Hi team, I’ve been exploring Supertokens recently and admire the work you’re doing to simplify auth while keeping it secure and developer-friendly. I wanted to share a couple of ideas that might strengthen observability and compliance handling, especially as usage scales across more complex applications. AI-assisted auth policy tuning and compliance automation As apps grow, manually configuring session lifecycles, MFA rules, and SSO settings can become error-prone, especially when trying to meet frameworks like gdpr, SOC2, or HIPAA. It might be valuable to introduce an AI layer that reviews app architecture and usage patterns to suggest optimal policy settings. For example, it could recommend different session expirations for high-risk vs. low-risk users or identify gaps in MFA enforcement for sensitive endpoints. Additionally, building automated compliance readiness checks and report templates could make audits much less painful for teams using Supertokens. AI-driven observability and edge-case testing - Supertokens already offers strong testing tools, but at scale, deeper observability could help preempt issues before they hit prod. Imagine an AI monitor that flags unusual login behavior, latency spikes, or unexpected token usage trends. - Pairing that with auto-generated test cases based on past errors or new config changes—using traffic replays or fuzz testing—could give teams much more confidence during deploys. Happy to dive deeper into these if useful—really looking forward to seeing how Supertokens evolves.