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Design ML-Powered Secret Rotation Prioritizer

Hard
HardSystem DesignInfrastructureFeature StoreModel ServingAsked 1 times

Problem

Product Context

Meta runs a large internal microservices platform where thousands of services consume secrets from a centralized secrets management system. Design an ML-driven control plane that prioritizes secret rotation, detects risky access patterns, and decides when to trigger immediate rotation versus scheduled rotation with minimal service disruption.

Scale

SignalValue
Internal services120,000
Secrets under management1.8B active secrets
Secret reads/day45B
Secret write / rotation events/day220M
Peak policy-evaluation QPS650K
Peak rotation-decision QPS90K
End-to-end online latency budget120ms p99
Regions12 global regions

Task

  1. Clarify the product goals, threat model, and success metrics for an ML-based secret rotation system.
  2. Design the end-to-end architecture, including candidate generation, risk ranking, and final re-ranking / policy enforcement before rotation.
  3. Choose models and features for each stage, and explain what runs online versus batch.
  4. Define the data pipeline, labels, training cadence, and how to avoid training-serving skew.
  5. Propose offline and online evaluation, including security guardrails and rollout strategy.
  6. Identify key failure modes, especially around feature drift, false positives, stale features, and regional outages.

Constraints

  • The system must integrate with Meta-style internal service identity, audit logging, and regional control planes.
  • Some secrets back critical production paths and cannot be rotated synchronously without a fallback credential path.
  • Access logs are high-volume and partially delayed across regions by up to 3 minutes.
  • Security decisions must be explainable to infra and service owners.
  • Cost matters: only the highest-risk 0.5% of secrets can trigger expensive immediate rotation workflows each day.
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