Forward Focus – The Impact of Artificial Intelligence
summary
AI augments detection and automates routine tasks but produces concrete risks: hallucinations that fabricate facts, model poisoning that embeds backdoors in pretrained models, and easy phishing in local languages that scales attacks. Large cloud models ingest tenant data and enable data exfiltration via developer prompts; explainability fails for deep learning, and qualified defenders are scarce. Practical mitigations include training private models, strict DLP for LLM usage, adversarial testing of models, and slower, safety-focused rollout in high-risk OT and medical environments.
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chapters (20)
- 0:00Introduction
- 2:30When did you start
- 4:49How will AI be used
- 6:20Ethical dimensions of AI
- 9:45Government involvement in AI
- 11:03Access to AI
- 17:14Risk
- 18:16How can we protect
- 19:16Phishing
- 20:44Adversarial Machine Learning
- 23:50Chat GPT
- 26:42Risks
- 29:16When
- 30:23Grammarly
- 31:03Data Leak Prevention
- 34:54How is it different from GitHub
- 36:35The problem with AI
- 37:33Autonomous vehicles
- 38:50Threat intelligence
- 39:53Talk about it
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