How Should You Train Employees on Gen-AI at Work? Five Behaviors to Encourage Before You Block Anything
The instinct, when you see the numbers, is to reach for the block button. Don't. The organizations succeeding with Gen-AI aren't the ones blocking it - they're the ones training five specific behaviors that make a permissive AI policy survivable. Here's what those behaviors are, why blocking fails, and how to actually train them at the speed Gen-AI is moving.
You can't block your way out of the Gen-AI problem. You can teach five behaviors that make it survivable. But only if your training can move as fast as the thing it's training people on. Frame is how you keep up.
Why blocking Gen-AI doesn't work
The numbers make the case for shutting it all down. 77% of online LLM access is to ChatGPT. Roughly 18% of enterprise employees paste data into Gen-AI tools, and more than half of those paste events include corporate information. IBM's 2025 Cost of a Data Breach Report puts breaches involving shadow AI at $4.63 million on average - about $670,000 above standard incidents. 57% of employees actively hide their AI usage at work, and 90% of security professionals themselves use unapproved AI tools. Block everything, the thinking goes, and the problem goes away. It doesn't.
The Samsung episode in 2023 - three separate data leaks in 20 days after engineers pasted source code, yield-optimization logic, and meeting transcripts into ChatGPT - is the canonical example. Samsung banned the tool. The leaks didn’t stop. Employees moved to personal devices, 5G hotspots, and AI tools the company hadn't even heard of, and the data kept flowing out. 80% of workers use unapproved AI tools for work tasks. If your policy relies on blocking, you're not reducing risk, you're reducing visibility into risk.
The alternative is training people to handle Gen-AI safely - and that starts with five specific behaviors. Frame is the platform built to teach them.
Behavior 1: How do employees know what counts as "sensitive"?
The most common mistake isn't an employee knowingly pasting a password into ChatGPT. It's an employee pasting something they don't think is sensitive; a meeting transcript, a customer list with names and emails, a draft of next quarter's roadmap, an internal Slack thread they're trying to summarize, and being genuinely surprised later to learn that it counted. Training has to move past the word "sensitive" (which every employee thinks they understand and none agree on) and give people concrete categories: unreleased financial numbers, legal drafts, customer PII, source code, internal strategy documents, anything under NDA, anything subject to HIPAA or GDPR.
But generic categories don't stick. A developer's "sensitive" looks nothing like a recruiter's "sensitive." A finance lead is guarding earnings drafts; a product manager is guarding roadmap leaks. The behavior takes root only when training shows examples drawn from the employee's actual job - the kind of role-specific, organization-specific content that legacy awareness libraries can't produce on demand. If people don't see their version of sensitive data being mishandled in training, they won't recognize it in real life.
Behavior 2: Are your employees using the sanctioned tool or a personal account?
There's an enormous gap between "our employees use ChatGPT" and "our employees use our enterprise ChatGPT account." 73.8% of ChatGPT accounts used in the workplace are non-corporate ones that lack the security and privacy controls of the enterprise version. The rate is higher for Gemini (94.4%). This is a training problem, not a tooling problem.
Most organizations have an enterprise license. Most employees don't know it exists, don't know how to access it, or don't understand why it matters. ChatGPT is ChatGPT, right? The behavior to train is specific and boring: when you open an AI tool for work, check which account you're signed into. That one habit removes most of the shadow AI problem without a single block rule.
Here's the catch: the tool stack changes. The company you adopted last quarter, the new internal AI assistant you rolled out this month, the Copilot deployment that started last week. Training that names the actual tools your organization sanctioned, by name, with screenshots, this quarter, is the only kind of training employees connect to their daily reality. Generic "use the corporate version" modules pulled from a vendor library don't move the needle.
Behavior 3: How do employees learn to verify AI outputs?
In 2025, Deloitte had to refund the Australian government after AI fabrications slipped into a $440K welfare report. This is the new class of AI incident: not a data leak, but a credibility leak. An employee asks a model to summarize a regulation, draft a client email, or cite a source. The model produces something confident and plausible. The employee ships it without checking. The organization owns the result.
The rule employees need to internalize is that AI outputs, especially citations, statistics, and specific claims, are a draft until a human has verified the underlying source. The moment an AI output leaves the employee's inbox to a client, a regulator, a journalist, or a board, the organization is accountable for every word of it.
But here's the harder problem: employees don't feel the danger of an AI output until they've been fooled by one. Reading "verify before you send" in a policy doc is theoretical. Watching a fabricated quote from a fake-but-realistic-looking industry expert end up in your draft email - that's more meaningful. Training on this behavior works when employees see convincing fakes in a safe environment, ideally featuring scenarios and characters drawn from their own world. Once you've been fooled in training, you stop trusting outputs in production. That's the muscle to build.
Behavior 4: Can your employees recognize when AI is being used against them?
This is the behavior most security awareness programs are currently skipping, and it's becoming the most important one. AI isn't just a tool employees use at work. It's a tool attackers use to target them. With a sharp rise in deepfake audio, AI-generated spear phishing, and business email compromise that reads in perfect corporate tone, the "is this from our CFO?" question is genuinely harder to answer than it was 18 months ago, and it will be harder still in another 18.
Generic "be careful of deepfakes" modules recorded in 2023, before any of this existed, don't prepare anyone for what's actually landing in inboxes this week. Employees need to see and hear AI-enabled social engineering the way attackers are deploying it right now: a voice that sounds like their CFO, a video that looks like their CEO, a spear phishing email that references a real internal project, a Teams call with a face that almost-but-not-quite belongs to someone they know.
The only way this behavior takes root is by running employees through realistic, current simulations: voice phishing, deepfake video, AI-generated spear phishing, built around the threat patterns landing in their industry this quarter, not last year. Static training libraries can't keep up. The threats are moving too fast.
Behavior 5: Will an employee tell you when they made a mistake?
The single most costly behavior in the Samsung episode wasn't the paste. It was the silence afterward. The company only discovered the code leak because internal monitoring caught it, not because an employee raised their hand. In the meantime, more data went the same way.
The behavior to train is simple: if you pasted something you shouldn't have, clicked something you shouldn't have, or realized after the fact that an AI tool stored something sensitive - tell someone immediately, without fear of punishment. The organization's ability to contain the damage (revoke tokens, request data deletion, notify regulators if needed) depends entirely on the speed of the report.
This behavior doesn't train itself into existence. It requires a named, non-punitive reporting channel, visible leadership reinforcement, and a way for security teams to act on reports the moment they come in. One alert employee reporting a suspicious AI-generated email or a misused tool should be enough to protect the rest of the organization - but only if there's a system that turns that one report into instant, organization-wide action. If your reporting path ends in a shared mailbox checked weekly, the behavior won't take root, and the damage won't be contained.
Why most security awareness training can't keep up
Every one of the five behaviors above involves AI tools, attack patterns, and incident types that didn't meaningfully exist two years ago, and the specifics shift every quarter. A new model launches. A new deepfake technique emerges. A new enterprise AI tool gets rolled out internally. Most awareness programs aren't built for that pace. Content libraries get refreshed annually. A module on "Gen-AI risks" filmed in 2024 will talk about ChatGPT and skip every tool that matters in 2026.
That's the gap Frame was built to close.
How Frame trains the five Gen-AI behaviors
Frame is an AI-driven human risk management platform that replaces one-size-fits-all training with personalized, role-based, threat-aligned content and simulations - generated in minutes, in 30+ languages, mapped to the actual threats and tools in your environment.
Three engines do the work, and each one maps directly onto the behaviors above.
Frame's Content Studio generates role-based training in minutes, not months. A developer gets a module on what "sensitive" looks like in a code review. A finance lead gets one on customer PII and earnings drafts. An IT admin gets one on the specific AI tools your organization just sanctioned, by name, with screenshots from this quarter. When you adopt a new internal AI tool on Monday, you can have role-specific training on it by Wednesday. Behaviors 1 and 2 - knowing what's sensitive and using sanctioned tools, depend entirely on training that reflects the employee's actual job and the company's actual stack. Frame is built to deliver exactly that.
Frame's Simulation runs realistic, data-driven social engineering simulations built around modern attacker behavior, including deepfake voice phishing, deepfake video, AI-generated spear phishing, and multi-channel impersonation. Frame can also generate custom deepfake characters from images or short videos, transformed via prompt, meaning you can run a simulation featuring a fabricated "industry expert," a fake executive, or a synthesized vendor representative, tailored to your organization. That capability is what makes behavior 3 trainable: employees learn to verify AI outputs by being shown what convincing fakes actually look like, in scenarios drawn from their own world. And it's what makes behavior 4 trainable at the pace attackers are operating: simulations updated this quarter, not pulled from a library filmed in 2023.
Frame Triage turns one employee's instinct into organization-wide protection. Triage monitors employee activity in real time - emails reported, emails opened, suspicious messages flagged, and surfaces it in a single dashboard where security admins can act in seconds. Mark a message as spam, quarantine it, or dismiss it, and that decision propagates instantly across every employee's inbox in the organization. That's behavior 5 made operational: one report, organization-wide containment, no shared mailbox, no week-long delay.
Together, the three engines give security teams something legacy training never could: measurable human risk readiness against the Gen-AI threats actually emerging this quarter, across the tools your employees use, the roles they hold, and the attacks they're being targeted with.
To train the five behaviors that make a permissive Gen-AI policy survivable, organizations need role-based content, current threat simulations, and real-time reporting infrastructure. Frame is the platform built to deliver all three.
Frame is how you keep up with the Gen-AI threats moving faster than your training. Book a demo and see how AI-driven simulations and role-based training prepare your organization at scale, in minutes.
