Running a disclosure amnesty
A survey, two kinds of records and one promise: how to find out what's really in use.
An amnesty is the first promise put to work. You tell everyone what you're asking and why, you ask everyone at once, and you check what they tell you against two kinds of records. Done well, it gives you the first version of your AI list within a week, and it tells you something no survey can: whether people believe the promises.
People decide before the first question
Whether someone tells you the truth is settled before they see the survey. They decide from the announcement, from who makes it, and from what happened the last time someone admitted a shortcut. The reasons to stay quiet are real, and they're not only about rules.
of people who use AI at work are reluctant to admit using it for their most important tasks.
Microsoft and LinkedIn, 2024
worry that using it on important work makes them look replaceable.
Microsoft and LinkedIn, 2024
of privacy and security professionals admit entering non-public company information into AI tools.
Cisco, 2024
The last number is the one to remember when you write the announcement. Even the people who write the rules do it. An amnesty isn't about catching the careless few. It's about finding out what nearly everyone is doing.
Announce it under the promises
The announcement does four things.
- It says why. You can't improve work you can't see, and the people furthest ahead know things everyone else needs.
- It restates the promises, in the words your sponsor signed in module 1.
- It says where the answers go: to the disclosure custodian, outside the management chain, with leaders seeing totals only.
- It says what happens next: anything worth adopting is adopted, and its maker gets the credit.
Make it the sponsor's announcement. An amnesty announced by the people who would have enforced a ban reads like a trap, however well it's worded.
Ask everyone, in the room
The survey runs at the baseline session, the one-hour session for everyone that opens the loop. Its last ten minutes are for the survey, filled in there and then. That matters more than it sounds. A survey sent by email gets answered by the people with least to hide. A survey filled in by the whole room, straight after the aim and the promises, gets answered by nearly everyone.
Keep it short. Four core questions find what the AI list needs: which tools, what for, what data goes in, and whether anything someone made is now relied on by others. Add a question only if you'll act on the answer.
The first four are the core: they find tools, uses, data and shared items. Add what you'll act on, and nothing else.
You don't need to give your name. Your answers go to the disclosure custodian, who holds them outside the management chain, and leaders only ever see totals. Nothing you disclose can be used in performance reviews or in decisions about who leaves. If you think something has exposed confidential or regulated data, tell the disclosure custodian: it will be contained and handled through stop, tell, fix, learn, as coaching, not discipline.
- 01Which AI tools do you use for work? Include free ones, ones on personal accounts, and AI inside software we already use.
- 02What do you use them for?
- 03What kinds of information have gone into them?
- 04Have you made anything with AI that other people now rely on? A template, a prompt, an automation, a script.
- 05Have you noticed AI features switching on inside software you already use?
- 06Where does your work wait, pile up or get redone?
- Different AI tools named
- 6
- Put confidential data in
- under 5
- Put regulated data in
- under 5
- Made something others rely on
- under 5
- Noticed AI switched on in our software
- 7
- Described friction
- 19
- Names
- Never shown
On [date], at the baseline AI session, everyone will fill in a ten-minute survey about the AI they already use for work. We're asking because we can't improve work we can't see, and because the people furthest ahead know things the rest of us need to learn. The four promises apply, including this one: no one is punished for improving their own work. You don't need to give your name. Your answers go to the disclosure custodian, who holds them outside the management chain, and leaders only ever see totals. Nothing you disclose can be used in performance reviews or in decisions about who leaves. If you think something has exposed confidential or regulated data, tell the disclosure custodian: it will be contained and handled through stop, tell, fix, learn, as coaching, not discipline. If something you've made is worth adopting, we'll adopt it and you'll get the credit.
Three sources, because one isn't enough
The survey is one source. The pilot kit starts the AI list from three, and each catches what the others miss.
- The amnesty survey: what people tell you. It's the only source that finds free tools, which leave no record anywhere.
- SSO and expense records: what people sign in to with work accounts, and what's been paid for on cards and expense claims. A personal subscription claimed back every month is one of the most common finds.
- A check of software already in use: AI features vendors have switched on inside tools you already pay for, often in an update nobody read. Video calls, email, the CRM, the help desk and document tools are the usual places. This is AI that nobody chose at all.
Ask IT for the single sign-on log counted by app, and finance for card and expense lines from AI vendors. Then list every piece of software you pay for and check each one's admin settings and release notes for AI features. The vendor checklist from module 2 asks whether admins can control them; this is where you find out.
Who sees what
Individual answers go to the disclosure custodian you named on your aim page. Leaders see totals: how many tools, how many people use AI for drafting, how many have put confidential data into something. Never who.
Monitoring follows the same rule. It counts tools, not people: fourteen people sign in to this note-taker, not a list of who they are. The framework borrows this firewall from aviation, where a confidential reporting system has protected pilots who report their own mistakes for fifty years, and the reports keep coming because of it.
There is one exception. If a disclosure or the records show that regulated data has been exposed, someone has to follow up with the person: to find out what went where, and to contain it. That follow-up runs through stop, tell, fix, learn, under the firewall, as containment and coaching. The custodian handles it, not the person's manager, and it's still never used in reviews or decisions about who leaves.
The records check shows that patient referral letters have been uploaded to a personal transcription tool. That's regulated data. The head of the department asks the custodian for the name of whoever did it, so they can deal with it.
What do you do?
Disclosure against the records
With all three sources in, you can ask the question that tells you whether the promises are working: of the AI tools the records found, how many did people also disclose?
The pilot kit takes this measure on day 5 and again on day 90. If disclosure covers less than half of what the records found, the readout says, in its first line, that the promises didn't make disclosure safe. The aim is for the two to match, and wherever they don't, the gap shows you where trust is weakest.
Small organisations often have no monitoring at all. That's fine. Check the list against expense and single sign-on records, which every organisation has in some form.
Ask everyone, check what they say against the records, and keep the names away from the managers. Leaders see totals only.
The records show 14 people signed in to an unapproved note-taker last month. What should leaders see?
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