Overview

AI Security Penetration Testing is a specialized cybersecurity service that evaluates the security of artificial intelligence (AI) and machine learning (ML) systems, including models, datasets, APIs, and deployment environments. This service simulates real-world attacks to identify vulnerabilities, misconfigurations, and weaknesses unique to AI systems. Our testing leverages the OWASP Top 10 for Machine Learning (ML) Framework (2023), ensuring a comprehensive and standardized approach to securing AI/ML deployments.

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OWASP Top 10 for Machine Learning Methodology

We align our testing with the OWASP Top 10 for Machine Learning, focusing on AI-specific vulnerabilities. Key components of our methodology include:

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• ML01: Prompt Injection: Testing for vulnerabilities that allow attackers to manipulate AI model outputs through malicious inputs.

• ML02: Data Poisoning: Assessing the integrity of training datasets to prevent tampering that could compromise model performance.

• ML03: Model Theft: Evaluating protections against unauthorized access to proprietary AI models or their intellectual property.

• ML04: Adversarial Attacks: Simulating attacks that manipulate inputs to cause misclassification or incorrect model behavior.

• ML05: Supply Chain Vulnerabilities: Checking for weaknesses in third-party datasets, pre-trained models, or ML libraries.

• ML06: Insecure Output Handling: Ensuring outputs are sanitized to prevent exploitation, such as injecting malicious code.

• ML07: Sensitive Data Exposure: Identifying risks of exposing sensitive data in training sets or model outputs.

• ML08: Lack of Access Controls: Testing authentication and authorization mechanisms for AI APIs and deployment environments.

• ML09: Model Inversion: Assessing risks of reconstructing sensitive training data from model outputs.

• ML10: Insufficient Monitoring: Evaluating logging and monitoring systems to detect and respond to AI-specific attacks.

 

This methodology ensures thorough coverage of AI-specific risks while adhering to industry best practices.

 

Business Value

AI and ML systems are increasingly integral to business operations but introduce unique security challenges. This service provides:

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• Secure AI Deployments: Protect AI models, data, and infrastructure from exploitation, ensuring reliable performance.

• Safeguarded Innovation: Maintain competitive advantage by securing proprietary AI models and intellectual property.

• Compliance Alignment: Meet emerging regulatory requirements (e.g., EU AI Act, ISO 27001) for secure AI systems.

• Customer Trust: Ensure AI-driven services are secure, fostering confidence among users and stakeholders.

• Proactive Risk Management: Mitigate AI-specific threats, such as adversarial attacks or data poisoning, before they impact operations.

 

This service is critical for organizations deploying AI/ML solutions, ensuring robust protection against evolving threats.

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