Learn to identify, analyze, and mitigate GenAI threats using modern security playbooks
β±οΈ Length: 6.1 total hours
π₯ 5 students
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- Course Overview
- This course delves into the rapidly evolving intersection of Artificial Intelligence and Cybersecurity, focusing on the practical application of AI-driven security solutions. As AI technologies, particularly Generative AI (GenAI), become more integrated into business processes, they introduce novel attack vectors and vulnerabilities. This program equips participants with the knowledge and skills to proactively defend against these emerging threats. We explore how to leverage AI principles and tools to build robust security frameworks, manage risks, and ensure the integrity and confidentiality of AI systems and the data they process. The curriculum emphasizes a hands-on approach, enabling learners to understand the nuances of AI-specific security challenges and implement effective mitigation strategies in real-world scenarios.
- Target Audience
- This course is designed for a diverse group of IT professionals, including cybersecurity analysts, security architects, software developers, data scientists, AI engineers, and IT managers. It is particularly beneficial for individuals responsible for securing AI applications, managing cloud infrastructure, or implementing data protection strategies in AI-driven environments. Professionals seeking to upskill in the specialized domain of AI security will find this program highly relevant.
- Core Themes Explored
- The Evolving Threat Landscape: Understanding the unique vulnerabilities introduced by AI, especially Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) systems, beyond traditional cybersecurity concerns. This includes exploring novel attack methodologies that exploit the inherent characteristics of AI models, such as prompt injection, data poisoning, model inversion, and adversarial attacks. We will examine how these attacks can lead to data breaches, unauthorized access, manipulation of AI outputs, and the compromise of system integrity.
- Proactive Defense Strategies: Moving beyond reactive measures, this course emphasizes building security into the AI development lifecycle from the ground up. This involves establishing comprehensive security playbooks, developing robust threat modeling techniques specifically for AI systems, and implementing layered security controls. The focus is on creating resilient AI architectures that can withstand and adapt to evolving threats.
- The AI Security Reference Architecture: A deep dive into a structured framework for designing and implementing secure AI applications. This section will cover the fundamental components and principles of this architecture, guiding participants on how to integrate security best practices at each stage of AI development and deployment.
- Operationalizing AI Security: Practical guidance on implementing and managing security controls for AI systems. This includes the deployment of specialized security tools like AI firewalls and runtime protection mechanisms, the configuration of secure access controls, and the establishment of effective data governance policies. Emphasis will be placed on continuous monitoring and adaptation to maintain security posture.
- Maturity and Roadmapping: Developing a strategic approach to AI security maturity. Participants will learn to assess their current AI security posture and create actionable roadmaps for continuous improvement, focusing on the first 30, 60, and 90 days of implementation.
- Requirements / Prerequisites
- A foundational understanding of cybersecurity principles and common attack vectors.
- Familiarity with cloud computing concepts and environments.
- Basic knowledge of AI and machine learning concepts is beneficial but not strictly required, as the course will provide context.
- Experience with software development lifecycle (SDLC) principles.
- A willingness to engage with complex technical concepts and problem-solving.
- Skills Covered / Tools Used
- AI Threat Identification & Analysis: Expertise in recognizing and dissecting sophisticated AI-specific threats.
- Secure AI Architecture Design: Proficiency in applying the AI Security Reference Architecture for robust application development.
- AI System Threat Modeling: Skill in systematically identifying and quantifying risks associated with AI systems.
- Implementation of AI Security Controls: Hands-on experience with deploying and configuring AI firewalls, filtering, and runtime protection.
- Secure AI Development Practices: Building secure AI Software Development Lifecycles (SDLCs) with a focus on data, evaluation, and adversarial testing.
- AI Identity & Access Management: Configuring secure access models for AI tools and endpoints.
- Data Governance for AI: Applying data governance techniques for RAG pipelines and related AI components.
- AI Security Monitoring Platforms (SPM): Utilizing SPM tools for drift detection, violation monitoring, and asset inventory management.
- AI Observability & Evaluation: Deploying tools to track model behavior, performance, and quality metrics.
- AI Security Control Stack Assembly: Integrating various security measures into a cohesive defense strategy.
- Strategic AI Security Roadmapping: Developing actionable plans for enhancing AI security maturity.
- Benefits / Outcomes
- Participants will gain a comprehensive understanding of the unique cybersecurity challenges posed by AI technologies.
- They will be equipped to proactively identify, analyze, and mitigate GenAI-specific threats.
- The ability to design and implement secure AI applications using established architectural frameworks will be developed.
- Learners will be proficient in conducting threat modeling exercises tailored for AI systems.
- Practical skills in deploying and managing AI security controls, including firewalls and runtime protection, will be acquired.
- Participants will be able to integrate robust security measures throughout the AI development lifecycle.
- They will be capable of configuring and managing secure access and data governance for AI environments.
- The course fosters the ability to leverage security platforms for monitoring and managing AI assets.
- Graduates will be prepared to build strategic roadmaps for advancing AI security within their organizations.
- PROS
- Timeliness and Relevance: Addresses a critical and rapidly growing area of cybersecurity with immediate real-world applications.
- Practical Focus: Emphasizes hands-on application of security principles and tools, moving beyond theoretical concepts.
- Holistic Approach: Covers the entire AI security lifecycle, from design to operational monitoring and strategic planning.
- Expert-Led Content: Designed to provide insights from current industry challenges and best practices.
- CONS
- Rapidly Evolving Field: The AI security landscape changes quickly, requiring continuous learning beyond the course to stay completely current.
Learning Tracks: English,IT & Software,Network & Security
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