From the course: AI Security Tools and Automation
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Security considerations
From the course: AI Security Tools and Automation
Security considerations
Our demo works but it's not production ready yet. In production, you need to think about access control, input validation, rate limiting, secrets management, and audit logging. These aren't optional for compliance tools. If your AI system gets compromised or leaks data, you're liable. I'm going to show you five security controls we need to add before deploying this tool to real clients. Alright, on to sandboxing and access control. The problem here is anyone can access your MCP server. And here's the fix. So we're returning all our gap analysis. However, there's no authentication, no authorization. The risk here is your competitors could see your clients compliance gaps. And the solution we need to add authentication. With authentication, we need to require a valid API key. We add an API key decorator. Before the endpoint runs, it It checks the header. If the key doesn't match the environment variable, it returns a 401, unauthorized. Now only clients with the correct API key can…
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