CloudShield AI

AI-Driven Defense Against Cloud Vulnerabilities

CloudShield AI employs multiple LLM agents to autonomously identify and exploit cloud security vulnerabilities in real-time. By providing dynamic analysis, attack path exploration, and detailed remediation strategies, CloudShield AI enhances cloud security policies and prevents potential attacks.

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Why It Matters

Cloud security is a critical challenge in today's digital landscape, with misconfigurations and cyber attacks causing billions in losses annually. Security engineers must manage thousands of access policies for countless users, groups, roles, and data stores, making the system prone to errors. Frequent changes, such as adding temporary contractors, complicate tracking and verification. AWS alone offers thousands of actions, providing numerous opportunities for attackers to exploit. New strategies emerge regularly, further exacerbating these vulnerabilities.

Slow Detection

With data stored in the cloud, it takes an average of 277 days to identify and contain a breach. IBM Report

Increased Attacks

Attacks on open-source libraries, critical to cloud systems, have increased by 742% since ChatGPT's release.

Phishing Attacks

Phishing emails, which can target cloud credentials, have increased by 1265% since ChatGPT launched. CNBC Report

Our Vision

We aim to leverage AI and LLM agents to provide an autonomous, intelligent defense system capable of dynamically analyzing current permissions, identifying and exploiting potential vulnerabilities in real-time, and offering detailed remediation strategies to enhance cloud security policies and prevent potential attacks.

Technical Framework Overview

CloudShieldAI leverages the CrewAI agentic framework to integrate Tasks, AI Agents, and Tools. Tasks define steps for identifying data access paths, classifying documents, and simulating attacks. Specialized AI Agents execute these tasks both sequentially and asynchronously, autonomously coordinating and invoking tools that interact with cloud APIs and external packages (e.g. for encryption). This architecture ensures validated actions, minimizing inaccuracies and "hallucinations."

AI Agents Workflow

Our AI agents are given broad task descriptions in natural language. Each agent autonomously creates a sequence of steps, calling the appropriate tools in the correct order. If an exception is raised from the tools, the agent learns, recovers, and attempts the task again.

Analyze Access Paths

Determine all data stores accessible by the user through various policies, groups, and roles.

Classify Content

Classify the content of all documents to determine if they are sensitive for the user to access.

Perform Attacks

For each sensitive document, attempt data exfiltration and ransomware attacks.

Collect, Analyze, and Report

Collect all results, analyze the user's security policy, and generate a comprehensive report detailing findings and strategies to fix identified vulnerabilities.