AI-Based Security Tooling for Agentic Workflows

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AI-Based Security Tooling for Agentic Workflows

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: 00:02:24 - 00:03:18

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

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Direct Quote

"We can actually tell what they're doing. We can impose constraints on them and we can have confidence that tomorrow they're not going to leak our private information."

Market Gap

AI agents pose unique security vulnerabilities for enterprises.

As AI agents become more integrated into business processes, they present significant security vulnerabilities that traditional security measures cannot effectively address. Unlike human employees, these agents operate continuously, can access multiple endpoints simultaneously, and can generate sophisticated hacking tools. This increases the risk of data leaks and unauthorized access, leading to potential financial and reputational damage for organizations. Current security solutions are inadequate for managing the unique behaviors and risks posed by these agents, necessitating the development of new tooling specifically designed to secure AI workflows and ensure compliance.

Summary

The business idea revolves around creating a specialized security tooling platform tailored for AI agents involved in enterprise workflows. This platform would leverage the unique capabilities of AI to monitor agent behavior, enforce security policies, and provide insights into potential vulnerabilities in real-time. By utilizing advanced machine learning algorithms, the platform can continuously adapt to new threats and ensure that agents do not misuse sensitive data. The target audience would be enterprises that increasingly rely on AI agents for various functions but lack adequate security measures to protect their systems. The platform could integrate features such as anomaly detection, policy enforcement, and reporting to provide comprehensive security coverage.

Categorization

Business Model
SaaS
Target Founder
Technical
Difficulty
High
Time to Revenue
6-12 months
Initial Investment
$10,000+

Potential MRR (18-24 months)

Conservative
$4,000 - $10,000 MRR
Moderate (Most Likely)
$20,000 - $40,000 MRR
Optimistic
$60,000 - $100,000 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
8/10
Overall
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?

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