Trust at Scale: Fueling AI and Defending Against Quantum Threats
summary
Explains how trusted data becomes scalable AI by locating and classifying data, reducing vector database bloat (example: 1 PB of source data can expand to ~10 PB after embeddings), and pushing near‑real‑time change notifications into pipelines instead of costly scans. Recommends post‑quantum cryptography now (cites NIST 2024 algorithm approvals), highlights NSA‑certified storage, AI‑driven ransomware detection, multi‑admin controls and end‑to‑end encryption as practical, deployable defenses.
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chapters (15)
- 0:00Trust at Scale in AI
- 1:05AI data pipeline questions
- 3:52Outdated data challenges
- 5:36Unified data management
- 7:23Proactive data currency
- 8:50Ethics and data guardrails
- 9:51Quantum threats today
- 10:33Storage as security core
- 11:35Postquantum cryptography
- 12:18NSA-certified storage
- 13:01Malicious file detection
- 13:22AI ransomware protection
- 14:04Multi-admin authentication
- 14:46End-to-end encryption
- 15:08NIST postquantum standards
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