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AISF: AI Security Fundamentals

Artificial intelligence (AI) systems are becoming integral to everything from enterprise operations to national infrastructure, and so the stakes for ensuring their security have never been higher. With rapidly evolving regulatory landscapes and increasingly sophisticated threat vectors, organizations must understand not only how AI systems can be exploited—but how to protect them. AI Security Fundamentals, instructed by Sunil Soares, offers a critical foundation in navigating this complex terrain by exploring the intersection of AI governance, compliance, and system security.

As AI becomes embedded in critical systems across industries and governments, securing these technologies is no longer optional—it’s essential. With the rise of advanced threats and tightening regulations, organizations must be proactive in identifying risks and protecting AI systems from exploitation.

AI Security Fundamentals, taught by Sunil Soares, equips professionals with the knowledge to navigate the intersection of AI governance, compliance, and cybersecurity. The course explores real-world vulnerabilities like prompt injection, model poisoning, and evasion attacks, while addressing regulatory requirements such as the EU AI Act.

Participants will learn key defense strategies including red teaming, federated learning, synthetic data, and privacy-enhancing technologies. The course also covers AI Security Posture Management (AI-SPM), guardrails, and tools from top vendors—giving learners a practical foundation for building secure, compliant, and trustworthy AI systems.

By the end of the AI Security Fundamentals course, participants will be able to:

Understand AI governance frameworks and how they align with security requirements outlined in regulations such as the EU AI Act
Identify and mitigate a wide range of AI security threats, including:
- Prompt injection and jailbreak attacks
- Data poisoning (availability, targeted, and backdoor attacks)
- Evasion techniques (white-box, black-box, and transfer attacks)
- Privacy breaches such as data extraction, membership inference, and model inversion
- Model integrity attacks and AI agent misuse
Analyze real-world case studies involving attacks on large language models and agentic AI systems
Evaluate and apply AI security tools and approaches, including:
- AI Security Posture Management (AI-SPM)
- Red teaming simulations
- Federated learning for secure collaboration
- Synthetic data generation for privacy protection
- Guardrails for safe AI agent deployment
- Advanced privacy-preserving technologies such as homomorphic encryption, SMPC, and zero-knowledge proofs
Understand the vendor landscape for AI governance and security tools.
Recognize how AI security is foundational to robust AI governance and aligns with frameworks like NIST, MITRE ATLAS, and OWASP Top 10 LLM

Who Should Take This Course?

This course is ideal for cybersecurity professionals, risk managers, AI engineers, compliance officers, IT leaders, and data governance experts responsible for securing and overseeing AI systems. It’s also valuable for legal and policy professionals seeking to understand the intersection of AI security, governance, and regulation. Whether you're building, deploying, or managing AI, this course provides the practical tools and knowledge needed to protect AI systems from threats, ensure regulatory compliance, and align with industry frameworks like NIST, MITRE ATLAS, and the EU AI Act.

This Course Includes: