Podcast AI Compliance: Managing Data Change and Growth
Artificial intelligence (AI) is transforming how data is handled, but with this shift comes new compliance challenges. Mathieu Gorge, CEO of Vigitrust, highlights the risks involved in AI data processing, especially during training phases. As AI systems learn, they generate increasing amounts of data, which complicates compliance efforts.
Key questions arise: What data enters the AI system? What is produced? Where does it go? Who can access it? And how is it stored? Ensuring compliance requires clear answers to these questions, along with strong security and governance frameworks. Integrating AI compliance into the organisation's security culture is essential.
Current Trends in AI Compliance
AI adoption is accelerating globally, and regulations are beginning to catch up. The European Union has introduced AI-specific rules, while frameworks like NIST’s AI framework are adapting to the technology. Security associations such as the Cloud Security Alliance, ISSA, and ISACA are developing their own AI guidelines.
Expect more AI regulations nationally and internationally—similar to how privacy laws evolved. Historically, IT security started with numerous standards before consolidating into a few key ones like HIPAA, PCI, NIST, ISO, and CIS. The goal is to streamline AI governance similarly, focusing on data classification, privacy, and storage.
What is AI Governance?
At its core, AI governance addresses how data is sourced, processed, and managed within AI systems. It ensures organisations have the right to use input data for AI purposes and that output data remains compliant.
- Where does the data come from?
- Does AI processing change the data format or type?
- Are there safeguards controlling who accesses the data?
- How and where is data stored?
- What are the retention and reporting requirements?
AI systems often multiply data volume, creating new storage and compliance challenges. Organisations need to monitor their AI ecosystems closely—from input to output, access controls, and storage locations—to maintain compliance.
How CIOs Should Approach AI Compliance
The Chief Information Officer (CIO) should take responsibility for understanding the data lifecycle within AI systems. This includes data entering the system, data leaving it, and how third parties access it. Collaboration with Chief Security Officers (CSOs) and security teams is crucial.
Using resources like the International Association of Privacy Professionals (IAPP) AI law and policy tracker helps keep up with global AI regulations and data requirements.
CIOs must foster a culture that balances AI adoption with solid data management and security practices. Employee training is vital—just like how organisations teach safe email and social media use, staff should understand AI risks and compliance responsibilities.
Integrate AI compliance into your organisation’s DNA by:
- Reviewing your AI ecosystem and use cases
- Adopting relevant AI policies from trusted frameworks
- Embedding these policies into daily operations and culture
AI will continue to grow in business applications. Without proper governance, it can complicate data management. But with clear strategies and compliance frameworks, organisations can harness AI effectively while staying compliant.
For those looking to build or expand AI skills with a focus on responsible AI use, explore practical training options at Complete AI Training.
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