Understanding the Shift
AI projects often fail not due to flawed algorithms but because organizations use unprepared, unvetted, or rights-restricted data. As AI transitions from an experimental tool to an enterprise necessity, a critical gap has emerged: most information governance programs aren't equipped to determine if specific datasets are suitable for AI consumption.
The change isn't technical, it's operational. Your team must now assess data fitness before it enters an AI system, not just manage the output. This requires adapting five core information governance practices to ensure your data is ready for AI, rather than a liability.
Key Considerations
Lineage Determines Legal Risk
The origin of your data dictates whether it's legally usable in an AI project. Privacy restrictions, intellectual property constraints, and contractual limitations all depend on lineage. For instance, if you're using customer communications in a sentiment analysis model, you must confirm whether consent includes AI processing. Similarly, verify that third-party datasets have usage rights that extend to AI.
The practical test: Can you document the origin and rights status of every dataset feeding your model? If not, you're on shaky ground.
Access Controls and AI Compatibility
Your permissions model likely restricts access to sensitive data like salary information or customer health records. However, enterprise AI tools often access your entire data corpus. An employee query might inadvertently reveal restricted information because the model doesn't respect existing access controls.
This isn't a simple configuration issue. It's a fundamental mismatch between your data governance and AI's data retrieval methods. You need to map which datasets your AI can access and ensure this doesn't violate your controls.
Model Training Alignment Over Sophistication
A model trained on structured financial data won't perform well on unstructured legal memos, even if both datasets are clean. The alignment between training data and input data types is crucial for producing useful output.
Before selecting data for an AI project, verify the model's training data. If there's a mismatch, you'll need different data or a different model. Data preparation alone can't fix a fundamental training-input misalignment.
Data Preparation is Essential
Using a vendor's pre-trained model doesn't eliminate the need for data preparation. Input quality directly affects output reliability. Issues like duplicates, inconsistent formatting, and outdated information degrade model performance.
Preparation steps vary by model type. Predictive models require different cleaning than generative ones. Every AI project needs a documented preparation process before data enters the system.
Monitor for Model Degradation
Data changes, business processes evolve, and new record types appear. Without monitoring, your model's inputs may drift from its training, leading to degradation. By the time output quality is noticeably wrong, decisions based on unreliable analysis have already been made.
You need a mechanism to detect when input data characteristics drift from what the model expects. This is an ongoing governance requirement.
Implications for Your Team
Your information governance program now has a gatekeeping role. Every AI project should undergo an IG review to answer:
- Do we have rights to use this data in an AI context?
- Will this AI application expose data to unauthorized users?
- Does our available data match what this model was trained on?
- What preparation steps are required before input?
- How will we monitor for data or model degradation?
If you can't answer these questions, you're not ready to deploy. The risk isn't just poor performance; it's the inadvertent disclosure of protected information, regulatory violations, or decisions based on degraded model output.
Action Items by Priority
Map Your Data Lineage
Start with datasets likely to feed AI projects. Document their origin, rights status, and any contractual or regulatory restrictions. Build a decision matrix to determine which datasets are cleared for AI use, which need legal review, and which are off-limits. This becomes your AI data catalog.
Audit Access Control Compatibility
Test whether your AI tools respect existing permissions. If evaluating a generative AI system, run controlled queries to ensure it doesn't reveal restricted data to unauthorized users. If it does, consider different tools or a restricted deployment model.
Establish Model-Data Alignment Reviews
Before deploying AI, ensure documentation shows the model's training data matches your input data types. Create a checklist: structured vs. unstructured, data formats, record completeness, domain specificity. Reject projects where training and input don't align until resolved.
Define Data Preparation Standards
Develop preparation protocols for the AI models your organization uses. What cleaning steps are required? What quality thresholds must data meet? Who validates preparation is complete? Document this as part of your AI governance framework.
Implement Degradation Monitoring
Set up alerts for significant changes in input data characteristics. Monitor output quality metrics. Schedule regular reviews with technical teams and IG professionals to assess whether the model is still performing as expected.
Your information governance program was built to manage data as a compliance obligation. AI requires you to also manage it as a strategic input. The practices that protect you from regulatory risk now also determine whether your AI investments produce value or expensive failure.
