With growth in artificial intelligence, securing and protecting data has never been more critical. This presentation discusses the importance of data privacy, compliance with regulations, and the growing need for enhanced data security measures.
We will explore security concerns associated with generative AI, such as data bias, social engineering, and model manipulation. This session introduces concepts like adversarial machine learning, data poisoning, and prompt injection, highlighting the potential risks and vulnerabilities in AI systems. Ethical considerations and responsible use of data in AI practices are emphasized, ensuring that organizations can navigate the complexities of AI with integrity.
Practical strategies and techniques for enhancing data security are discussed, including adversarial training, differential privacy, and continuous monitoring of training data. These recommendations provide data professionals with the tools they need to protect their data assets and stay ahead of emerging threats in the AI era.
Key Takeaways:
1. Data Protection in AI: AI opens new attack surfaces, making data protection more challenging yet crucial
2. Ethical AI Practices: Responsible use of data and ethical considerations are essential to navigate AI complexities
3. Advanced Security Measures: Techniques like adversarial training and differential privacy can enhance data security
4. Continuous Monitoring: Ongoing monitoring and protection of training data are vital to maintaining robust AI systems
AI as a Security Tool: AI can be leveraged to secure data, providing innovative solutions to emerging threats

Perfect for an audience that works in roles that support or integrate with AI.