I started with the data model which felt safe, principals, resources, roles, policies.
Start by clarifying requirements and scale, then propose a unified identity model that treats humans and services as first-class principals with distinct credential types. Walk through authentication, authorization, and audit layers, highlighting trade-offs like centralized vs. federated auth and RBAC vs. ABAC. Conclude with how you'd handle key challenges such as token revocation, key rotation, and audit log integrity.
Pro tip: Emphasize that machine identities need different lifecycle management (e.g., short-lived credentials, automated rotation) than human users, and mention how you'd prevent privilege creep through periodic access reviews.
Ask about expected number of users/services, latency requirements, compliance needs (e.g., SOC2, GDPR), and existing infrastructure. This scopes the design and shows you avoid over-engineering.
Propose a unified principal model with types (human, service) and attributes. For authentication, discuss options like OIDC for humans, mTLS or JWT for services, and how to handle credential storage and rotation.
Choose an authorization model (RBAC, ABAC, or ReBAC) and explain how policies are evaluated and enforced at the API gateway or service mesh. Discuss trade-offs between centralized and decentralized enforcement.
Describe how to capture immutable audit logs for all authn/authz decisions, including who, what, when, and why. Mention log integrity (e.g., append-only, cryptographic signing) and real-time alerting for anomalies.
Discuss how to scale the system (caching, sharding, federation), handle failures (graceful degradation, fallback auth), and key trade-offs like consistency vs. availability in policy decisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging the risk of long-lived secrets in config files and advocate for dynamic, short-lived credentials issued by a central identity service. Then describe a concrete pattern such as IAM roles for service accounts or a secrets manager with automatic rotation, and explain how workloads retrieve and refresh credentials without hardcoding them.
Pro tip: Emphasize that the best solution eliminates secrets entirely by using identity-based authentication (e.g., mTLS, SPIFFE, or cloud IAM roles) rather than just rotating static keys. Also mention the importance of auditing and monitoring credential usage to detect anomalies.
Explain why long-lived secrets in config files are risky: they can be leaked, accidentally committed, or stolen, and they are hard to rotate.
Propose using a central identity provider (e.g., AWS IAM, HashiCorp Vault, or SPIFFE) that issues short-lived credentials based on the workload's identity, such as a Kubernetes service account or instance profile.
Describe how the service retrieves credentials at runtime (e.g., via SDK, sidecar, or init container) and automatically refreshes them before expiry, avoiding any manual intervention.
Outline best practices: least privilege, short TTLs, automatic rotation, and revocation. Ensure credentials are never logged or stored on disk.
Mention logging and monitoring of credential issuance and usage to detect anomalies, and the ability to quickly revoke compromised credentials.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints of the policy evaluation system, then walk through a layered evaluation model that resolves conflicts through precedence rules and explicit deny semantics. Emphasize trade-offs between simplicity, performance, and flexibility, and how you would handle edge cases like role hierarchies and dynamic permissions.
Pro tip: Mention that you would make conflict resolution deterministic and auditable—logging which policy won and why—because in large-scale systems like Coupang's, debugging permission issues without a clear audit trail is a nightmare.
Ask about scale, latency requirements, consistency needs, and whether permissions are static or dynamic. This shows you don't jump to solutions without understanding the problem.
Describe how policies are represented (e.g., role-based, attribute-based) and how they map to users with multiple roles. Mention the need for a clear data model that supports efficient lookup.
Propose a precedence order: explicit deny > explicit allow > inherited allow, or role priority. Explain how you'd handle overlapping permissions and ensure deterministic outcomes.
Outline an efficient algorithm that aggregates permissions from all roles, applies conflict resolution, and returns a decision. Discuss caching, indexing, and performance optimizations.
Cover scenarios like role hierarchies, temporary roles, and policy changes. Discuss trade-offs between simplicity (e.g., deny-overrides) and flexibility (e.g., priority-based).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Async write to an append-only log, fan out to whatever downstream system does compliance reporting.
Start by clarifying the requirements: what needs to be logged, the acceptable latency overhead, and the durability guarantees. Then propose an asynchronous, non-blocking logging pipeline that decouples audit logging from the critical path, using in-memory buffers and a dedicated logging service. Finally, discuss trade-offs around consistency, reliability, and performance, and how to handle failures without impacting access decisions.
Pro tip: Emphasize that audit logs must be tamper-evident and immutable, and suggest using a write-ahead log or append-only store with cryptographic hashing to ensure integrity. Also, mention that sampling or aggregation can reduce volume while preserving security insights.
Ask about the volume of access decisions, latency SLAs, regulatory requirements, and what data must be logged. This ensures the design meets business and compliance needs.
Propose an asynchronous approach where access decisions are made first, and audit events are emitted to a high-throughput, non-blocking channel (e.g., in-memory queue or Kafka).
Describe a pipeline with buffering, batching, and backpressure handling. Use a distributed log like Kafka for durability and decoupling, and consumers that write to a tamper-evident store.
Discuss what happens if the logging pipeline fails: do you block access, drop logs, or buffer locally? Explain the trade-offs between consistency, availability, and latency.
Mention immutability, cryptographic hashing, and access controls for the audit store. Also, consider retention policies and the ability to query logs for forensics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about caching policy decisions at the edge, read replicas for the policy store, and sharding by tenant.
Start by clarifying the scope and scale of the IAM system (e.g., number of users, requests per second, geographic distribution) and the specific challenges that arise at scale. Then, structure your answer around key scaling dimensions—data volume, request throughput, latency, consistency, and security—and propose concrete architectural approaches and trade-offs for each.
Pro tip: Emphasize that scaling IAM is not just about handling more requests but also about maintaining low-latency authorization decisions and strong consistency for critical operations like permission changes. Mention the importance of caching and eventual consistency for read-heavy paths while ensuring strong consistency for writes.
Ask questions to understand the expected scale (e.g., millions of users, thousands of requests per second), latency requirements, consistency needs, and geographic distribution. This ensures your answer is tailored to the specific context.
List the primary challenges such as handling high read/write throughput, maintaining low latency for authorization checks, ensuring data consistency across distributed systems, managing large volumes of policies and permissions, and dealing with security and compliance at scale.
Discuss strategies like sharding/partitioning user data, using caching (e.g., Redis) for frequent authorization decisions, employing a distributed policy decision point (PDP) with a central policy administration point (PAP), and leveraging eventual consistency where acceptable.
Explain trade-offs between consistency and availability (CAP theorem), latency vs. accuracy, and cost vs. performance. Describe how to mitigate issues, such as using read replicas, asynchronous replication, and fallback mechanisms.
Recap the key challenges and your proposed solutions, emphasizing how they address the specific scale and requirements. Highlight any monitoring, testing, and iterative improvement plans.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Namespace everything by tenant ID, enforce it at the query layer, and don't let any cross-tenant join happen without an explicit federation model.
Start by clarifying the system's scale, tenant types, and compliance needs, then propose a layered isolation strategy (data, compute, network) with trade-offs between cost and isolation. Emphasize that the right approach depends on tenant size and regulatory requirements, and describe how you'd evolve from shared to isolated resources as tenants grow.
Pro tip: Show maturity by acknowledging that perfect isolation is expensive and often unnecessary; propose a tiered model where small tenants share resources with logical isolation, while large or regulated tenants get dedicated infrastructure, and explain how you'd migrate between tiers.
Ask about tenant scale, data sensitivity, compliance (e.g., PCI, GDPR), performance SLAs, and cost tolerance to determine the appropriate isolation level.
Propose a model: shared database with tenant ID, schema-per-tenant, database-per-tenant, or dedicated infrastructure, and justify based on requirements.
Detail how to enforce tenant boundaries in queries, storage, and APIs, including row-level security, tenant-aware connection pools, and encryption per tenant.
Explain how to isolate compute (e.g., separate containers, namespaces, or VMs) and network (VPCs, security groups) to prevent cross-tenant interference.
Discuss monitoring per tenant, noisy neighbor mitigation, cost allocation, and how to migrate tenants between isolation tiers as they grow.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.