Artificial intelligence (AI) has quietly infiltrated various core functions across companies, not through grand transformations, but through gradual adoption. Departments like HR and compliance are now utilising large language models (LLMs) to enhance their processes. However, alongside this rise, a concerning aspect has emerged: the neglect of data provenance, which is essential for effective governance.
The Importance of Provenance in AI Governance
Provenance goes beyond mere logging of data; it’s crucial for understanding data’s origins, transformations, and the accountability chain. In environments dependent on LLMs, where data outputs can be unpredictable, this lineage can become obscured, risking compliance and governance.
AI Sprawl and the Challenges of Decentralised Systems
AI implementation in companies isn’t a cohesive effort; instead, it leads to an array of tools operating independently. This decentralisation can result in sensitive data being processed without proper oversight, creating potential governance crises.
Regulatory Landscape: Evolving, Not Lagging
Contrary to popular belief, regulations like GDPR are evolving to align with AI usage, but the challenge lies with current systems being unable to comply. Questions of liability and accountability remain troubling, particularly in audit scenarios.
Best Practices for Modern AI Governance
CISOs should prioritise a governance framework that starts with infrastructure, focusing on:
- Continuous, automated data mapping to understand data flows.
- AI-aware records of processing activities (RoPA) that includes model behaviours.
- Dynamic consent mechanisms that require ongoing user agreement.
- Prompt and output audit logging to capture sensitive interactions.
- Classification and governance of AI outputs based on their context.
The CISO’s Changing Role
The role of the CISO is rapidly evolving to encompass more than just data protection. They must now consider the context, legality, and ethical implications of AI usage, collaborating closely with other departments for comprehensive governance.
Trust in AI: Building through Traceability
As we navigate the complexities of AI, having robust systems that can provide clear answers regarding data usage will be essential for establishing trust. The fix isn’t in policies alone, but in ensuring transparency and accountability through effective governance initiatives.
Why should I read this?
This article is a wake-up call for CISOs and data governance professionals. It dives deep into the importance of data provenance in AI strategies, highlighting key best practices to mitigate risks associated with AI sprawl. If you want to stay ahead in the ever-evolving landscape of AI regulation and governance, this is a must-read!