- Популярные видео
- Авто
- Видео-блоги
- ДТП, аварии
- Для маленьких
- Еда, напитки
- Животные
- Закон и право
- Знаменитости
- Игры
- Искусство
- Комедии
- Красота, мода
- Кулинария, рецепты
- Люди
- Мото
- Музыка
- Мультфильмы
- Наука, технологии
- Новости
- Образование
- Политика
- Праздники
- Приколы
- Природа
- Происшествия
- Путешествия
- Развлечения
- Ржач
- Семья
- Сериалы
- Спорт
- Стиль жизни
- ТВ передачи
- Танцы
- Технологии
- Товары
- Ужасы
- Фильмы
- Шоу-бизнес
- Юмор
Production-Ready AI Systems: Security, Evaluation & Data Platforms
Modern AI systems require more than powerful models—they require security, evaluation, governance, and continuous improvement. This session combines lessons from production AI agent security with real-world LLM evaluation and fine-tuning workflows.
Topics may include prompt injection, tool abuse, memory poisoning, defense-in-depth architectures, custom evaluation frameworks, Azure OpenAI fine- tuning, and practical engineering lessons learned from deploying AI-powered systems.
Key Takeaways:
- Understand security challenges in AI agents
- Learn practical defense patterns for production AI
- Explore LLM evaluation methodologies
- Understand fine-tuning workflows using Azure OpenAI
- Apply production engineering best practices to AI systems
📌 This is part of a series, learn more here: https://aka.ms/ProdReadySystems/series
00:00 Intro & Housekeeping
01:50 Securing the AI Stack: Why AI Security Matters
05:00 The Four-Layer AI Security Framework
06:48 Layer 1: Model Security (Prompt Injection, Jailbreaks & Output Hijacking)
10:55 Layer 2: Agent & Application Security
14:09 Layer 3: MCP & Tool Security Risks
16:06 Layer 4: Infrastructure Security Essentials
17:57 Defense in Depth & Security Checklist
19:42 Resources & Final Takeaways on AI Security
20:43 LLM-Driven Merge Conflict Resolution Introduction
22:21 Why Merge Conflicts Are Still a Major Developer Challenge
28:23 Fine-Tuning LLMs for Merge Conflict Resolution
29:40 Key Fine-Tuning Insights & Lessons Learned
33:31 Evaluating Merge Resolution Models
34:42 Python Tips, Structured Outputs & Development Best Practices
39:00 Handling Large Files & Token Limits
40:46 Q&A Transition
42:07 Agent Security in Practice: Real-World Risks & Attacks
43:26 Hugging Face Security Incident Case Study
47:44 OWASP Top Risks for LLM Applications
54:05 Live Demo: Testing & Defending Against Prompt Injection
56:17 Excessive Permissions, Data Exposure & Supply Chain Risks
59:30 Token Abuse, Rate Limiting & Secure Agent Design
01:01:09 Evaluations, Red Teaming & Reliability Testing
01:03:09 Slides, Q&A & Resources
01:03:46 Closing Remarks & Event Survey
[eventID:27335]
Видео Production-Ready AI Systems: Security, Evaluation & Data Platforms канала Microsoft Reactor
Topics may include prompt injection, tool abuse, memory poisoning, defense-in-depth architectures, custom evaluation frameworks, Azure OpenAI fine- tuning, and practical engineering lessons learned from deploying AI-powered systems.
Key Takeaways:
- Understand security challenges in AI agents
- Learn practical defense patterns for production AI
- Explore LLM evaluation methodologies
- Understand fine-tuning workflows using Azure OpenAI
- Apply production engineering best practices to AI systems
📌 This is part of a series, learn more here: https://aka.ms/ProdReadySystems/series
00:00 Intro & Housekeeping
01:50 Securing the AI Stack: Why AI Security Matters
05:00 The Four-Layer AI Security Framework
06:48 Layer 1: Model Security (Prompt Injection, Jailbreaks & Output Hijacking)
10:55 Layer 2: Agent & Application Security
14:09 Layer 3: MCP & Tool Security Risks
16:06 Layer 4: Infrastructure Security Essentials
17:57 Defense in Depth & Security Checklist
19:42 Resources & Final Takeaways on AI Security
20:43 LLM-Driven Merge Conflict Resolution Introduction
22:21 Why Merge Conflicts Are Still a Major Developer Challenge
28:23 Fine-Tuning LLMs for Merge Conflict Resolution
29:40 Key Fine-Tuning Insights & Lessons Learned
33:31 Evaluating Merge Resolution Models
34:42 Python Tips, Structured Outputs & Development Best Practices
39:00 Handling Large Files & Token Limits
40:46 Q&A Transition
42:07 Agent Security in Practice: Real-World Risks & Attacks
43:26 Hugging Face Security Incident Case Study
47:44 OWASP Top Risks for LLM Applications
54:05 Live Demo: Testing & Defending Against Prompt Injection
56:17 Excessive Permissions, Data Exposure & Supply Chain Risks
59:30 Token Abuse, Rate Limiting & Secure Agent Design
01:01:09 Evaluations, Red Teaming & Reliability Testing
01:03:09 Slides, Q&A & Resources
01:03:46 Closing Remarks & Event Survey
[eventID:27335]
Видео Production-Ready AI Systems: Security, Evaluation & Data Platforms канала Microsoft Reactor
Copilot said: AI Security Agent Security AI Agents Generative AI Large Language Models LLM Security Prompt Injection Jailbreak Attacks OWASP Top 10 for LLMs Model Context Protocol MCP Security AI Infrastructure Security Defense in Depth Azure AI Microsoft Security AI Governance Secure AI Development AI Risk Management GitHub Copilot Merge Conflicts LLM Fine-Tuning Azure OpenAI Machine Learning ML Engineering AI Coding Assistants AI Evaluation
Комментарии отсутствуют
Информация о видео
24 июля 2026 г. 16:46:00
01:04:53
Другие видео канала
