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Who it's for:
- CISOs and security leaders
- Security architects and engineers
- AI/ML engineers and AI product owners
- Compliance and privacy officers
- Data protection officers
- Cloud and DevSecOps teams
Data Security within AI Environments
Release Date: 12/03/2025
As organizations adopt large language models, multi-modal AI systems, and agentic AI, traditional safeguards must evolve. This publication provides a comprehensive, practitioner-focused overview of how AI reshapes modern data protection. Aligned to the CSA AI Controls Matrix (AICM), this guide outlines AI data security challenges and maps them to essential AI risk management controls.
Understand why you must apply the CIA Triad differently in AI-driven ecosystems. Learn about emerging risks like cross-modal data leakage, data poisoning, insecure annotation pipelines, and unmonitored AI tool usage.
Examine the regulatory landscape surrounding data protection in AI, including GDPR, CCPA, HIPAA, and global AI governance frameworks. Learn about privacy-enhancing technologies such as differential privacy, homomorphic encryption, secure multi-party computation, and tokenization.
Finally, explore case studies (including Snowflake, OpenAI, and DeepSeek) to understand how weak governance can lead to critical failures.
Key Takeaways:
- How AI systems introduce new data security risks across the full AI lifecycle
- How the AICM maps to AI-specific data protection needs
- Guidance on privacy-enhancing technologies and secure data handling for AI
- Regulatory, ethical, and compliance considerations for AI deployments
- Real-world AI incidents illustrating common data security failures
- Best practices for securing data, models, pipelines, and AI infrastructure
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