Master the latest OWASP list for AI, protect Large Language Models apps, and build secure, resilient systems
β±οΈ Length: 4.3 total hours
π₯ 2,052 students
π September 2025 update
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- Course Overview
- Delve into the emergent field of Large Language Model security, establishing foundational understanding of unique threat vectors inherent in AI systems.
- Explore the strategic importance of the 2025 OWASP Top 10 for LLMs, positioning it as the definitive industry standard for identifying and mitigating critical AI application risks.
- Gain insights into methodologies for embedding security throughout the LLM development lifecycle, from design to deployment and monitoring.
- Understand the broader societal and economic implications of insecure AI systems, emphasizing ethical responsibilities and compliance challenges for organizations.
- Prepare to adapt to a rapidly changing threat landscape by adopting a proactive, defensive posture against sophisticated adversarial attacks targeting LLM integrity and confidentiality.
- Requirements / Prerequisites
- Fundamental Programming Knowledge: Basic familiarity with programming concepts, ideally Python, given its prevalence in LLM interactions and security libraries.
- Web Application Basics: A conceptual understanding of web application functions, including client-server architecture, APIs, and data exchange formats.
- Core Security Concepts: Prior exposure to general cybersecurity principles such as authentication, authorization, data encryption, and common attack vectors.
- Enthusiasm for AI: A keen interest in artificial intelligence and machine learning, plus a curiosity to understand the inner workings and potential vulnerabilities of advanced language models.
- Skills Covered / Tools Used
- AI Threat Modeling: Develop the ability to systematically identify potential threats, vulnerabilities, and attack surfaces specific to LLM architectures and integrations.
- Secure LLM Integration Patterns: Master design patterns and best practices for securely integrating Large Language Models into applications, ensuring robust data handling and controlled access.
- Adversarial Defense Engineering: Cultivate expertise in designing and implementing robust defenses against various adversarial techniques, including sophisticated prompt manipulation and model inversion.
- LLM Security Posture Assessment: Learn to conduct comprehensive security audits and assessments of LLM deployments, evaluating adherence to best practices and identifying weaknesses.
- Deployment of Guardrail Technologies: Gain practical experience with deploying and configuring AI-specific security guardrails, content filters, and input/output validation mechanisms.
- Utilizing AI Security Frameworks: Become proficient in leveraging emerging open-source and proprietary security frameworks designed to test, monitor, and protect LLM applications.
- Benefits / Outcomes
- Become an AI Security Leader: Position yourself as a critical asset in the burgeoning field of AI security, equipped to guide organizations in building and maintaining secure AI infrastructures.
- Drive Secure AI Innovation: Contribute directly to the development of trustworthy AI solutions by integrating security-by-design principles, fostering user confidence and regulatory compliance.
- Mitigate Business Risks: Significantly reduce the financial, reputational, and operational risks associated with LLM vulnerabilities, safeguarding sensitive data and ensuring service continuity.
- Advance Your Career: Unlock new career opportunities in AI security engineering, AI risk management, and secure LLM development, a highly sought-after specialization.
- Achieve Regulatory Preparedness: Develop expertise to navigate complex AI regulations and compliance standards, ensuring LLM deployments meet evolving legal and ethical requirements.
- PROS
- Highly Relevant & Up-to-Date: Access content based on the very latest OWASP Top 10 for LLMs (2025), ensuring you learn the most current and critical threats.
- Practical, Actionable Insights: Gain concrete, implementable strategies and techniques that can be immediately applied to real-world LLM security challenges.
- Career-Defining Specialization: Acquire specialized knowledge in a rapidly expanding and high-demand domain, significantly boosting your marketability and professional trajectory.
- Industry-Recognized Standard: Learn from the authoritative OWASP framework, a globally trusted benchmark for application security, providing a credible foundation for your expertise.
- CONS
- Rapid Evolution of the Field: The AI security landscape is constantly changing, requiring continuous self-learning beyond the course to stay abreast of new threats and defenses.
Learning Tracks: English,IT & Software,Other IT & Software
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