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Permit.io AI Access Control – Fine Grained Permissions for AI Apps

Hacker News

Permit.io AI Access Control – Fine Grained Permissions for AI Apps

Hi HN, I'm Gabriel, VP of DevRel at Permit.io. We keep hearing from AI developers that there’s no simple way to enforce access control in AI workflows—so most either build permission logic for their AI implementation from scratch. To help with this, we are introducing Permit.io AI Access Control - a set of new integrations with PydanticAI, LangChain, MCP, and LangFlow that allow you to easily implement fine-grained authorization (FGA) into AI applications. These integrations allow developers to enforce structured prompt validation before inputs reach AI models, control AI access to RAG data sources, apply identity-based permissions to AI-driven actions and decisions, and apply filtering to AI-generated responses. I’d love to hear about how you’ve been handling access control in your AI applications, and what you think about his solution.

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, mcp, apps · Missing: mac, agents, macos
88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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