Belajar Security Architect - Secure AI/LLM Architecture
Mempelajari arsitektur aman untuk AI/LLM: data security, model protection, inference security, AI governance, dan merancang AI system yang responsible
#Pendahuluan
Setelah di episode 18 kita mempelajari zero trust implementation design, pada episode ini kita dalami secure AI/LLM architecture — bagaimana mendesain arsitektur keamanan untuk sistem AI dan LLM. AI/LLM membawa attack surface baru yang belum pernah ada sebelumnya.
Mengapa AI security architecture penting? Karena AI/LLM mengubah fundamental cara sistem memproses data. Data bisa bocor melalui model, model bisa dimanipulasi, dan inference bisa disalahgunakan. Tanpa arsitektur yang aman, AI menjadi liability, bukan asset.
#AI Security Threat Landscape
#Threat Categories
| Threat | Deskripsi |
|---|---|
| Data poisoning | Training data dimanipulasi |
| Model theft | Model dicuri atau direplikasi |
| Prompt injection | Input memanipulasi output |
| Data exfiltration | Data bocor melalui model |
| Adversarial examples | Input yang fool model |
| Model inversion | Merekonstruksi data dari model |
#OWASP Top 10 for LLM
| No | Risk |
|---|---|
| 1 | Prompt injection |
| 2 | Insecure output handling |
| 3 | Training data poisoning |
| 4 | Model denial of service |
| 5 | Supply chain vulnerabilities |
| 6 | Sensitive information disclosure |
| 7 | Insecure plugin design |
| 8 | Excessive agency |
| 9 | Overreliance |
| 10 | Model theft |
#Secure AI Architecture
#Architecture Components
#Security Controls per Layer
| Layer | Controls |
|---|---|
| Data | Classification, access control, audit |
| Training | Secure enclave, integrity verification |
| Model | Access control, encryption, watermarking |
| Inference | Rate limiting, input validation, monitoring |
| Application | Auth, output filtering, logging |
#Data Security for AI
| Concern | Solution |
|---|---|
| Training data privacy | Differential privacy, federated learning |
| Data access control | Role-based, data segmentation |
| Data lineage | Tracking, audit trail |
| Data quality | Validation, anomaly detection |
#Model Security
| Concern | Solution |
|---|---|
| Model access | API key, OAuth, rate limiting |
| Model integrity | Signing, hash verification |
| Model confidentiality | Encryption, secure enclaves |
| Model theft | Watermarking, watermark detection |
Warning
Model yang mengakses data sensitif harus diperlakukan sama dengan database sensitif — encryption at rest + in transit, access logging, dan audit trail. Model bukan "just code" — ia adalah asset yang mengandung informasi dari training data.
#AI Governance Framework
| Component | Detail |
|---|---|
| Responsible AI principles | Fairness, transparency, accountability |
| AI risk assessment | Identify AI-specific risks |
| Model validation | Test for bias, accuracy, security |
| Monitoring | Continuous performance & security monitoring |
| Incident response | AI-specific IR procedures |
Tip
AI governance harus dimulai dari design, bukan setelah deployment. Privacy by design, security by design, dan fairness by design harus built-in ke AI architecture dari awal.
#Penutup
Inti yang harus dibawa pulang:
- AI threats: data poisoning, model theft, prompt injection, data exfiltration.
- Secure architecture: per-layer controls from data to inference.
- Model security: access control, encryption, integrity verification.
- AI governance: responsible AI principles + risk assessment.
Di episode 20 selanjutnya kita akan membahas resilience & BCP security — cyber resilience, business continuity, dan disaster recovery architecture.