Secure Your AI Systems: Learn OWASP Top 10 LLM Risks, Real Incidents, and Practical Mitigations
What you will learn
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Learn to identify threats across the LLM lifecycle: training, prompting, and deployment phases.
Gain practical mitigation strategies to secure GenAI systems and apply best practices effectively.
Explore case studies of real-world AI security incidents and their impact on organizations.
Gain practical mitigation strategies to secure GenAI systems and apply best practices effectively.
Add-On Information:
- Master the critical security vulnerabilities impacting generative AI, going beyond theoretical knowledge to understand practical exploitation techniques.
- Develop a robust defense posture by integrating security principles throughout the entire AI development and operational lifecycle.
- Deconstruct real-world AI security breaches to understand the anatomy of attacks, their root causes, and the cascading effects on businesses.
- Implement proactive security measures for prompt engineering, mitigating risks like prompt injection, data leakage, and denial-of-service attacks.
- Understand the security implications of model training data, including bias, poisoning, and privacy concerns, and learn to mitigate these risks.
- Fortify AI deployment environments against common attack vectors such as adversarial attacks, unauthorized access, and model inversion.
- Gain actionable insights into the OWASP Top 10 for Large Language Models, translating these risks into practical, implementable security controls.
- Learn to audit and assess the security posture of generative AI applications, identifying weaknesses before they can be exploited.
- Explore emerging threats and defense strategies in the rapidly evolving landscape of generative AI security.
- Build a foundational understanding of legal and ethical considerations related to AI security and responsible AI development.
- Acquire the skills to communicate AI security risks and mitigation strategies effectively to technical and non-technical stakeholders.
- Understand the importance of secure coding practices specifically tailored for generative AI applications.
- Explore the role of access control and authentication in safeguarding AI models and their outputs.
- Learn to leverage security testing tools and methodologies relevant to generative AI systems.
- Develop a mindset of continuous security improvement for generative AI initiatives.
- PRO: Provides a practical, hands-on approach to securing generative AI, bridging the gap between theoretical concepts and real-world application.
- PRO: Equips learners with immediate, applicable skills to enhance the security of existing and future AI projects.
- PRO: Covers a comprehensive overview of the most pressing generative AI security threats as defined by industry-leading frameworks.
- CON: The rapidly evolving nature of GenAI means continuous learning is required beyond the course to stay current with the latest threats and defenses.
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