Armor scans the model before adversarial attacks and after adding adversarial attacks. The metrics, are calculated both times to evaluate the difference before and after the selected adversarial attacks.
Users can apply Defense mechanisms complementing the adversarial attacks to AI model(s) . One of the most effective defenses is Styrk’s proprietary defense mechanism. The model scanned after applying the proposed mitigation mechanisms will show new values for all the metrics. Comparing the values before and after applying Defenses in the model can help the user in taking an informed decision about the kind of defenses that can be applied to the model to make it more robust.

Trust is an advanced tool designed to identify and mitigate bias in classification-based AI models. It meticulously scans the outputs of these models to detect potential biases and provides a comprehensive report detailing the presence of bias both before and after the application of mitigation strategies.

Portal is designed to safeguard large language models (LLMs) from various risks and threats along with protecting sensitive information (PII,PCI, etc.) of the organisation/user. It focuses on protecting LLMs from harmful content, malicious attacks, and unintended outputs. It continuously monitors inputs and outputs for vulnerabilities such as prompt injections, and the generation of biased or toxic language by LLM. It also keeps monitoring at real-time any sensitive information in the prompts and masks it before passing it to LLMs.

To ensure compliance with privacy regulations Cypher measures, monitors and masks sensitive and personal data or any data as per the custom expression given by the user in unstructured data. It leverages artificial intelligence (AI) and machine learning (ML) algorithms to automatically detect and de-identify sensitive keywords. Upon identifying sensitive data, it presents it in an informative format (with sensitive data masked).