Skip to content

Privacy & Security in AI

Protecting data and defending models — training on sensitive data safely, and keeping AI systems robust against attack.

Privacy & Security in AI is one of the core areas in the AI University map of AI. Explore the diagram, then dive into each topic — every subtopic grows into its own deep-dive over time.

flowchart TB
  DATA[(Sensitive data)] --> FL[Federated / DP training] --> MODEL[[Model]]
  ATT[/Attacker/] -. adversarial input .-> MODEL
  ATT -. prompt injection .-> MODEL
  MODEL --> DEF{Defenses} --> SAFE[/Robust system/]

Key topics

  • Federated learning

    Train across many devices without centralizing raw data.

  • Differential privacy

    Provable guarantees that a model doesn't leak any individual's data.

  • Adversarial examples

    Tiny crafted perturbations that fool models — and defenses against them.

  • Data poisoning & backdoors

    Corrupting training data to plant hidden behaviors.

  • Model extraction & inversion

    Stealing a model or reconstructing its training data through its API.

  • Prompt injection & LLM security

    The new attack surface of agents and RAG — untrusted input hijacking instructions.

AI Safety, Alignment & Ethics · AI Agents & Autonomy · AI Ethics & Governance

Learn this properly

Want hands-on training in privacy & security in ai? Explore AI University courses and AI School camps for kids.