📚 AI Development Guide

Complete learning resource: 45 topics from beginner to expert

Risk Management - What to Watch Out For

P0 - MVP⭐⭐ Intermediate⏱️ 18 min read

Comprehensive risk assessment for AI development adoption. Explore 10 major risk categories, from security vulnerabilities to hallucinations to vendor lock-in. Each risk includes severity rating, likelihood, real-world examples, and concrete mitigation strategies.

(Spoiler: "What could possibly go wrong?" - Famous last words before AI hallucinated an entire API that doesn't exist. ~€37.5K debugging bill later... 💸)

⚠️

Understanding AI Development Risks

AI tools introduce new risks alongside massive benefits. The key is not avoiding risk entirely (impossible and counterproductive) but managing risk intelligently.

This topic provides a realistic, balanced view: Neither fear-mongering nor dismissing legitimate concerns.

🎯 What You'll Learn

✓Identify the 10 major risk categories in AI-augmented development
✓Assess risk severity and likelihood for your context
✓Learn concrete mitigation strategies for each risk
✓Study real-world incidents and lessons learned
✓Create a risk management plan for your team

🎯 The 10 Major Risk Categories

Risk Matrix Overview

Risk	Severity	Likelihood	Priority
1. Security Vulnerabilities	🔴 High	🟡 Medium	Critical
2. Hallucinations	🟡 Medium	🔴 High	High
3. Over-reliance	🟡 Medium	🟡 Medium	Medium
4. Privacy & Data Leakage	🔴 High	🟢 Low	High
5. IP & Legal Concerns	🟡 Medium	🟢 Low	Medium
6. Vendor Lock-in	🟢 Low	🟡 Medium	Low
7. Skill Atrophy	🟡 Medium	🟢 Low	Medium
8. Quality Degradation	🟡 Medium	🟢 Low	Low
9. Cost Overruns	🟢 Low	🟡 Medium	Low
10. Bias & Fairness	🟡 Medium	🟢 Low	Low

🔴 Risk 1: Security Vulnerabilities

🛡️

The Risk

AI-generated code can contain security vulnerabilities: SQL injection, XSS, insecure authentication, hardcoded secrets, unsafe deserialization, etc.

📊 Severity:

🔴 High

Security breach can be catastrophic: data loss, reputational damage, legal liability.

📈 Likelihood:

🟡 Medium

AI maakt fouten, vooral in security-kritieke code. Without proper review: likely.

Real-World Example

Incident: Developer gebruikt AI om authentication endpoint te genereren. AI code bevat SQL injection vulnerability (geen parameterized queries). Code gaat door review omdat "AI generated = trustworthy". Vulnerability ontdekt na 3 maanden in production.

Impact: Security audit, emergency patch, customer notifications. Cost: ~$50K.

🛡️ Mitigation Strategies

✓
Always Review Security-Critical Code
Authentication, authorization, data validation, SQL queries, API calls - treat als high-risk.
✓
Use SAST Tools
Static Application Security Testing: Snyk, SonarQube, Checkmarx - automated vulnerability detection.
✓
Security-Focused Prompts
"Generate secure code with parameterized queries" - explicitly request security best practices.
✓
Penetration Testing
Regular pen tests, especially after major AI-assisted development.
✓
Security Training
Developers need to recognize vulnerabilities in AI output - training is essential.

🟡 Risk 2: Hallucinations (Non-Existent APIs/Methods)

🤔

The Risk

AI "hallucinates" non-existent functions, libraries, or methods. Code looks plausible but doesn't compile, or worse: compiles but does something different than expected.

📊 Severity:

🟡 Medium

Wastes time, causes frustration. Rarely catastrophic maar wel productiviteitskiller.

📈 Likelihood:

🔴 High

Happens frequently, especially with newer libraries or specific versions.

Common Hallucination Patterns

🤖 Non-existent methods: "Use array.sortBy()" (doesn't exist in JavaScript)
🤖 Wrong library versions: Suggests API from older/newer version you're not using
🤖 Confused frameworks: Mixes React patterns with Vue patterns
🤖 Imaginary parameters: Function exists, but parameters don't
🤖 Plausible names: "Use getUserDetails()" (sounds right, but doesn't exist)

Real-World Example

// AI-generated code const sorted = array.sortBy('name') // Looks plausible! // But sortBy() doesn't exist in JavaScript // Correct: const sorted = array.sort((a, b) => a.name.localeCompare(b.name))

Developer wastes 15 minutes debugging why code doesn't work before realizing method doesn't exist.

🛡️ Mitigation Strategies

✓
Always Verify APIs
If AI suggests a method/function you don't know: Check documentation first!
✓
IDE Autocomplete Check
Type suggested method - IDE should show autocomplete. No autocomplete = likely hallucination.
✓
Run Immediately
Test AI output direct. Don't assume it compiles/runs.
✓
Provide Context
"Using React 18.2.0" - specific versions help reduce hallucinations.

🟡 Risk 3: Over-Reliance & Skill Atrophy

🧠

The Risk

Developers become so dependent on AI that fundamentals deteriorate. Critical thinking skills atrophy. "I don't know why this works, but AI says it's good."

Warning Signs

⚠️ Developer can't explain how their own code works
⚠️ Unable to debug without AI assistance
⚠️ Copy-paste AI output without reading/understanding
⚠️ Panic when AI tools are unavailable (outage, offline)
⚠️ Declining performance in technical interviews

🛡️ Mitigation Strategies

✓
Weekly "AI-Free" Hours
1-2 hours per week: Code zonder AI. Maintain fundamentals.
✓
Understand Before Accepting
Rule: If you cannot explain the code to a junior developer, do not use it.
✓
Code Review as Learning
Review AI code in detail - learn patterns, spot issues.
✓
Continuous Learning
Stay current with fundamentals: algorithms, data structures, patterns.

🔴 Risk 4: Privacy & Data Leakage

🔒

The Risk

Proprietary code, secrets, PII, or business logic accidentally sent to AI providers. Data may be used for training, stored indefinitely, or exposed in AI responses to other users.

Real Incident: Samsung Data Leak

2023: Samsung engineers pasted proprietary semiconductor code into ChatGPT for debugging. Code included confidential manufacturing processes.

Impact: Samsung banned ChatGPT company-wide. Potential IP compromise.

Source: Bloomberg, Reuters (April 2023)

What Can Leak?

🔓 API keys, passwords, database credentials
🔓 Proprietary algorithms and business logic
🔓 Customer data (PII, health records, financial info)
🔓 Internal code architecture and vulnerabilities
🔓 Trade secrets and competitive advantages

🛡️ Mitigation Strategies

✓
Use Enterprise Plans
GitHub Copilot Business, ChatGPT Enterprise - data not used for training.
✓
Data Sanitization
Remove sensitive data before sharing with AI: Replace real values with placeholders.
✓
Self-Hosted Solutions
For highly sensitive: Code Llama, local models - data never leaves your infrastructure.
✓
Developer Training
Clear policies: What can/cannot be shared with AI tools.
✓
DLP (Data Loss Prevention)
Tools that detect and block sensitive data in outbound requests.

🟡 Risk 5: Intellectual Property & Legal Concerns

⚖️

The Risk

AI trained on open-source code. Kan het copyrighted code reproduceren? Wie owns AI-generated code? Legal landscape is evolving.

The Debate

✅ AI Provider Position

• Fair use for training
• Transformation creates new work
• Indemnification clauses (GitHub, Microsoft)
• Low probability of exact reproduction

❌ Critics' Concerns

• Copyright violation in training
• Can reproduce copyleft code (GPL)
• Unclear legal precedent
• Ongoing lawsuits

🛡️ Mitigation Strategies

✓
Use Providers with Indemnification
GitHub Copilot, Microsoft offer legal protection. Read terms carefully.
✓
Originality Verification
Check if AI output matches existing code (Google search, GitHub search).
✓
Document AI Usage
Track which code is AI-generated for audit trail.
✓
Legal Review (High-Risk)
For critical products: Legal counsel review of AI usage policies.