Start by framing the problem around the four risk areas, then propose a prioritization matrix based on impact and likelihood. For each area, outline concrete fixes, tests, and a threat model, emphasizing data science's role in anomaly detection and experimentation.
Pro tip: Tie every recommendation to measurable business impact (e.g., revenue loss, customer trust) and show how data science can quantify and mitigate risks through monitoring and A/B testing.
List potential issues in each area: monetary correctness (pricing errors, rounding), transactional integrity (double charges, lost updates), security/privacy (PII exposure, payment data), and resilience (downtime, network failures).
Assess each issue by impact (financial loss, legal, reputation) and likelihood (frequency, exploitability). Prioritize high-impact, high-likelihood issues first, aligning with business goals.
For top issues, suggest specific fixes: e.g., idempotent transaction processing, encryption at rest/in transit, circuit breakers, and anomaly detection models for pricing errors.
Outline tests: unit tests for pricing logic, integration tests for transactions, chaos engineering for resilience, and security audits. Include data science monitoring: real-time anomaly detection, drift detection.
Create a threat model (e.g., STRIDE) covering spoofing, tampering, repudiation, information disclosure, denial of service, elevation of privilege. Map mitigations to each threat.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.