| Users | Security Challenges | Impact on System | Required Security Measures |
|---|---|---|---|
| 100 users | Basic authentication and access control needed | Low risk, manual monitoring possible | Implement strong passwords, HTTPS |
| 10,000 users | Increased attack surface, data privacy concerns | Moderate risk, manual checks insufficient | Use encryption at rest and in transit, role-based access control |
| 1 million users | High risk of data breaches, automated attacks | High risk, system performance affected by security checks | Automated threat detection, multi-factor authentication, secure APIs |
| 100 million users | Massive attack surface, regulatory compliance critical | Very high risk, security can bottleneck system performance | Security by design, zero trust architecture, continuous monitoring, incident response |
Why security must be designed in in HLD - Scalability Evidence
As user count grows, security checks (authentication, authorization, encryption) add processing overhead.
Without early design, security can slow down response times and increase resource use.
Security flaws also cause system failures through breaches or downtime.
Therefore, security mechanisms become the first bottleneck if not designed in from the start.
- Design security early: Integrate security in architecture, not as an afterthought.
- Use scalable authentication: OAuth, JWT tokens reduce load on servers.
- Implement caching: Cache security tokens to reduce repeated checks.
- Distribute security checks: Use edge servers or CDNs for initial filtering.
- Automate monitoring: Use AI/ML for threat detection to handle scale.
- Adopt zero trust: Continuous verification reduces risk at scale.
At 1 million users, assuming 10 requests per user per day:
- Requests per second: ~115 (1,000,000 users * 10 req/day / 86400 seconds)
- Security processing overhead: ~20-30% CPU increase due to encryption and auth checks
- Storage for logs and audit trails: several TBs per month depending on logging detail
- Bandwidth impact: Encryption adds ~10-20% overhead on data size
Ignoring security design leads to higher costs from breaches and downtime.
Start by explaining why security is critical at all scales.
Discuss how security impacts system performance and user experience.
Identify bottlenecks caused by security checks as user base grows.
Propose concrete scaling solutions like token caching, distributed checks, and zero trust.
Highlight cost trade-offs and importance of early design.
Your database handles 1000 QPS. Traffic grows 10x. What do you do first?
Answer: Since security checks add overhead, first optimize or scale authentication mechanisms (e.g., add token caching, use stateless tokens) to reduce load before scaling the database. Also consider adding read replicas and caching for database queries.
Practice
Solution
Step 1: Understand the cost of late security fixes
Fixing security problems after the system is built often requires rework, causing delays and higher costs.Step 2: Recognize the risk of ignoring early security
Ignoring security early can lead to data breaches and loss of user trust, which are costly and damaging.Final Answer:
Because fixing security issues later is more expensive and risky -> Option DQuick Check:
Early security design = Cost saving and risk reduction [OK]
- Thinking security can be added easily after development
- Assuming users don't care about security
- Believing security always slows down systems
Solution
Step 1: Identify secure data handling methods
Encrypting data protects it from unauthorized access both when stored and during communication.Step 2: Eliminate insecure options
Ignoring authentication, using default passwords, or open access are insecure and expose the system to attacks.Final Answer:
Encrypting sensitive data during storage and transmission -> Option AQuick Check:
Encryption = Secure data handling [OK]
- Skipping authentication for speed
- Using default or weak passwords
- Allowing unrestricted access
Solution
Step 1: Understand consequences of data breaches
A data breach exposes sensitive information, damaging user trust and requiring expensive fixes.Step 2: Reject incorrect outcomes
Quick fixes without impact are unrealistic; breaches never improve performance; public data assumption is unsafe.Final Answer:
The breach causes loss of user trust and costly fixes -> Option AQuick Check:
Data breach = Trust loss + Costly fixes [OK]
- Assuming breaches have no impact
- Thinking breaches improve performance
- Believing fixes are always quick and cheap
Solution
Step 1: Identify missing security features
Authentication verifies users; encryption protects data in transit. Both are essential for security.Step 2: Exclude harmful fixes
Removing access controls, disabling logging, or using default passwords worsen security risks.Final Answer:
Add authentication and encrypt data communication -> Option BQuick Check:
Authentication + Encryption = Secure system [OK]
- Removing access controls
- Disabling logs to speed up
- Using default passwords
Solution
Step 1: Recognize best security design practice
Planning authentication, encryption, and secure communication early integrates security deeply and reduces risks.Step 2: Understand risks of delayed security
Adding security late or relying only on firewalls leaves many vulnerabilities and increases costs.Final Answer:
Plan authentication, encryption, and secure communication before coding -> Option CQuick Check:
Early security planning = Strong protection [OK]
- Delaying security until after development
- Relying only on network firewalls
- Ignoring security to speed up features
Ready to prove it?
Pass the test and this pattern moves unknown
