Dynamic Policy Enforcement System for AI Models

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Dynamic Policy Enforcement System for AI Models

Inspired by a conversation on:

Machine Learning Street Talk (MLST)

Machine Learning Street Talk (MLST)

AI Agents Can Code 10,000 Lines of Hacking Tools In Seconds - Dr. Ilia Shumailov (ex-GDM)

Host: Steven Johnson

Timestamp: 00:15:01 - 00:15:56

Listen to Episode • View Podcast • All Ideas from Machine Learning Street Talk (MLST)

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Feather Flow

Direct Quote

"If you wanted to say, give your passport number to this agent and say, actually, I want you to only use this on a government website and nobody, nowhere else."

Market Gap

Current AI systems lack fine-grained control over sensitive data usage.

As AI models become more embedded in daily tasks, the need for strict control over how sensitive information is used becomes paramount. Users may want to provide sensitive data, such as a passport number, but only for specific, secure contexts. However, current systems do not allow for this level of control, leading to the risk of data being mishandled or exposed. Without a mechanism to enforce such policies dynamically, organizations face significant compliance and security challenges, especially in industries where data privacy is critical.

Summary

This business idea focuses on developing a dynamic policy enforcement system that allows users to impose strict rules on how AI models can handle sensitive data. By integrating this system with existing AI frameworks, users can specify conditions under which certain data can be accessed or processed. For example, a user could dictate that their passport number is only usable in interactions with government websites. The system would utilize a combination of machine learning and formal verification techniques to ensure compliance with these policies, thereby enhancing data security and user trust. Target customers would include businesses in regulated industries, such as finance and healthcare, where data protection is crucial.

Categorization

Business Model
SaaS
Target Founder
Technical
Difficulty
Medium
Time to Revenue
3-6 months
Initial Investment
$1,000 - $10,000

Potential MRR (18-24 months)

Conservative
$3,000 - $6,000 MRR
Moderate (Most Likely)
$10,000 - $20,000 MRR
Optimistic
$30,000 - $50,000 MRR

* Estimates assume solo founder/bootstrap scenario with competent execution

Scores

Clarity
8/10
Novelty
9/10
Feasibility
7/10
Market Potential
8/10
Evidence
7/10
Overall
7.8/10
Found on October 4, 2025 • Analyzed on October 4, 2025 11:00 AM

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How should I validate this saas idea before building it?

2:34 PM

Great question! For a saas idea like this, I'd recommend starting with these validation steps:

  1. Customer interviews: Talk to Technical to understand their pain points
  2. MVP approach: Build a simple landing page to test demand
  3. Competitor analysis: Research existing solutions and identify gaps

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2:35 PM

Yes, and what about the technical implementation? Should I build this myself or hire a team?

2:36 PM

Based on your idea's complexity and 3-6 months, here's my recommendation:

Technical Strategy:

  • Start with no-code tools for rapid prototyping
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2:37 PM

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