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The AI Architecture & Engineering Masterclass

Name: The AI Architecture &amp; Engineering Masterclass
Author: Baturi

Securing the Full-Stack AI Lifecycle: From Data Poisoning Defense to Governance.

What you'll learn

✓ Students learn to protect "model weights" from theft and secure data training processes against poisoning
✓ The course covers both direct "jailbreak" patterns and indirect injection vectors, where external websites might hijack an AI agent.
✓ Lessons include identifying "memorization" (where models leak PII like SSNs) and performing membership inference attacks
✓ Students will build real-time prompt firewalls, PII redaction engines, and "hallucination" guardrails that use secondary models for fact-checking.
✓ The curriculum teaches "intent-based" limiting to prevent users from scraping model logic by asking the same question in different ways.
✓ Students learn to classify AI risks under the EU AI Act and implement the NIST AI Risk Management Framework
✓ The course details how to set "bounded autonomy" for AI agents, including tool-use permissions and human-in-the-loop checkpoints for high-stakes actions
✓ Topics include scanning model files for malware and maintaining secure dataset lineage

Requirements

● Several modules focus on advanced configurations for Azure (VNet integration), AWS (IAM roles and Bedrock), and Google Cloud (VPC Service Controls)
● The course covers high-level topics such as encryption (TLS 1.2+), IAM mapping, and API gateway proxying
● Familiarity with how AI models are trained (model weights) and how they interact with users (inference endpoints) is central to the curriculum
● n interest or background in regulatory frameworks like the EU AI Act and NIST AI Risk Management Framework is relevant for the governance module.

Description

In the current landscape, AI is moving significantly faster than the architectural guardrails designed to contain it. While the majority of development teams remain focused on the surface-level challenge of "getting a prompt to work," The AI Architecture Masterclass shifts the focus to the structural integrity of the entire ecosystem. This is a deep-dive into the "Lead Red Team" reality of 2026, where we move past the novelty of generative outputs and confront the systemic risks of enterprise-grade integration. We treat every model as a potential data-leaking liability and every autonomous plugin as a high-privilege vector for exploitation.
We intentionally strip away the theoretical fluff and "AI hype" to focus on the cold, hard mechanics of adversarial defense. This course provides the technical blueprints required to transition from fragile prototypes to "Zero Trust" AI pipelines. Drawing from the rigorous standards of global infrastructure leaders and top-tier research institutions, the mandate is clear: the goal isn't just to innovate—it's to build systems that remain resilient under sophisticated, multi-vector attacks. This masterclass gives you the keys to that digital sovereignty, ensuring that your architecture is as robust as the intelligence it hosts.
In the agentic era, code is no longer written solely by humans; it is synthesized by models in real-time. Our role as architects has evolved from simply building the engine to designing the containment field. We operate under a singular, uncompromising truth: If you cannot defend the model, you do not truly own the model.
This masterclass is a commitment to that defense. It represents the gold standard in AI security engineering, moving beyond basic safety filters to address the deep-layer vulnerabilities in model weights, inference endpoints, and multi-tenant cloud infrastructures. By the end of this curriculum, the distinction between "functional AI" and "secure AI" will be erased, replaced by a singular standard of architectural excellence.

Who this course is for

■ Professionals who need to implement advanced security patterns across platforms like Azure, AWS, and Google Cloud.
■ Those looking to specialize in securing AI infrastructure, including model weight protection and inference endpoint hardening
■ Individuals responsible for classifying AI risks under frameworks like the EU AI Act and the NIST AI Risk Management Framework.
■ Those focused on integrating security into the AI lifecycle, such as weight scanning for malware and maintaining secure dataset lineage.
■ Anyone interested in building "defensive engineering" layers, including prompt firewalls and hallucination guardrails.

Homepage

https://www.udemy.com/course/the-ai-architecture-engineering-masterclass

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