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AI Safety Engineering: Red Teaming, Alignment Techniques, and Regulatory Compliance for Production AI Systems
Paperback
Series: Production AI Engineering
General ComputersComputer Security
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Publisher: Independently Published
Pages: 104
Weight: 0.33
Height: 0.22 Width: 6.00 Depth: 9.00
Language: English
As artificial intelligence transitions from academic research labs to mission-critical enterprise production, the discipline of AI safety engineering has become an absolute necessity. AI Safety Engineering: Red Teaming, Alignment Techniques, and Regulatory Compliance for Production AI Systems is the definitive, hands-on guide for machine learning engineers, ML platform teams, risk officers, and compliance leads who must deploy responsible AI systems with confidence.
This comprehensive technical handbook bridges the gap between theoretical alignment research and production engineering practice. It provides actionable frameworks, code-level concepts, and architectural patterns to protect your systems from failure, manipulation, and regulatory penalties.
Inside, you will master the critical pillars of production-grade AI safety:- Regulatory Compliance: Learn to implement the EU AI Act's risk-proportionate requirements and build automated workflows around the NIST AI Risk Management Framework (AI RMF).
- Adversarial Red Teaming: Design and execute rigorous red teaming campaigns to expose vulnerabilities in LLMs, multimodal systems, and agentic pipelines.
- Advanced Model Alignment: Apply Reinforcement Learning from Human Feedback (RLHF), Constitutional AI, and scalable oversight to align models with human values.
- Fairness & Explainability: Implement quantitative fairness metrics and design interpretable architectures to demystify complex neural network decisions.
- Automated Guardrails & Monitoring: Construct continuous integration safety pipelines and real-time monitoring infrastructure for post-deployment protection.
Whether you are deploying autonomous AI agents, managing sensitive enterprise data, or preparing for strict regulatory audits, this book delivers the engineering blueprints required to build resilient, ethical, and trustworthy AI systems.
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