Master the latest OWASP list for AI, protect Large Language Models apps, and build secure, resilient systems
β±οΈ Length: 4.3 total hours
β 4.14/5 rating
π₯ 3,968 students
π September 2025 update
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- Course Overview
- Explore the critical intersection of AI and cybersecurity, specifically addressing emergent risks in Large Language Models. This course uniquely positions professionals at the forefront of AI security, providing actionable insights into securing next-generation intelligent systems with proactive, defense-in-depth strategies.
- Understand the paradigm shift LLMs introduce to application security, highlighting why traditional measures are often insufficient. Prepare to anticipate and neutralize novel attack vectors by integrating security considerations from design to continuous monitoring for AI applications.
- Gain a comprehensive perspective on building resilient LLM-based solutions, fostering trust and integrity. This includes securing data pipelines, protecting model intellectual property, and ensuring ethical AI usage from a security standpoint in a rapidly evolving tech environment.
- Requirements / Prerequisites
- A foundational understanding of general cybersecurity principles and common web application vulnerabilities.
- Familiarity with basic software development concepts and experience with at least one modern programming language (e.g., Python).
- Conceptual grasp of machine learning fundamentals, particularly neural networks and large language model architectures.
- An inquisitive mindset and keen interest in AI security, willing to engage with complex, cutting-edge topics.
- Skills Covered / Tools Used
- Develop expertise in threat modeling for AI/LLM architectures, identifying unique attack surfaces in generative models.
- Acquire proficiency in robust input validation and sanitization techniques for natural language processing, mitigating malicious manipulation.
- Master strategies for secure API design and integration when connecting LLMs to external services, ensuring data integrity.
- Gain practical experience with security testing methodologies for LLMs, including specialized fuzzing, red teaming, and automated scanning.
- Cultivate the ability to establish and enforce strong access control and identity management within LLM ecosystems, safeguarding model parameters.
- Learn to implement comprehensive logging, monitoring, and alerting systems to detect anomalous behavior and security incidents in LLM applications.
- Benefits / Outcomes
- Transform into an indispensable AI security champion, proactively safeguarding LLM investments against sophisticated cyber threats.
- Enhance career prospects by adding highly sought-after, specialized AI security skills, becoming a valuable asset in AI development and cybersecurity.
- Gain confidence to design, develop, and deploy secure LLM applications adhering to high standards of data protection and ethical AI.
- Contribute directly to responsible AI innovation, setting benchmarks for secure development and fostering security awareness around LLM deployments.
- Receive practical frameworks and resources applicable to real-world LLM security challenges, accelerating effective defense strategies.
- PROS
- Highly relevant and cutting-edge content addressing critical, emerging threats in AI security.
- Provides practical, actionable insights immediately applicable to real-world LLM development.
- Taught by industry experts, ensuring up-to-date information and best practices.
- Significantly enhances career opportunities in the rapidly growing AI and cybersecurity sectors.
- Establishes a foundational understanding for advanced studies in AI safety and security research.
- CONS
- Requires dedicated time commitment and a foundational understanding of technical concepts to fully internalize the material.
Learning Tracks: English,IT & Software,Other IT & Software
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