What Security Teams Need to Know and Do
summary
Explains concrete risks and mitigations: shows agentic AI scanning environments and opening change requests, describes live use of automated incident response that contains endpoints and resets credentials, and demonstrates automated identity access cleanup removing stale permissions. Cites real failures like prompt poisoning, decision drift, a deepfake hiring scam, and vendor "AI powered" marketing. Recommends classify data, rapid risk triage, sandboxes, an approved app catalog, NIST AI RMF and documenting audits before deployment.
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chapters (21)
- 0:00AI security challenges
- 2:15AI deployment risks
- 4:26AI evolution stress
- 6:54AI levels and security
- 9:49Agentic AI security tasks
- 11:18AI policy pitfalls
- 12:45Built-in AI OS risks
- 14:54Invisible AI data risks
- 16:43Real AI threats
- 18:50Prompt poisoning risks
- 20:13AI threats to security ops
- 22:03Deepfake identity risks
- 24:09Shadow AI compliance
- 26:17Streamlining AI approvals
- 27:42Rushed AI trust issues
- 30:33Interconnected AI risks
- 35:05Evaluating AI vendors
- 36:50AI regulations requirements
- 37:54Security teams lead AI governance
- 39:23Security’s role in AI oversight
- 41:53Secure AI immediately
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