Security, Compliance and Governance for AI Solutions

An AI workload on AWS is protected with the same building blocks as any other workload: AWS Identity and Access Management (IAM) decides who can do what, AWS Key Management Service (AWS KMS) controls encryption keys, AWS PrivateLink keeps traffic off the internet, and Amazon Macie finds sensitive…

An AI agent doesn't just answer questions: it calls tools, reads systems and takes actions, often for a specific user. That creates two security questions a chatbot never had to answer. How does the agent prove who it is, and get credentials to other services safely? And what stops it from taking…

When an AI system makes a decision or writes an answer, people eventually ask where it came from. Which data trained this model? Who approved it, and for what use? Which document backs up this answer? Being able to reply is called provenance, and it matters for audits, for fixing problems and for…

An AI system is only as trustworthy as the data it learns from and reads at run time. Secure data engineering means four things: checking that the data is good, hiding personal details people don't need, letting each person see only the data their job requires, and proving the data hasn't been…

A generative AI application faces the usual security risks of any application, plus new ones: prompts that try to hijack the model, answers that leak private data, toxic content, and conversations that need an audit trail. No single control covers all of this. This lesson goes beyond recognising…

A hallucination is a confident answer that isn't true or isn't supported by any source. In a live application, hallucinations are a security and trust problem as well as a quality one: a wrong policy answer can mislead a customer, and an invented fact stored by one agent can mislead every agent…

Governing an AI system means being able to answer an auditor's questions with evidence: who did what, how resources were set up, whether anything is vulnerable, and whether AWS itself meets the standards you rely on. AWS has a different service for each of those questions. The exam tests whether…

Data governance is the set of decisions about how data moves through an AI system and how long it stays: where it's stored and processed, what gets logged, who watches it, and when it's deleted. For a generative AI application, that data includes training sets, documents used for retrieval, and…

Tools only work when people use them consistently, so AI governance also needs processes: written policies, people responsible for decisions, regular reviews, clear documentation and training. AWS's guidance adds a framework for deciding how much security work a generative AI use case needs, the…