Editorial
Unlike traditional, legacy data sets, AI-generated content and insights tend to live inside a vacuum, created, used and taken for granted without proper governance. Unfortunately, for companies that don’t provide proper oversight — and proactively govern AI data — they’re susceptible to unseen risks.
In other words, ungoverned AI data can poison the well, making companies vulnerable to legal or compliance issues, IP concerns, holes in data sourcing and accountability and inconsistent data results.
At the same time, for leaders of data management who understand the importance of governing AI-generated insights and data, the challenge is to do so proactively rather than continually working backwards to fix or be reactive to data issues as they arise.
Table of Contents
- Ungoverned AI: What Can Go Wrong
- AI Output Governance: Tips Toward Improved Management
- Cross-Functional Collaboration Is Key
Ungoverned AI: What Can Go Wrong
Rather than simply taking AI-synthesized data at face value and pulling it out of a system, companies need to ensure all synthetic data and GenAI-powered insights are tagged, tracked, traced, stored and properly governed.
Enterprises can move too quickly, ingesting AI-powered data out of a system, saving it to a file share and then rolling it into their systems without tracing the history. Unfortunately, companies lacking proper AI data governance can be subject to unexpected results, legal issues and making decisions on suspect sources.
Situations to keep an eye on include:
- Regulatory frameworks differ around the world, such as the EU AI Act, legislation that requires companies to document AI system behavior. Companies not tagging AI-generated content could be exposed to noncompliance.
- A company that uses GenAI to develop an infographic, which cleverly uses an image of a celebrity or a piece of art, might require prior permission, causing the company to pay royalties.
- Similarly, marketing copy generated for a campaign might experience a hallucination, which causes AI to directly borrow from a text or quote that can’t be used, raising legal issues.
- Large synthetic data sets built with AI are used to train models, and those models are pushed into production. Companies that don’t track who created that data, when and where might lose that foundational knowledge going forward, causing teams to recreate the data set repeatedly.
Continually recreating data sets through AI causes inconsistencies in the data, as each pull might be different. Additionally, constantly remaking large synthetic data sets — only to have them disappear — is like building and melting icebergs. AI-driven insights are incredibly helpful and convenient for business teams to leverage, but the process doesn’t need to be reckless and wasteful.
Related Article: Why Bad Data Is Blocking AI Success — and How to Fix It
AI Output Governance: Tips Toward Improved Management
From the very beginning, before insights are generated, enterprise organizations need best practices in place to support how AI data gets governed. This includes foundational steps such as tagging, tracing, storing and establishing accountability around AI data.
Other key tactics include:
- Pull all data sources to the center. Companies need to centralize every data source — AI-generated, internal data, external sources, etc. — into the cloud, where it can be tagged, tracked and not get filtered to different locations outside the center.
- Eliminate silos. Similarly, different business teams may vary in how they use and create data, naturally causing them to work in silos. All teams need to work together from a single source of truth.
- Don’t take AI for granted. Culturally, companies should impress upon business teams to not take AI for granted. Just because insights, content and images are easy to generate with AI, it doesn’t mean governance steps should be overlooked.
- Be vigilant in how AI gets tagged. Ensure users note AI outputs by what specific AI model was used and what version, including the timestamp of generation, each user initiating a request, what content is getting pulled (analysis, recommendations, summaries, content) and apply the results with confidence scores.
Cross-Functional Collaboration Is Key
Companies that deliver a tight data management system rely on total collaboration across the company. IT and legal teams, compliance officers and each business unit must work together to develop guidelines that work for them and are easy to follow to protect the organization.
AI works fast, and users tend to leverage the models for immediate satisfaction, but a lack of governance creates risk and compliance concerns. Tracing, tracking, storing and properly building AI data can improve the overall AI literacy of their systems and accelerate AI ROI by delivering dependable results and reducing redundant workflows.
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Going forward, regulations around AI expect to intensify. Companies tagging, monitoring and governing AI outputs now will build infrastructure that’s able to navigate regulatory changes and be a scalable, profitable asset.
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