CASE 01 · PCI DSS
Fintech Payment Processor - PCI v4.0.1 Execution
Environment
- 2M+ monthly transactions
- AWS + microservices architecture
- 40+ APIs integrated with third parties
Problem
- PCI scope incorrectly defined — 38% unnecessary assets in scope
- 126 control gaps across 12 requirements
- Prior audit failed due to weak logging + segmentation
- No centralized evidence tracking
What We Did
- Re-scoped CDE → reduced scope by 31%
- Ran full PCI DSS v4.0.1 gap assessment
- Built remediation tracker with control-level ownership mapping
- Network segmentation validation, logging & monitoring fixes (SIEM alignment), API security hardening
- Pre-audit simulation (QSA-led)
OUTCOME
73%Control gaps reduced (126 → 34)
14wAudit readiness (vs 9–12 months typical)
0Major non-compliances in final audit
22%Audit cost savings
What Made The Difference
Execution + validation (not advisory). Pre-audit simulation eliminated surprises.
CASE 02 · DATA PRIVACY
Healthcare Group - Multi-Region Privacy Program
Environment
- 3 countries
- 12M+ patient records
- Mix of on-prem + cloud systems
Problem
- No data classification
- Zero visibility into sensitive data flows
- Non-compliance with PDPL + GDPR
- High breach exposure risk
What We Did
- Data discovery for structured and unstructured data
- Classified 100% critical data assets
- Built privacy framework (GDPR + PDPL aligned)
- Implemented data access controls, retention policies, breach response workflows
OUTCOME
<5%Unclassified sensitive data (from 78%)
16wRegulatory alignment
65%Data exposure risk reduction
✓Audit-ready privacy governance
CASE 03 · AI SECURITY – SATLAS-LED
Fintech AI Fraud Engine - Adversarial Validation
Environment
- AI-based fraud detection engine
- Real-time decisioning system
- High-volume financial transactions
Problem
- No adversarial testing on AI models
- Model exposed to manipulation (false negatives risk)
- No AI governance or audit trail
What We Did
- Simulated adversarial attacks via SATLAS — data poisoning, input manipulation, model evasion
- Tested model resilience across multiple attack scenarios
- Conducted AI risk assessment + governance framework setup
OUTCOME
18High-risk model vulnerabilities
22-28%Potential fraud bypass under attack
40%Resilience uplift post-mitigation
22%AI governance + audit framework
What Made The Difference
Not just AI assessment - actual attack simulation on AI models.
CASE 04 · TPRM
Bank - Third Party Risk Program
Environment
- 120+ vendors
- Critical fintech integrations
- Regulatory pressure
Problem
- No centralised vendor risk scoring
- 70% vendors unassisted
- High compliance exposure
What We Did
- Built TPRM framework
- Assessed 100+ vendors in 10 weeks
- Implemented risk scoring model
- Implemented data access controls, retention policies, breach response workflows
OUTCOME
100%Vendor visibility
11%High-risk vendors (from 34%)
✓Improved regulatory readiness
↓New vendor onboarding risk
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