Protect your ML infrastructure from data poisoning, model theft, and adversarial attacks with enterprise-grade security frameworks designed for production AI systems.
Attackers can inject malicious data into training sets, compromising model accuracy and creating backdoors for exploitation.
Proprietary models worth millions can be extracted through API queries or compromised infrastructure access.
Unsecured ML pipelines expose you to GDPR, HIPAA, and industry-specific regulatory penalties and lawsuits.
We implement end-to-end encryption for data at rest and in transit, with strict access controls and audit logging. Our approach includes:
Protect your training pipeline from poisoning attacks and ensure model integrity throughout the development lifecycle:
Secure your models in production with comprehensive runtime protection and monitoring:
Implement robust access management and compliance frameworks for your ML infrastructure:
Get a comprehensive security assessment and customized protection strategy for your machine learning infrastructure.
Identifying and blocking malicious inputs before they reach models
With automated monitoring and alert systems
Meeting GDPR, HIPAA, and industry standards
The most prevalent threats include data poisoning attacks (where malicious data is injected into training sets), model extraction through API abuse, adversarial inputs designed to trick models, insider threats from compromised credentials, and supply chain attacks targeting dependencies. Our security framework addresses all these vectors with multi-layered defenses.
We implement multiple protection mechanisms including API rate limiting and query monitoring to detect extraction attempts, model watermarking to prove ownership, model obfuscation techniques that maintain accuracy while preventing reverse engineering, and strict access controls with audit logging. We also deploy honeypot techniques to detect unauthorized access attempts.
We specialize in both scenarios. For existing pipelines, we conduct comprehensive security audits, identify vulnerabilities, and implement security upgrades with minimal disruption to your operations. For new deployments, we build security into the architecture from day one. Our phased approach ensures continuous operation while systematically hardening your infrastructure.
Our security framework supports GDPR for data privacy, HIPAA for healthcare applications, SOC 2 for service organizations, ISO 27001 for information security management, and industry-specific regulations like PCI-DSS for financial services. We provide documentation and audit trails necessary for compliance verification and certification.
Implementation timelines vary based on your infrastructure complexity and current security posture. A typical deployment includes: 1-2 weeks for security assessment and planning, 2-4 weeks for core security controls implementation, 1-2 weeks for testing and validation, and ongoing monitoring setup. We prioritize critical vulnerabilities first and implement improvements iteratively to minimize business disruption.
Protect your AI models from adversarial inputs and manipulation attacks.
Safeguard your proprietary models and intellectual property from theft.
Ensure your AI systems meet regulatory and industry standards.
Comprehensive evaluation of your AI systems for security and compliance risks.
Don't wait for a security breach. Contact Boaweb AI for a comprehensive ML pipeline security assessment and protect your valuable AI infrastructure.
Based in Lund, Sweden | Serving clients globally |