# Unlocking Bulletproof FinTech IAM Security: My 3 Week AWS Journey from Africa

In FinTech, trust isn’t just valuable, it’s important. One misconfigured policy can cost millions or expose sensitive financial data. This repo documents how I built secure, AI augmented IAM controls tailored for African FinTech using AWS:

🔹AWS IAM

🔹Google Gemini (AI)

🔹AWS Config

🔹AWS Lambda

🔹CloudFormation

Context: This was born out of a near breach caused by a misconfigured S3 bucket.

The result: A self healing IAM framework, deployed 100% as code, aligned with compliance and FinOps goals.

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My 3 Week Journey

I split the journey into 3 phases:

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Phase 1: FinTech IAM Foundations

1. Role-Based Access Control (RBAC)

Engineered least-privilege roles for Finance

Enforced:

• ✅ MFA

  ✅ Session expiration

  ✅ IP-based access control

  Aimed to drastically reduce the risk of unauthorized internal access through strict access controls and policy enforcement.

3. IAM Cost Governance with AWS Budgets

Integrated IAM permissions with AWS Budgets to track cost per role/team.
✅ FinOps + Security = Day 1 Priority

Phase 2: AI Powered IAM Controls

4. Drift Detection with AWS Config

aws config put-config-rule --config-rule '{ "ConfigRuleName": "root-key-check", ... }'

✅ Slack alerts on violations

✅ Enabled continuous monitoring for configuration drift, aiming for a consistent and compliant environment.

5. AI Driven Policy Drafting (Gemini)

Policies drafted via Google Gemini:

✅ TLS only s3:GetObject permissions

✅ KMS encryption enforced

✅ IP whitelisting (Nigeria only)

✅ Validated via IAM Policy Simulator

Significantly cut policy creation time and drastically reduced syntax errors, enabling faster and more accurate policy development.

6. IAM Access Analyzer

It Scanned for:

🔹 S3 public access

🔹 Cross account role exposure

🔹 Shared KMS keys

Initial scan found zero external data exposures, confirming a secure baseline at the time of analysis.

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Phase 3: Automation & Self-Healing

6. CloudFormation: IAM Password Policy

Resources: AccountPasswordPolicy: Type: AWS::IAM::AccountPasswordPolicy Properties: MinimumPasswordLength: 14 RequireSymbols: true RequireNumbers: true RequireUppercaseCharacters: true RequireLowercaseCharacters: true AllowUsersToChangePassword: true PasswordReusePrevention: 5 MaxPasswordAge: 90

✅ Version controlled in Git

✅ Compliance as Code enforced

7. AWS Lambda: Auto Remediation for S3 Public Buckets

def lambda_handler(event, context): if is_public(bucket): s3.put_public_access_block( Bucket=bucket, PublicAccessBlockConfiguration={ "BlockPublicAcls": True, "BlockPublicPolicy": True } )

✅ Exposure window drastically reduced: From hours of potential exposure to seconds via automated remediation.

FAQs: Your Cloud Security Questions Answered

Q: I'm not in FinTech. Is this still relevant?
A: Yes. IAM drift detection, automation, and and AI-generated policies are best practices for any cloud native team.

Q: Can AI really write secure IAM policies?
A: Yes, with human validation. Here's our 4-step workflow:

1. Draft with Gemini

2. Validate syntax

3. Simulate permissions

4. Approve logic

Q: How can startups implement this?
A: Start with:

✅Role Based Access Control (RBAC)

✅IAM Password Policies

✅AWS Config rules

✅Lambda Automation (Free Tier-friendly)

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Lessons Learned

✅Security is never static: monitor & remediate constantly

✅Use AI wisely: draft, simulate, then approve

✅Context matters: model threats unique to your region

Ready to Build Your Own Guardrails?

📂 Clone This Repo: https://github.com/OluwatosinOsho/african-fintech-iam-guardrails

#CloudSecurity #AWS #IAM #FinTech #DevSecOps #AIinSecurity

#FromZeroToCloud #RBAC #Lambda #AccessAnalyzer #InfrastructureAsCode #AfricaTech