AI learning path
Turn responsible-AI principles into risk registers, threat models, evaluation gates, incident plans, and accountable operating practices.
For Builders, security teams, product owners, and governance leaders
7 resources
Google for Developers
A structured introduction to fairness, accountability, safety, and privacy considerations when developing and scaling AI systems.
NIST
A voluntary framework organized around Govern, Map, Measure, and Manage for operationalizing trustworthy and responsible AI risk management.
NIST
A companion profile that applies the AI RMF to generative-AI risks, actions, measurement needs, and governance considerations.
OWASP GenAI Security Project
A practitioner reference for common LLM application risks such as prompt injection, sensitive-data disclosure, excessive agency, and insecure output handling.
Microsoft
A public overview of Microsoft's responsible-AI principles, governance approach, and resources for putting accountability into product development.
A security framework for mapping AI risks, extending established controls, automating defenses, and adapting protection to model, data, infrastructure, and application layers.
MITRE
A structured knowledge base of adversary tactics, techniques, case studies, and mitigations for machine-learning and generative-AI systems.
