AI Secured Howto

Mindgard is an AI security platform that provides continuous automated red teaming and vulnerability remediation to help enterprises deploy AI and GenAI securely.
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How to Use AI Secured

Sign up for an account: Visit the Mindgard website and create an account to access their AI security platform.
Integrate your AI models: Connect your AI/ML models, including LLMs and GenAI applications, to the Mindgard platform. They support integration with TensorFlow, PyTorch, ONNX, Hugging Face, and GitHub.
Configure security testing: Set up automated security testing for your AI models. Define parameters like risk thresholds and testing frequency.
Run comprehensive security tests: Leverage Mindgard's extensive attack library to test your AI models against a wide range of threats, including jailbreaks, extraction attacks, evasion, inversion, data poisoning, prompt injection, and membership inference.
Review security assessment results: Analyze the automated test results to identify vulnerabilities and security risks in your AI models and applications.
Implement security improvements: Based on the assessment results, apply recommended security enhancements and mitigations to strengthen your AI models against potential threats.
Set up continuous monitoring: Enable ongoing security monitoring of your AI assets to detect any changes in security posture due to updates, fine-tuning, or new threats.
Integrate with existing tools: Connect Mindgard's platform with your existing MLOps and SecOps tools for seamless security management across your AI pipeline.
Generate security reports: Use the platform to create detailed reports on your AI security posture for stakeholders and compliance purposes.
Stay updated on new threats: Leverage Mindgard's continuously updated threat library and research to stay protected against the latest AI security risks and vulnerabilities.

AI Secured FAQs

Mindgard is an AI security company that provides automated security testing, detection, and remediation for enterprise AI models and applications, including large language models (LLMs) and generative AI.

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