Cloud data and AI security in 2024: What you need to know
summary
Claims that 90% can't answer what data exists and that a typical enterprise owns ~20 data store types guide a demo of automated discovery that snapshots and scans disks (no connection strings), deploys an outpost per region, and classifies structured and unstructured data. Findings include 45–50% of data stores unused, orphan snapshots and public files, DDR rules that flag mass downloads or ransomware in near real time, AI coverage (95% of AI trained in cloud) and an ASPM beta that found a public model trained on sensitive data.
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chapters (23)
- 0:00Introduction to data security
- 1:54Fragmentation challenges
- 3:46Discovery and classification
- 6:17Contextualizing data risk
- 7:36Detection and response
- 9:04Technology and architecture
- 10:17Data security dashboard demo
- 11:52Data analysis in action
- 15:25Monitoring data activity
- 16:59Risk assessment and policy
- 19:17Data map and hotspot analysis
- 21:38AI application security
- 24:53DDR policy and automation
- 27:38ZoomInfo security journey
- 30:31Data governance importance
- 32:35Breach readiness strategies
- 34:42Selecting a security vendor
- 37:07Velocity in data security
- 38:29AI as a data problem
- 40:39Responsible AI practices
- 42:29Implementation tips and talent
- 44:01Data security organizational
- 49:01Automation and conclusion
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