Government · Government · NIST
NIST SP 800-239 initial public draft, AI Data Center Security Analysis: A High-Performance Computing (HPC) Driven Approach
Outline · 32 pages
- 1. Introductionp. 9
- 1.1. Zone-Based HPC Architecturep. 9
- 2. Reference Architecture and Differencesp. 12
- 2.1. AI Data Center Reference Architecturep. 12
- 2.2. Computing Zone Hardware Infrastructure Differencep. 14
- 2.2.1. Accelerator-Dense Infrastructurep. 14
- 2.2.2. Diverse AI Acceleratorsp. 14
- 2.2.3. Reduced-Precision Arithmeticp. 14
- 2.2.4. Use of High-Bandwidth Memoryp. 14
- 2.2.5. AI-Optimized High-Speed Networkingp. 15
- 2.3. Computing Zone Software Stack Differencep. 15
- 2.3.1. System Software and Deep Learning SDKp. 15
- 2.3.2. Deep Learning Librariesp. 15
- 2.3.3. Resource Management and Orchestrationp. 16
- 2.4. Data Storage Zone Differencep. 16
- 2.4.1. Use of Object Storage as the Backbonep. 16
- 2.4.2. Layered Data Storage Systemp. 16
- 2.5. Access Zone Differencep. 17
- 2.6. Management Zone Differencep. 17
- 2.7. Open Models and Datasetsp. 18
- 3. AI Data Center Security Threats and Challengesp. 19
- 3.1. AI Data Center Scalability Challengep. 19
- 3.2. Data Quality, Provenance, and Security Challengesp. 19
- 3.3. Regulatory and Compliance Challenges and Logging, Monitoring, and Audit-Ready Challengesp. 19
- 3.4. Virtual Environment Threatsp. 20
- 3.5. Denial-of-Service (DoS) Threatsp. 20
- 3.6. Prompt-Based Exploitation Threatsp. 21
- 3.7. Supply Chain Threatsp. 22
- 3.8. Multi-Tenant Threats and the Three-Way “Trust Dilemma”p. 22
- 3.9. Insider Threatsp. 23
- 3.10. Silent Data Corruption (SDC) Threatp. 23
- 3.11. Expanding and Evolving Attack Surface Threats and AI-Powered Attacksp. 23
- 3.12. Multiple Data Center Training Threatp. 23
- 3.13. Firmware Integrity Threatp. 24
- 3.13.1. Baseboard Management Controller (BMC) Threatsp. 24
- 3.13.2. Unified Extensible Firmware Interface (UEFI) Threatp. 24
- 3.14. AI Accelerator Vulnerabilityp. 25
- 4. AI Data Center Security Posture and Possible Solutionsp. 26
- 4.1. Strengthening the Access Zone Protectionp. 26
- 4.2. Providing Strong Monitoring, Logging, Security, and Compliance Oversight Capabilitiesp. 26
- 4.3. Zero-Trust vs. Nurturing Trustp. 27
- 4.4. Enabling Human-In-The-Loop Oversight and Governance Capabilityp. 27
- 4.5. Developing a Robust and Fault-Tolerant Workflowp. 28
- 4.6. Security-by-Design and Verification-by-Design Approach to AI Data Centerp. 28
- 5. Conclusionsp. 29
- Referencesp. 30