One of the risks of AI and machine learning systems is that they extend the attack surface. Whether it is poisoned datasets or adversarial queries, mo...
One of the risks of AI and machine learning systems is that they extend the attack surface. Whether it is poisoned datasets or adversarial queries, modern hackers have devised tactics to manipulate, steal, and degrade machine learning models. It is for this reason that Secure Code provides clients with a top-shelf AI Security suite to address end-to-end protection for the AI/ML lifecycle.
We specialize in safeguarding data collection and preprocessing pipelines against tampering, injection, and unauthorized access, ensuring training data remains valid and trustworthy. Secure Code integrates adversarial attack prevention techniques during training and validation to prevent evasion and poisoning.
Post-deployment, we enforce model integrity verification and drift detection, giving your organization continuous assurance that the deployed model behaves regardless of evolving inputs and operating environments.
If you opt for a distributed or collaborative learning environment, we deploy secure federated learning protocols, allowing multiple parties to train shared models without exposing raw data.
Finally, we embed governance by design, ensuring the framework aligns with global AI security and compliance standards, including the EU AI Act, NIST AI Risk Management Framework, and ISO/IEC standards.