Create a full-stack AI defense strategy across model, data, and infrastructure layers
β±οΈ Length: 6.1 total hours
π₯ 12 students
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- Course Overview
- Navigate the burgeoning landscape of Artificial Intelligence and its inherent security vulnerabilities through a comprehensive, hands-on curriculum.
- Understand that modern AI applications, particularly generative AI and Retrieval Augmented Generation (RAG) systems, present novel and complex attack vectors that traditional security paradigms struggle to address.
- This course provides a robust framework for architecting and implementing secure AI solutions, moving beyond reactive measures to proactive, integrated security strategies.
- We will explore how to fortify the foundational elements of AI systems β the data they consume, the models they employ, and the underlying infrastructure that supports them β against sophisticated threats.
- Participants will gain the ability to conceptualize and build resilient AI security postures, ensuring the integrity, confidentiality, and availability of AI-driven applications throughout their lifecycle.
- The focus is on a holistic approach, emphasizing the creation of a “full-stack” defense that anticipates and mitigates risks at every conceivable point of interaction within an AI ecosystem.
- This involves delving into the intricacies of data poisoning, model inversion, adversarial attacks, prompt injection, and other emerging threats specific to AI technologies.
- Learn to architect AI systems with security embedded from inception, fostering a culture of security-first development practices within AI teams.
- By the end of this program, you will possess the knowledge and practical skills to design, deploy, and manage AI applications with an unprecedented level of security assurance.
- The course is designed for a small, interactive group of 12 students, fostering a collaborative learning environment conducive to in-depth discussion and problem-solving.
- The total duration of 6.1 hours ensures focused learning on critical AI security principles and practices.
- Requirements / Prerequisites
- A foundational understanding of AI and Machine Learning concepts is beneficial, though not strictly mandatory.
- Familiarity with cloud computing platforms (AWS, Azure, GCP) and their security constructs will enhance the learning experience.
- Basic programming knowledge, preferably in Python, will be helpful for understanding practical implementations discussed.
- An awareness of general cybersecurity principles and common vulnerabilities is advantageous.
- Willingness to engage with complex technical concepts and actively participate in discussions and exercises.
- Experience with prompt engineering or AI application development is a plus but not required.
- Skills Covered / Tools Used
- AI Threat Modeling: Developing comprehensive risk assessments tailored to unique AI attack surfaces.
- Secure AI Architecture Design: Architecting AI solutions with multi-layered security controls.
- Data Security for AI: Implementing robust data protection strategies within AI pipelines, focusing on sensitive information handling.
- Generative AI Security: Specific techniques to defend Large Language Models (LLMs) and RAG applications against manipulation and misuse.
- AI Governance and Compliance: Establishing policies and controls for responsible AI deployment and oversight.
- AI Monitoring and Observability: Setting up systems to track AI behavior, detect anomalies, and ensure operational integrity.
- Security Integration in MLOps: Embedding security practices throughout the Machine Learning Operations lifecycle.
- AI Access Control and Permissions: Implementing granular security for AI components and their interactions.
- AI Output and Input Validation: Leveraging gateways and guardrails to control data flow into and out of AI systems.
- AI Security Tools: Introduction to and application of conceptual AI SPM (Security, Performance, Management) tools for tracking and risk detection.
- Benefits / Outcomes
- You will gain the confidence to lead the development and deployment of secure AI applications within your organization.
- Develop a proactive mindset towards AI security, enabling you to anticipate and mitigate emerging threats before they materialize.
- Enhance your ability to protect critical organizational data and intellectual property from AI-specific vulnerabilities.
- Become proficient in designing AI systems that not only perform effectively but also adhere to stringent security and ethical standards.
- Acquire practical strategies for building a resilient AI infrastructure, capable of withstanding sophisticated cyberattacks.
- Elevate your organization’s AI security posture, fostering trust and confidence among stakeholders and users of AI solutions.
- Be equipped to articulate and implement a comprehensive AI security roadmap for your organization, covering both immediate needs and future growth.
- Contribute to the responsible and secure advancement of AI technologies within your professional domain.
- This course empowers you to be at the forefront of securing the AI revolution, ensuring its benefits are realized without compromising safety and security.
- PROS
- Focuses on the unique and evolving threat landscape of AI, especially Generative AI and RAG.
- Provides a practical, architectural approach to AI security, moving beyond theoretical concepts.
- Covers a broad spectrum of AI security concerns from data to infrastructure.
- Emphasizes integration of security throughout the AI development lifecycle.
- Designed for a small group, promoting deeper engagement and personalized learning.
- CONS
- Due to its specific focus, it may not cover all aspects of general enterprise cybersecurity.
Learning Tracks: English,IT & Software,Network & Security
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