PART 6 · MODULE 1The threat landscape
Introduction to AI Security
Overview of AI security threats
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Overview of AI security threats
AI systems are powerful, but they are also surprisingly fragile. Unlike traditional software, AI models can be fooled, stolen, or corrupted in ways that are invisible to the naked eye.
A self-driving car that misreads a stop sign. A fraud detector that lets criminals through. A chatbot that leaks private data. These are not hypothetical scenarios — they are real vulnerabilities in AI systems deployed today.
Traditional cybersecurity protects servers, networks, and data. AI security is different because the model itself is the attack surface. You cannot patch a neural network the way you patch a server. The threats are fundamentally new.
AI Security Threat Taxonomy
Trick the model at inference time. Add tiny, invisible changes to an input so the AI misclassifies it. The model works fine on normal data but fails on crafted inputs.
Corrupt the model during training. Slip bad data into the training set so the model learns the wrong thing. Like putting wrong answers in a textbook before students read it.
Steal the model by querying it. Send thousands of carefully chosen inputs and observe the outputs to reverse-engineer a private, proprietary model.
Leak private training data. Probe the model to reveal sensitive information it memorized during training, such as personal data, passwords, or proprietary content.
If you are building, deploying, or relying on AI systems, security is your concern. This includes ML engineers, product teams, executives, and anyone whose business depends on AI making correct decisions.
The defender's mindset is simple: think like an attacker to build better defenses. Understanding how attacks work is the first step to preventing them.
AI systems have unique vulnerabilities that traditional cybersecurity does not address. A firewall cannot stop an adversarial image, and antivirus software cannot detect a poisoned training dataset. Defending AI requires a new playbook.
For hands-on practice, check out the AI Security course on GitHub.