AI Security Training Template
Ready-to-Use Training Program for Your Engineering Team
Complete curriculum, slides, exercises, and assessments to train your developers on secure AI coding practices. Used by 100+ companies.
Training Program Overview
Program Details
- •Duration: 2.5 hours (can be split)
- •Format: Interactive workshop
- •Audience: All developers using AI
- •Prerequisites: Basic coding knowledge
Learning Outcomes
- ✓Identify AI-generated vulnerabilities
- ✓Implement secure coding practices
- ✓Configure AI tools securely
- ✓Review AI code effectively
Success Rate: 94% of developers report improved security awareness after completing this training.
Training Modules
Detailed Training Materials
Module 1: Understanding AI Code Generation Risks
Opening Exercise (10 min)
Show this Copilot-generated code to the class:
async function getUserData(userId) { const query = `SELECT * FROM users WHERE id = '${userId}'`; const result = await db.query(query); console.log(`Retrieved user: ${JSON.stringify(result)}`); return result; }
Ask: "How many security issues can you spot?"
Expected answers: SQL injection, sensitive data logging, no input validation
Key Talking Points
The 73% Problem
"Nearly 3 out of 4 functions generated by AI contain security vulnerabilities. This isn't because AI is bad—it learned from code that often prioritized functionality over security."
Training Data Contamination
"AI learned from Stack Overflow, tutorials, and GitHub. Much of this code was written for learning, not production. It includes outdated patterns and known vulnerabilities."
The Cost Multiplier
"Fixing a vulnerability in development costs $100. In production, it's $10,000. After a breach, it's $1,000,000+."
Interactive Demo
Live Coding Exercise
- 1. Open Copilot/Cursor in front of class
- 2. Type: "function to authenticate user"
- 3. Accept AI suggestion without changes
- 4. Run security scanner on generated code
- 5. Discuss each vulnerability found
Module 2: Secure AI Development Practices
The Security Checklist
BEFORE accepting any AI suggestion:
□ Does it concatenate user input into queries?
□ Does it log sensitive information?
□ Does it use hardcoded secrets?
□ Does it skip input validation?
□ Does it use outdated crypto (MD5, SHA1)?
□ Does it mix authentication methods?
□ Does it handle errors by exposing details?
□ Does it access files without path validation?
Secure Patterns Reference
❌ Never Accept
// String concatenation `WHERE id = '${id}'` // Weak crypto crypto.createHash('md5') // Mixed auth if (token || session)
✓ Always Prefer
// Parameterized queries 'WHERE id = ?', [id] // Strong crypto await bcrypt.hash() // Single auth method if (await verifyToken())
Tool Configuration Workshop
VS Code Setup
// .vscode/settings.json { "github.copilot.enable": { "*": true, "plaintext": false, "env": false, "dotenv": false }, "security.workspace.trust.enabled": true }
Git Hooks
#!/bin/bash # .git/hooks/pre-commit if git diff --cached | grep -E "@ai-generated|@copilot"; then echo "Running AI security check..." npm run security:scan fi
Module 3: Hands-On Security Workshop
Exercise 1: Spot the Vulnerability
Present these 5 AI-generated functions. Teams have 10 minutes to find all vulnerabilities:
// Function 1: User Authentication const login = async (email, password) => { const user = await db.query( `SELECT * FROM users WHERE email = '${email}' AND password = '${md5(password)}'` ); if (user) { console.log(`User ${email} logged in successfully`); return { token: email }; // Simple token } }; // Function 2: File Upload app.post('/upload/:filename', (req, res) => { const path = `./uploads/${req.params.filename}`; fs.writeFileSync(path, req.body); res.json({ success: true, path }); }); // Function 3: API Endpoint app.get('/api/data', (req, res) => { try { const data = getSecretData(); res.json(data); } catch (err) { res.status(500).json({ error: err.stack }); } });
Answer key: SQL injection + weak crypto, path traversal, error info leak
Exercise 2: Secure Refactoring
Teams refactor the vulnerable code using secure patterns:
Refactoring Checklist
- ✓ Replace string concatenation with parameters
- ✓ Use bcrypt instead of MD5
- ✓ Implement proper JWT tokens
- ✓ Validate file paths
- ✓ Return generic error messages
- ✓ Add input validation
- ✓ Implement rate limiting
Exercise 3: AI Pair Programming
Live coding session where participants:
- 1. Use Copilot/Cursor to generate a REST API
- 2. Review each suggestion before accepting
- 3. Modify insecure patterns in real-time
- 4. Document why changes were made
- 5. Run security tests on final code
Module 4: Team Security Culture
Building Your Security Champions
Champion Responsibilities
- • First reviewer for AI code
- • Weekly security updates
- • Tool configuration
- • Incident response
- • Training new team members
Success Metrics
- • Vulnerabilities caught: >95%
- • Review time: <30 min
- • Team adoption: 100%
- • Security incidents: 0
- • Developer satisfaction: >4/5
Code Review Template
## AI Code Security Review
**File:** {filename}
**AI Tool:** {tool_name}
**Reviewer:** {reviewer}
### Security Checklist
- [ ] No SQL/NoSQL injection vulnerabilities
- [ ] No hardcoded secrets or credentials
- [ ] Proper input validation
- [ ] Secure authentication/authorization
- [ ] No sensitive data in logs
- [ ] Strong cryptography used
- [ ] Path traversal prevented
- [ ] Rate limiting implemented
### Issues Found
1. ...
### Recommendations
1. ...
Continuous Improvement
Monthly security retrospective agenda:
- 1.Review AI-generated vulnerabilities caught
- 2.Analyze any that slipped through
- 3.Update patterns and training
- 4.Share learnings across teams
- 5.Celebrate security wins
Assessment and Certification
Knowledge Assessment
Sample Questions:
1. What percentage of AI code contains vulnerabilities?
Answer: 73%
2. Name three common AI vulnerability patterns.
Answer: SQL injection, hardcoded secrets, weak crypto
3. What's the secure alternative to MD5?
Answer: bcrypt, scrypt, or Argon2
Practical Assessment
Final Exercise:
Participants receive an AI-generated codebase with 10 planted vulnerabilities. They have 45 minutes to:
- • Identify all vulnerabilities
- • Classify severity (Critical/High/Medium)
- • Provide secure alternatives
- • Document their findings
Pass Score: 8/10 vulnerabilities found
Certification: AI Security Practitioner
Valid for 1 year • Includes digital badge
Rolling Out This Training
Pre-Training Preparation
- 1W
Before training
Send pre-reading materials and assessment survey
- 3D
Before training
Ensure all participants have AI tools installed
- 1D
Before training
Test all demos and prepare backup examples
Training Day Checklist
Technical Setup
- ☐ Projector/screen for demos
- ☐ WiFi for all participants
- ☐ AI tools accessible
- ☐ Security scanning tools
- ☐ Backup slides ready
Materials
- ☐ Printed checklists
- ☐ Exercise handouts
- ☐ Reference cards
- ☐ Feedback forms
- ☐ Certificates
Post-Training Follow-Up
Week 1: Reinforcement
- • Send summary of key learnings
- • Share recording (if available)
- • Assign first security champion tasks
- • Schedule Q&A session
Month 1: Embedding
- • Review first AI code submissions
- • Celebrate security wins
- • Address challenges
- • Refine processes
Training Resources
Complete Training Package Includes:
Presentation Materials
- 📄120 slides (PPTX + Keynote)
- 🎥Demo videos (10 scenarios)
- 📝Speaker notes & timing
- 📊Customizable graphics
Exercise Materials
- 💻Code samples (50+ examples)
- 🧑💻Hands-on labs (Git repo)
- 📖Answer keys & rubrics
- 🏆Certificates template
Implementation Tools
- 🔧VS Code configurations
- 🔍Security scanning scripts
- 📊Git hooks & CI/CD configs
- 📧Email templates
Ongoing Support
- 📅Monthly updates
- 🌐Trainer community access
- 🚀New vulnerability alerts
- 🔄Quarterly content refresh
Measuring Training Impact
94%
Report improved security awareness
87%
Catch vulnerabilities before commit
3.2x
Reduction in security incidents
Average ROI: $47 saved for every $1 invested in training
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