When Your AI Vendor Becomes a Risk: A Plain-English Guide to Protecting Your Intellectual Work

Severin Sorensen
3 days ago
6 min read

Updated: 9 hours ago

Most executives and coaches who use AI tools think of their conversation history as something like a search history: transient, forgettable, and replaceable. That assumption is worth revisiting.

Over the past year, many of us have done something more significant inside AI platforms than simple lookups. We have worked through complex coaching frameworks, drafted nuanced client communications, developed proprietary methodologies, refined our voice and reasoning across hundreds of exchanges, and built what amounts to a running record of how we think. That history lives on a vendor's server. And as the regulatory and competitive landscape around AI continues to shift, the question of what happens to that history when a vendor's circumstances change is no longer abstract.

In April and May of 2026, I walked through that scenario firsthand. A regulatory action involving the agentic AI platform Manus prompted me to extract and secure my full archive of work before the situation could evolve further. What I developed in response is a two-phase procedure I am calling the Vendor Exit Playbook. This article translates that procedure into language accessible to executives and coaches who are not technical specialists. For those who want the full technical detail, including Python code samples, forensic hashing guidance, and enterprise-scale patterns, the complete playbook is available on LinkedIn (click here to view).

Why This Matters for Coaches and Executives

If you have spent meaningful time working inside an AI platform, you have likely deposited something valuable there. For example, a coaching engagement that produced a breakthrough framework, a series of conversations that helped you develop a communication style for difficult client situations, or a set of prompts and approaches that took months to refine. These are professional assets, not data points.

The risk is not that AI vendors are malicious. The risk is that vendors operate inside legal, regulatory, and commercial environments that can change without notice. An acquisition, a regulatory mandate, a pricing restructuring, a change in data policy. Any of these can alter your practical ability to access or export what you have created. Waiting until a disruption occurs to think about extraction is, in most cases, waiting too long.

The goal of what follows is to make sure your intellectual work remains yours, on your terms, accessible inside whatever tools you choose to use going forward.

Phase One: Getting Your Work Out

The first phase of the playbook is about extraction, and it begins before you touch any export button.

Start by taking inventory. Before doing anything else, understand what exists. Log in to your vendor platform and make a written count of every session, project, document, and file you hold there. Vendor dashboards are not always reliable, and some platforms hide older sessions or archive them in ways that make them easy to overlook. Knowing your expected total before you begin is what lets you confirm that you actually got everything at the end.
Next, understand how the platform actually releases data. Some platforms offer a single account-level export button that delivers everything in one compressed file. Others require you to export session by session. Some offer no native export at all, which requires a different approach. Understanding the mechanics in advance saves significant time and prevents errors.
Once you understand the export mechanics, set up a receiving location on your own computer or a storage device you control. A cloud-synced folder, such as a shared Dropbox or Google Drive folder whose retention policies you have not verified, is not the right destination. A local folder with a clear, consistent naming structure is. Name each exported file with the date, a recognizable title, and an identifier, so that finding anything later is straightforward.
Then execute the export. Work through your session list methodically. If the platform allows a single account-level export, use it. If the platform requires session-by-session exports, work through the queue patiently, pausing briefly between each one. (Platforms sometimes limit how quickly you can download files, and moving too fast can trigger errors or interruptions.) When an export fails, log it and move on rather than letting one problem halt the entire effort.
After the export is complete, reconcile what you captured against what you expected. If any sessions are missing, investigate before moving on. In my own extraction, this reconciliation step surfaced six sessions that the dashboard had hidden but that were still recoverable.
Before you close the account, make sure you have also captured anything that lives outside the session logs themselves: account settings, billing history, any API connections or integrations you have been using. These are easy to overlook and often not included in standard session exports.
Finally, close the account through the vendor's own process and screenshot the confirmation. This closes the loop cleanly and gives you a record of when and how the account ended.

Phase Two: Turning an Archive into a Working Asset

The second phase is where most exit projects fail, and it is what separates a defensible archive from an intelligence asset that actually continues to serve you. A folder full of compressed files satisfies a data-sovereignty requirement, but it does not let you find, query, or build on anything. The goal of Phase Two is to convert your archive into something useful inside whatever AI platform you move to next.

The first step is converting your exported files into a format that AI tools can actually read and work with. Most vendor exports compress conversation logs into formats that require some unpacking before a language model can engage with them effectively. Converting those logs into clean, readable text files makes the archive accessible to any AI tool you use going forward.
The second step is building what I think of as a semantic index: a structured catalog that tells you not just what files exist, but what they are about. Think of it as the table of contents for your professional thinking. Working through your archive and assigning broad topic categories to each session, whether that is coaching methodology, client communication, business development, personal frameworks, or whatever categories fit your practice, gives you the ability to find relevant material later without rereading everything.
The third step is protecting the archive appropriately. If any of your sessions contain client-confidential material, regulated information, or content you have reason to consider sensitive, the archive should be encrypted at rest. This is the professional standard that applies to client records in most advisory and coaching contexts, and an AI session archive that contains client work is client record.
The fourth step is what I call standing up the Project Brain in your new environment. Rather than uploading everything to a new AI platform, identify the fifty or so sessions that carry your most important frameworks, your refined voice, your recurring methodologies, and your most reusable thinking. Upload those into a dedicated workspace in your new tool, whether that is a Claude Project, a NotebookLM notebook, or another environment, and write instructions that tell the new platform what the corpus represents and how to use it. The result is a working transplant of your professional intelligence from the old platform into the new one.

If You Need to Move Quickly

When a vendor disruption is announced on a Friday afternoon, the full procedure compresses into a 24-hour triage:

Run the export loop immediately.
Convert the exports to readable text as quickly as possible.
Stand up a basic working knowledge base in your new platform.
Encrypt the archive.
Close the account on your terms rather than waiting for the situation to resolve itself.

The full playbook is the considered version for planned transitions. The 24-hour triage is the emergency version for situations that do not announce themselves in advance. Both end in the same place: your work is out, it is protected, and you have a functioning starting point in whatever comes next.

A Note on Client Confidentiality

For executive coaches, organizational consultants, and advisory professionals, the archive question intersects directly with professional obligations. If you have used an AI platform to work through client engagements, you have likely deposited confidential material there. The extraction procedure above is the path to getting that material back under your control. The encryption and access controls in Phase Two are what let you retain it responsibly. And the question of whether to retain it at all, or how long, should be answered against the standards your engagement agreements and professional ethics require, not just against what is technically possible. This is an area where a brief conversation with your firm's legal counsel before acting is worth the time.

The Larger Point

What this experience clarified for me is that professional intellectual work done inside AI platforms deserves the same intentional stewardship we apply to any other professional asset.

The conversation history, frameworks, and refined methodologies that accumulate over months of serious AI engagement represent real value. They should not be left entirely in a vendor's custody, dependent on that vendor's continued operation and goodwill.

Taking ownership of that work is not complicated. It requires a modest investment of time and some basic organizational discipline. The two-phase procedure above makes the process manageable for anyone willing to work through it.

For the full technical detail, including code samples, forensic verification guidance, and patterns for teams managing this at larger scale, the complete Vendor Exit Playbook is available on LinkedIn, here.