Implementing AI in Critical systems
summary
Organizations face a shortage of high‑quality labeled data and real interoperability, so success requires mandating data standards and building large clean training sets, plus automated code scanners and encryption. Attacks focus on the data layer — data poisoning and deepfakes drive wrong outputs and phishing — so defenses are continuous monitoring, anomaly detection, incident response that can take models offline, and traditional backups; real example: a pipeline outage that halts fuel distribution for about a week.
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chapters (16)
- 0:00AI in critical systems
- 1:06AI integration challenges
- 3:57Regulatory frameworks
- 7:07Generative AI security risks
- 11:01Human error mitigation
- 12:49AI team profiles
- 15:43Data confidentiality & integrity
- 17:32AI ethics considerations
- 21:06AI innovation vs regulation
- 22:11AI security collaboration
- 23:58US AI adoption strategies
- 27:11Cyber attacks on critical systems
- 30:23Grid emergency protocols
- 34:14Data poisoning protection
- 38:33Bias in AI training data
- 44:14Closing & Q&A
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