Design, deploy, and govern secure, compliant AI systems used in real enterprises
β±οΈ Length: 3.8 total hours
π₯ 15 students
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Course Overview
- This intensive 3.8-hour course, designed for a small cohort of 15 students, focuses on the critical intersections of artificial intelligence, cybersecurity, and regulatory adherence. It directly addresses the contemporary imperative to design, deploy, and govern secure, compliant AI systems used in real enterprises.
- Participants will delve into protecting AI models and data from adversarial attacks, ensuring data privacy, and managing algorithmic bias within evolving global regulatory frameworks.
- The curriculum spans foundational concepts of AI security, including threat modeling unique to machine learning, alongside robust AI governance principles for establishing ethical guidelines, accountability, and risk management.
- A significant portion is dedicated to AI compliance, providing insights into navigating complex legal landscapes such as GDPR, HIPAA, CCPA, and emerging AI-specific legislation like the EU AI Act.
- This course is built on a practical, enterprise-centric approach, aiming to equip professionals with actionable strategies and frameworks to build trustworthy and responsible AI systems from conception to ongoing operation.
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Requirements / Prerequisites
- A foundational understanding of core Artificial Intelligence and Machine Learning concepts, including model training and deployment cycles, is expected.
- Basic familiarity with programming paradigms (e.g., Python) will aid in comprehending architectural examples; heavy coding is not required.
- An interest in cybersecurity principles, data privacy regulations, or corporate governance best practices will enhance the learning experience.
- General awareness of cloud computing environments (e.g., AWS, Azure, GCP) where AI systems are frequently hosted.
- No advanced legal expertise or deep AI research background is necessary, but a willingness to engage with interdisciplinary topics is crucial.
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Skills Covered / Tools Used
- AI Threat Modeling & Mitigation: Identify AI-specific vulnerabilities (data poisoning, model evasion, data leakage) and implement defense strategies.
- Adversarial Robustness Techniques: Enhance AI model resilience against adversarial inputs.
- Secure MLOps Practices: Integrate security across the machine learning lifecycle: data ingestion, deployment, continuous monitoring.
- AI Governance Framework Development: Establish ethical AI principles, accountability structures, and risk assessment frameworks for AI applications.
- Regulatory Compliance Interpretation: Interpret and apply key provisions from global data privacy regulations (GDPR, CCPA) and emerging AI-specific laws (EU AI Act, NIST AI RMF).
- Explainable AI (XAI) for Compliance: Leverage XAI techniques for model transparency, auditability, and compliance with “right to explanation” mandates.
- Data Privacy Enhancing Technologies (PETs): Explore PETs (differential privacy, federated learning) for privacy-preserving AI systems.
- Audit & Assurance for AI: Learn methodologies for conducting AI audits, assessing fairness, bias, and overall compliance posture.
- (Conceptual) Tools/Frameworks: Discussion of principles behind tools like IBM AI Fairness 360, Google’s What-If Tool, Microsoft Responsible AI Toolbox, and OWASP Top 10 for LLM applications.
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Benefits / Outcomes
- Empowerment to act as a crucial link between technical AI, legal, and compliance teams, fostering a holistic approach to responsible AI.
- Enhanced capability to proactively identify, assess, and mitigate complex security and ethical risks associated with enterprise AI deployment.
- Proficiency in articulating and implementing robust AI governance policies that align with organizational values and stakeholder expectations.
- Confidence in navigating the evolving landscape of AI regulations, ensuring deployed AI solutions are innovative, legally sound, and ethically responsible.
- Ability to contribute to the strategic development of a secure and compliant AI roadmap, safeguarding reputation and fostering trust.
- Preparation for emerging roles focused on AI risk management, AI ethics, or AI compliance auditing.
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PROS
- Highly Focused and Timely: Addresses an urgent industry need for professionals skilled in AI security, governance, and compliance.
- Enterprise-Centric Practicality: Content tailored for real-world application in corporate environments, offering actionable insights.
- Small Group Intensive Learning: Limited class size (15 students) promotes highly interactive and personalized learning.
- Concise and Efficient: At 3.8 hours, allows busy professionals to quickly gain critical, high-impact knowledge.
- Interdisciplinary Approach: Integrates technical security with legal, ethical, and governance considerations for a well-rounded perspective.
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CONS
- The concise duration, while efficient, may necessitate a high-level overview of certain complex topics, potentially limiting deep dives into all specific tools or regulatory nuances.
Learning Tracks: English,Development,Data Science
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