AI/ML Devices Security Testing

Expert Team: Our cybersecurity experts, data scientists, and AI/ML engineers bring deep technical knowledge of AI and machine learning algorithms, model vulnerabilities, and device security.

Holistic Security Assessment: We evaluate the entire lifecycle of AI/ML devices, from data input and model training to deployment and ongoing operations, ensuring robust security measures are in place.

Advanced Tools and Techniques: Utilizing tools like IBM Adversarial Robustness Toolbox, CleverHans, TensorFlow Privacy, and proprietary scripts, we deliver in-depth security testing tailored to AI/ML devices.

Enhancing Device Security: Identifying and mitigating security vulnerabilities in IoT/SCADA devices to prevent unauthorized access and control.

Ensuring Operational Continuity: Securing industrial systems to prevent disruptions, sabotage, or data manipulation that could impact operations.

Regulatory Compliance: Ensuring adherence to industry standards and regulations such as NERC CIP, IEC 62443, and other sector-specific security frameworks.

Minimizing Attack Surface: Reducing the potential attack vectors by securing device configurations, network settings, and communication protocols.

Specialized Expertise: Our team combines cybersecurity knowledge with AI/ML expertise, offering a comprehensive understanding of the unique security challenges associated with these technologies.

Tailored Security Testing: Customized assessments based on your specific AI/ML deployment scenarios, threat landscape, and operational needs.

Comprehensive Vulnerability Assessment: Identifying both traditional security vulnerabilities and AI-specific threats, including adversarial inputs, model poisoning, and data extraction attacks.

Actionable Insights and Remediation: Detailed reports with technical guidance on mitigating identified vulnerabilities, enhancing the security and robustness of your AI/ML devices.

Proactive Threat Mitigation: Addressing security gaps before they can be exploited, reducing the risk of adversarial manipulation and ensuring AI/ML models perform as intended.

Secure Data Handling: All data, including model parameters, training datasets, and test inputs, are handled securely with encryption protocols like AES-256 during storage and TLS 1.3 during transmission.

Access Control Policies: Role-based access controls (RBAC) restrict access to sensitive data and models, ensuring that only authorized personnel are involved in the testing process.

Non-Disclosure Agreements (NDAs): NDAs are enforced with our team and clients to safeguard sensitive information and ensure the confidentiality of proprietary models and datasets.

Isolated Testing Environments: Testing is conducted within secure, isolated environments to prevent unauthorized access, data leakage, and potential manipulation during security assessments.

Data Retention Policies: Adherence to strict data retention and deletion policies ensures that sensitive data and models are securely erased or stored according to client requirements post-testing.