Designing Secure AI Architectures for Modern Systems

Packt via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

8 weeks, 1 hour a week
Sessions

Self-Paced
Level

Advanced
Subtitles

English

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Overview

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Build resilient AI systems with advanced security, governance, and zero trust design. Learn to protect models, secure endpoints, and manage risks across multi-cloud platforms while engineering robust guardrails. Modern AI systems operate across complex pipelines, APIs, and cloud platforms, creating new layers of risk. This journey begins by uncovering vulnerabilities in AI infrastructure, including model weights, inference endpoints, and plugin integrations. You will examine attack vectors such as prompt injection, data leakage, and membership inference while understanding trust boundaries and behavioral patterns in production environments. The next phase focuses on strengthening systems through defensive engineering. You will design prompt firewalls, implement token-level filtering, and integrate PII redaction to secure sensitive data flows. Governance is introduced through risk classification, transparency reporting, and structured artifacts such as model and system cards aligned with global frameworks. In the final stage, you will build secure agentic workflows and deploy multi-cloud architectures with confidence. Topics include IAM roles, private endpoints, RAG security, and sovereign cloud configurations. By the end, you will move from functional AI implementation to designing secure, compliant, and resilient systems capable of handling real-world threats. This course is designed for AI engineers, cloud architects, and cybersecurity professionals responsible for deploying and securing AI systems in enterprise environments. It is ideal for individuals working with Azure, AWS, or Google Cloud who seek to strengthen model security, enforce governance, and manage AI risks. A foundational understanding of cloud computing, APIs, and machine learning concepts is recommended. The course follows a structured progression from identifying AI risks to implementing defensive mechanisms and governance strategies. It emphasizes practical architecture patterns and real-world attack scenarios. Learners will build a security-first mindset aligned with modern AI system design principles. This course is based on Designing Secure AI Architectures for Modern Systems, by Anand Rao Nednur. This video is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

Course Introduction
- This module provides an overview of the course structure, key themes, and learning goals. Learners will gain foundational knowledge on designing secure AI architectures for modern systems. It sets the stage for understanding the importance of security in AI development.
Module 1: AI Infrastructure & Attack Surfaces
- This module covers the critical security challenges in AI infrastructure, including protecting model weights, securing inference endpoints, and mitigating various attack vectors. Learners will gain insights into data privacy, threat detection, and secure AI deployment strategies. It also explores how to implement and assess security measures in real-world AI systems.
Module 2: Defensive Engineering & Guardrails
- This module explores critical strategies for building secure AI systems, including input filtering, data protection, and system integrity measures. Learners will gain practical knowledge on implementing defensive techniques to prevent security vulnerabilities and maintain ethical AI operations.
Module 3: Governance & Compliance
- This module explores key concepts in AI governance, including risk classification, transparency reporting, and documentation practices. Learners will gain insights into managing AI systems responsibly and ensuring compliance with evolving regulatory standards.
Module 4: Agentic Workflows & Bounded Autonomy
- This module explores strategies for managing AI autonomy and tool access, focusing on risk mitigation, command interpretation, and approval processes. Learners will gain insights into defining permissions, limiting AI actions, and implementing safe decision-making frameworks.
Module 5: Operations & Influence (The First Hire Role)
- This module covers strategies for securing AI systems through vulnerability scanning, dataset tracking, and cross-team collaboration. It also explores how to position security as a business asset and addresses key threats and defenses in AI model security.
Module 6: Cloud AI Patterns & Platforms
- This module covers key strategies for securing AI systems in cloud environments, including private networking, access control, and compliance with data residency requirements. Learners will gain an understanding of how to implement and manage security measures for enterprise-level AI deployments.
Phase 6 Case Study: The Multi-cloud "Internal Startup"
- This module explores critical strategies for managing identity and access in multi-cloud environments, secure API gateway configurations, and data encryption practices. Learners will gain insights into protecting data across cloud platforms and implementing secure, scalable solutions.