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According to the PwC Global Workforce Hopes and Fears Survey for 2026, nearly two-thirds of employees use AI in their roles.1 But despite the uptake, not all organizations offer an AI security training program for employees.
This is worrisome, as AI introduces new risks that fall well outside the range of standard security awareness training. This guide explores what an AI training curriculum should include and how to tailor it for different users.
What Should an AI Security Training Program for Employees Include?
Comprehensive AI security awareness training starts with a foundational course for all employees. It introduces them to potential AI risks, explains the organization's approach to safe AI use, and helps workers develop practical AI habits. Training may cover:
- The organization's AI governance and privacy policy. A workplace policy explains which AI tools employees are free to use in their jobs. It also defines what workers can and can't share with AI.
- The potential risks of AI. Training should discuss how sharing inappropriate data with AI can result in privacy or regulatory violations. Other topics to address include common types of AI-generated attacks, AI bias, and discrimination.
- The importance of validating outputs. AI responses aren't always accurate. Teams must know how to check outputs against valid sources before relying on them for work tasks.
A foundational course sets the stage for deeper, role-based AI training for employees that aligns with their current workflows.
How Do You Assess Your Organization's AI Training Needs Before You Build?
Before rolling out a complete training program, you'll want to get a feel for how employees currently use AI on the job. You can find out through an employee survey. You'll want to ask which tools workers use, what tasks they use them for, and whether they've engaged in formal or informal AI training.
Besides the survey, you can run an independent audit on AI use with help from your IT team. They can review system logs for AI app installations and traffic. Discovery tools can also uncover shadow AI use across your organization.
Knowing how your team uses AI will give you insights into what your training should include.
How Do You Design a Role-Based AI Security Curriculum?
Since employees vary in how they use AI and the threats they may encounter, you'll want to separate your training by role. That way, the risk scenarios used in your sessions are tailored to the work they perform. Here's how to build AI training that yields results:
1. Organize Employees by Role Type, AI Use, and Department
Your employee survey should give you insight into what AI tools teams are using and why. Use that data to map out which models your teams use, their business cases, and the associated security risks.
For example, members of your finance team may use AI to create dashboard visuals and charts. If they share organizational financial information with the AI, there's a real risk of data loss. Covering that risk and explaining safe alternatives would feel relatable to employees in these roles.
2. Decide What to Include in Each Training Track
During this phase, you'll outline the security risks covered in your training and ways to minimize them. Tie these to actual compliance frameworks that apply to your team's roles, such as HIPAA or NIST CSF.
Have a subject-matter expert review your security training content before finalizing it. Their input can confirm you're working with realistic scenarios, instead of ones that don't tie into the team's tasks.
3. Enhance Training with Engagement Techniques
Traditional security awareness training follows a lecture format. However, listening to someone discuss best security practices may result in poor information retention among your team. A better solution is to include hands-on simulations, gamification, and realistic scenarios that hold each participant's attention.
Should Non-Technical Employees Get the Same Training as Developers?
Non-technical employees and developers face different business risks, so they should receive separate training.
For non-technical roles, training should focus on validating AI outputs, recognizing common AI scams, and data privacy. Employees in these roles aren't actively building software, so risks are mainly associated with how they use AI models.
Developer training should cover secure code reviews, OWASP Top 10 for LLM Applications, and risks associated with an AI software development life cycle. Their work revolves around using AI to build, which introduces its own set of risks. As Michael Burch, VP at Security Journey, says, "You are not capable unless you also understand the security implications of what you are building."
How Do You Measure Whether AI Security Training Is Actually Working?
Implementing an AI security awareness training course is the first step to safeguarding your business from AI harms. But you won't know whether it resonates with your team unless you measure AI training effectiveness. To do so, track their AI interactions before and after the training. Look for:
- AI phishing successes and failures: Running a targeted simulation before and after training can tell you how vulnerable employees are to AI-generated phishing scams. You should see a drop in successful attempts after the course.
- Shadow AI use: The number of employees using unapproved AI tools should decline after training.
- Time to report AI threats: You should see a quicker turnaround among employees who recognize suspicious AI-generated materials. A targeted simulation can track their response rates before and after training.
- Fewer AI-generated code vulnerabilities: Developers who use AI to code should apply stronger review practices, resulting in fewer vulnerabilities reaching testing and production.
Security Journey offers role-based AI security awareness training for developers, business leaders, and employees. Our tiered learning paths scale across your entire organization, delivering hands-on training that teams can relate to. Schedule a demo with us to learn more.
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