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🇰🇪Enforcement active

Kenya Data Protection Act — AI Enforcement 2026

Data PrivacyKenyaAfricaFintech

Kenya's Data Protection Act 2019 is actively enforced by the Data Commissioner. Any company processing M-Pesa transaction data, mobile airtime data, or user location data for AI credit scoring must register as a data controller, conduct DPIAs, and disclose automated decision-making to users.

⚠️
Maximum Penalty
KSh 5 million (~$38,000 USD) per violation
Affects: Kenyan fintechs, banks, telcos, and any international company processing Kenyan resident data.

The Developer Problem

Kenyan fintech and credit scoring companies are building AI models on M-Pesa data without registering with the Data Commissioner or conducting DPIAs. The Commissioner has made fintech a priority enforcement target.

What You Must Build

These are the exact technical components regulators will check for:

1

Data controller registration with Kenya Data Commissioner

2

DPIA (Data Protection Impact Assessment) for AI credit scoring models

3

Automated decision disclosure to users (in Swahili and English)

4

User consent for data processing beyond transaction execution

Consequences of Non-Compliance

KSh 5 million (~$38,000) per violation

Data Commissioner enforcement notice and public naming

Mandatory suspension of data processing pending compliance

Drop-In Code Solution

Instead of building this from scratch (2–6 weeks of engineering time), use this production-ready package that implements all the required components above.

🔒
Zero-Trust Edge PII Masking Middleware
Let your developers use AI without leaking customer data to OpenAI or Anthropic.
$79
/month
⏱ Setup: 30 minutesNext.jsTypeScriptVercel EdgeNode.js

A lightweight TypeScript middleware wrapper. It intercepts the prompt at the Edge before it leaves your network, uses optimized local regex + lightweight NER to identify and hash all sensitive data, sends the clean prompt to the AI API, and decodes hashes back into real values only inside the secure browser context.

Code Preview
// pii-masker.edge.ts — intercept before prompt leaves network
import { maskPII, unmaskPII } from './pii-masker.edge'

// In your Next.js API route:
export async function POST(req: Request) {
  const { prompt } = await req.json()
  
  // Mask PII before sending to OpenAI
  const { cleanPrompt, vault } = await maskPII(prompt)
  
  const response = await openai.chat.completions.create({
    messages: [{ role: 'user', content: cleanPrompt }]
  })
  
  // Restore real values only for the user's browser
  const safeResponse = unmaskPII(response.choices[0].message.content, vault)
  return Response.json({ content: safeResponse })
}
Files Included
pii-masker.edge.ts
pii-decoder.client.ts
patterns/financial.ts
patterns/healthcare.ts
patterns/general.ts
README.md
LICENSE
kenya dpa mpesa credit scoring compliance fix, kenya data commissioner registration ai fintech 2026, automated credit scoring kenya dpa compliance typescript, kenya data protection act developer requirements