To say that AI has simply changed Records Information Management (RIM) would be a serious understatement. Widespread use of AI touches nearly every aspect of our professional and personal lives, transforming once static records into living elements of our lives and our work.
But as AI usage increases, so does the challenge of keeping those living records accountable. For RIM professionals, interacting with AI records raises a new question: how do we know what changed, when, and why?
Key Records Management AI Data Traceability Takeaways
- Generative AI turns static data into dynamic outputs (chats, prompts, auto-generated metadata) that traditional record retention schedules (RRS) cannot adequately track.
- Effective compliance requires understanding data provenance (the total chain of custody and context) rather than simple point-to-point data lineage.
- Build data traceability by categorizing records based on the underlying business processes and data sources, not by the specific AI tools used.
Records are dynamic assets of your organization, impacted by employees, upper management, and now AI tools. These tools are supposed to increase efficiency, but often cause confusion when trying to track, manage, and audit records, especially ones impacted by AI. Data traceability offers a way to manage that chaos.
Why Traceability Matters Now More Than Ever
AI records can include the following:
- User-generated chats and prompts
- AI-edited documents, both drafted and finalized versions
- Internal policies and procedures governing AI-usage
- AI-generated images and infographics
- AI summaries from different meetings, including transcripts and notes
The word ‘Provenance’ is significant here. Provenance is the historical record that catalogs the origins, ownership, contexts and metadata of a record across its lifecycle. We often hear about lineage, what happens when data moves from point A to point B. Provenance is just as important, as it acts as the custody chain as data changes shape, ownership, and contexts in your organization.
AI records are unique in how they are often accompanied by multiple sources of data: metadata, source data, text data. Often AI records are built off other internal records, since generative models produce new content by drawing on the data they are trained on and the material they are given. Traditional record retention schedules are ill-equipped to manage these types of records. Compounding the problem, many AI records, such as chats, prompts, and auto-generated transcripts, are created outside official systems, leaving them ungoverned and easy to overlook. Long gone are the days where you can store physical records in file folders and destroy them at the end of a retention period.
AI-generated records ask us:
- What version is the record of truth?
- How do we prove it?
Ways Data Traceability can be integrated into your RRS
Data traceability also concerns handling requirements, or how records are stored and changed in their lifecycle.
Handling requirements ask:
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- Are signatures required?
- What format should records be kept in?
- Where should records be stored?
- Are there language requirements?
- Is anonymization required when handling personal data?
Handling requirements provide the audit trail that good data traceability relies on. But how do we implement these handling requirements when managing AI records? There are a few strategies to build data traceability into your records retention schedule (RRS).
Back to Basics
We have touched on how provenance plays a part in data traceability and that generative AI records are built off multiple chains of data. Good traceability then is about looking at the provenance behind what is generating records, which can include information like the system of origin, context, and the multiple types of metadata that exist (structural, administrative, and descriptive). Categorizing AI generated records based on what data is being used to create helps streamline the ability to audit and ensure compliance. Following the chain begins the audit trail that is the first step to good data traceability.
Categorizing by Process, Not by Tool
AI models are updated frequently, and with each update comes a new set of tools and features. It can be tempting to include every new upgrade into your RRS, but it’s important to keep from creating a new record type or category with each new feature. Stability and consistency should be the driving features of your RRS.
AI Data Traceability in Action
By implementing a process-driven mindset, data traceability becomes a streamlined methodology that isn’t distracted by the bells and whistles AI offers.
It can look like different record series created to distinguish AI-edited drafts from the final versions that are used. Or perhaps a record series is created to retain the metadata used when generating AI records, preserving a visible audit trail. Another example can include storing user-generated chats with AI in a specific digital folder or database or perhaps requiring a signature in a log whenever an AI record is changed.
AI records should be captured in their totality when their output and purpose serve a clear role in your organization. It would be excessive to capture every piece of metadata or the whole custody chain when working with non-records, so it’s important to be discerning when looking at what generative AI records need to be tracked or audited for compliance.
Together, these practices answer the two questions we started with: the version of record is the one the audit trail can identify, and the trail itself is how you prove it.
Data traceability also has roots in the decision-making processes of employees handling or creating AI records. If the individual components of traceability, which include version histories and change logs, are not integrated in the day-to-day record management of your organization, then all you have are fragmented routines. Good data traceability starts with people, not with the records.
It’s important to note that handling requirements are also built into the law and regulations governing recordkeeping. For this reason, it is essential to collaborate with counsel to ensure compliance.
AI-Overwhelm (and Inner Peace)
With AI-usage ever increasing, it can be easy to feel overwhelmed. On top of the different models available, there are so many ways to use AI, and with limitless options, it can be easy to get lost in the details.
Data traceability is one of the cornerstones of a defensible RRS in the digital age. It’s not about designing a system that is too complicated to implement, but about creating a methodology designed to modernize and simplify. What’s more modern than AI? Your organization should have the RRS to match. By categorizing AI records based on what data is used to generate them and then mapping each type to an existing retention category, you can begin building good traceability from the ground up.
Disclaimer: The purpose of this post is to provide general education on information governance software. The statements are informational only and do not constitute legal advice. If you have specific questions regarding the application of the law to your business activities, you should seek the advice of your legal counsel.