Protect sensitive data, prevent AI misuse, master prompt security, and build responsible GenAI practices at work.
β±οΈ Length: 2.1 total hours
β 4.50/5 rating
π₯ 25 students
π October 2025 update
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- Course Overview
- Embark on a critical journey into the evolving landscape of data security, specifically addressing the profound impact of Generative Artificial Intelligence (GenAI). This course is designed for professionals seeking to navigate the complex intersection of cutting-edge AI capabilities and the imperative of safeguarding sensitive information in today’s dynamic digital environment.
- Gain a comprehensive understanding of the inherent security challenges posed by GenAI, from the foundational principles of data ingestion and model training to the generation of outputs. We will dissect the vulnerabilities that can arise at each stage, providing a robust framework for proactive defense.
- Explore the strategic considerations behind enterprise-level AI adoption, including the rationale for implementing stringent access controls and the architectural components that underpin robust data protection within GenAI workflows.
- Develop a nuanced appreciation for data classification, understanding the distinct characteristics and security implications of public, internal, confidential, and regulated data sets. This knowledge is crucial for making informed decisions about what information can be responsibly leveraged with GenAI tools.
- Acquire practical, actionable strategies for mitigating data exposure risks. This includes the application of advanced techniques such as data masking and anonymization, ensuring that even when using AI-powered tools, your organization’s most valuable assets remain protected.
- Master the art of secure prompting, recognizing its pivotal role in preventing accidental data leakage and unintended AI behavior. We will delve into the psychological and technical aspects of crafting prompts that elicit desired outcomes without compromising security.
- Become adept at identifying and understanding the nuances of adversarial attacks targeting GenAI systems, with a particular focus on prompt injection. Learn to recognize the subtle and overt ways malicious instructions can be embedded within seemingly innocuous text and documents.
- Distinguish between various forms of prompt injection, analyzing the distinct mechanisms through which direct and indirect attacks can subvert AI functionality and lead to data breaches or manipulation.
- Understand the ethical considerations and regulatory frameworks surrounding GenAI and data security, ensuring your organization’s practices are not only secure but also compliant and responsible.
- This course is a vital resource for anyone responsible for data governance, cybersecurity, AI implementation, or risk management within an organization leveraging or planning to leverage Generative AI technologies.
- Requirements / Prerequisites
- A foundational understanding of general data security principles and concepts is beneficial.
- Familiarity with the basic capabilities and applications of Generative AI is recommended, though not strictly required, as the course will cover essential context.
- A professional interest in cybersecurity, data privacy, or AI ethics.
- Access to a stable internet connection for the duration of the course.
- Skills Covered / Tools Used
- Data De-identification Techniques: Proficiency in methods like generalization, suppression, and perturbation for protecting PII and sensitive data.
- Prompt Engineering for Security: Crafting secure, unambiguous prompts to steer AI behavior and prevent data exfiltration.
- Adversarial AI Awareness: Recognizing patterns and methodologies of common AI-based attacks, particularly prompt injection variations.
- Risk Assessment Frameworks for GenAI: Evaluating potential security threats and vulnerabilities introduced by GenAI integration.
- Data Governance Policies for AI: Developing and implementing internal guidelines for the responsible use of AI with corporate data.
- Understanding of LLM Architectures (Conceptual): Grasping how Large Language Models process and generate information to identify security weak points.
- Case Study Analysis: Deconstructing real-world incidents to derive practical security lessons.
- Conceptual understanding of enterprise security solutions applicable to AI.
- Benefits / Outcomes
- Enhanced Data Protection: Significantly bolster your organization’s defenses against data breaches and unauthorized access in the context of GenAI usage.
- Mitigated AI Misuse Risks: Develop strategies to prevent malicious actors from exploiting GenAI for harmful purposes or data theft.
- Improved Compliance Posture: Ensure your organization’s GenAI practices align with relevant data privacy regulations and ethical standards.
- Strategic Advantage: Equip yourself with the knowledge to confidently and securely integrate GenAI into business operations, fostering innovation while maintaining trust.
- Proactive Threat Identification: Gain the ability to anticipate and identify emerging security threats unique to the GenAI ecosystem.
- Responsible AI Implementation: Become a champion for secure and ethical AI adoption within your workplace.
- Career Advancement: Acquire in-demand skills crucial for roles in cybersecurity, data science, risk management, and AI governance.
- PROS
- Highly relevant and timely content addressing a critical emerging security challenge.
- Practical, actionable insights applicable to real-world scenarios.
- Focus on both technical vulnerabilities and ethical considerations.
- Updated content reflecting the latest trends in GenAI security.
- Expertly curated to provide a comprehensive understanding in a concise format.
- CONS
- May require some pre-existing technical background for deeper technical discussions.
Learning Tracks: English,Business,Other Business
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