Cybersecurity in AI – OWASP Top 10 & Use Cases

🔒 Securing Intelligence in the Age of AI

Introduction
Artificial Intelligence (AI) is transforming industries, but with great power comes great risk. When AI models handle sensitive data, a small security gap can lead to massive breaches. Cybersecurity in AI isn’t just about protecting systems; it’s about protecting trust.

Key Topics

1. AI-Specific Threats
   • Data poisoning (injecting malicious data into training sets).
   • Adversarial attacks (tricking AI into wrong predictions).
   • Model theft & reverse engineering.
2. OWASP Top 10 for AI Systems
   • Broken access control in AI APIs.
   • Insecure model deployments.
   • Insufficient monitoring & logging for AI pipelines.
   • Insecure dependencies in ML frameworks.
   • Data leakage from training sets.
3. Real-World Use Cases
   • Banking AI fraud detection → poisoned datasets can blind the model.
   • Healthcare AI misdiagnosis → adversarial inputs trick medical image models.
   • Chatbots → prompt injection & data leakage risks.

Conclusion
AI is powerful, but unprotected AI is dangerous. Following OWASP-inspired guidelines ensures AI models remain assets, not liabilities.