — covering new obligations, technical controls, data inventory, DPIAs, 72-hour breach notification procedures, and cross-border transfer governance. The relevant technical and business risk is the cost of leaving this workflow, control, or product boundary unclear, manual, insecure, or difficult to operate. The case study focuses on the decisions used to make that risk manageable.

Objectives and Success Criteria

• Deliver the capability described in the project objective using a maintainable technical design.
• Make the main workflow, control boundary, and operational responsibilities explicit.
• Produce reviewable implementation evidence without publishing confidential values or customer data.

Requirements and Constraints

The implementation had to create technically useful controls and evidence without representing the product or environment itself as formally certified. Confidential organisational details are generalised.

My Role and Ownership

I owned the technical design and implementation described in this portfolio record, including the technology choices, core system boundaries, and the documentation of the resulting approach. Related organisational decisions, vendor operations, and formal certification decisions remain outside the scope of this technical case study unless stated explicitly.

Solution Overview

The solution is built around Cloud DLP, BigQuery, Security Command Center. Its implementation is organised as the components below so that the architecture, control points, and operating model can be evaluated separately rather than treated as a list of tools.

Architecture and System Design

The public architecture description follows the path from the initiating user, workload, or operator action through the relevant compliance components. Access boundaries, data or state handling, and operational controls are described at a safe level; secrets, private addresses, and client-sensitive implementation details are intentionally omitted.

Technology Decisions and Trade-Offs

I selected Cloud DLP, BigQuery, Security Command Center because they support the project objective and make the critical engineering boundaries explicit. The trade-off is operational and platform complexity: the design depends on documented configuration, controlled access, and ongoing review rather than assuming that a managed service or framework removes engineering responsibility.

Implementation Walkthrough

The following implementation notes describe the components that matter most to the project outcome. They are intentionally focused on technical decisions and operating behaviour instead of reproducing entire source files or sensitive configuration.

Implementation: NDPA 2023 vs NDPR 2019 Gap Analysis

Comparison identifying new obligations: mandatory DPO appointment for medium and large organisations; data breach notification to the NDPC within 72 hours; mandatory DPIA for high-risk processing; cross-border transfer restrictions.

Implementation: Data Inventory & Classification

Cloud DLP used to scan Cloud Storage, BigQuery, and Cloud SQL for PII patterns (names, emails, phone numbers, BVN, NIN, financial data). Output: a data inventory register covering what data, where it lives, who processes it, and the legal basis.

Implementation: Data Protection Impact Assessments (DPIAs)

DPIAs conducted for three high-risk processing activities: health data processing, automated decision-making (credit scoring), and processing of minors' data.

Implementation: 72-Hour Breach Notification Procedure

Procedure designed and documented: detection → internal escalation → impact assessment → NDPC notification (within 72 hours) → data subject notification. Cloud Functions automation triggers an internal alert within 15 minutes of SCC logging a data breach finding.

Implementation: Cross-Border Transfer Governance

All third-party processors receiving Nigerian personal data reviewed. Adequacy basis confirmed or Standard Contractual Clauses prepared for each processor.

Challenges, Failures and Corrections

This portfolio record does not invent deployment failures, client incidents, or corrective actions that are not supported by the published project evidence. Where operational details are confidential or were not captured for public release, the case study documents the design boundary and lesson instead of presenting an unsupported narrative.

Security, Compliance and Risk

The implementation is described with least privilege, traceability, controlled configuration, and evidence boundaries in mind. Where the project supports a regulated environment, these are technical safeguards and audit-readiness contributions, not a claim that an application or product is independently certified.

Testing, Validation and Evidence

The evidence published for this project is the documented implementation, its stated architecture and controls, and the portfolio record. The published record documents the implemented architecture, the core controls, and the technology choices that can be reviewed without exposing confidential operational data. Claims are limited to the implementation details and outcomes documented in this case study.

Results and Evidence

The published record documents the implemented architecture, the core controls, and the technology choices that can be reviewed without exposing confidential operational data.

What I Learned

This project reinforced that compliance work is not a technology checklist. The durable value comes from making boundaries, trade-offs, ownership, and evidence clear enough for another engineer or stakeholder to review and operate the system responsibly.

What I Would Improve Next

• Expand automated validation around the most important workflow and control boundaries.
• Add or deepen operational evidence, such as dashboards, test artefacts, or an architecture diagram where it is safe to publish one.
• Review the design against the next expected scale, reliability, and governance requirement before extending the system.

Final Takeaway

This compliance and governance engineering case study addresses a defined technical problem through Cloud DLP, BigQuery, Security Command Center. It demonstrates my ability to turn requirements into an explainable design, implement the important controls, and document outcomes and limitations without overstating what the evidence proves.