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AI Security Platform - Coverage and Capabilities
Learn the key principles and practices to secure AI models, ensuring robustness against emerging threats.


OWASP Top 10 for Large Language Models (LLMs)
Input Validation & Sanitization: Detect and prevent prompt injection, malicious queries, and data poisoning attempts on LLMs.
Contextual Response Filtering: Ensure safe, policy-compliant responses by monitoring LLM outputs for sensitive or disallowed content.
Model Integrity Protection: Monitor and alert on unusual model behavior, ensuring that parameters remain uncompromised and consistent over time.
OWASP Top 10 for Machine Learning (ML)
Model Vulnerability Analysis: Identify and mitigate common ML vulnerabilities such as insecure model endpoints, adversarial inputs, and data leakage.
Access Control & Authentication: Implement strong identity management for ML pipelines, ensuring only authorized entities can train, modify, or query models.
Robust Deployment Architecture: Promote secure development, testing, and deployment practices aligned with recognized ML security guidelines.
NIST Risk Management Framework (RMF)
Categorization & Control Selection: Classify AI systems according to their risk profile, guiding the selection of appropriate security and privacy controls.
Implementation & Assessment: Seamlessly integrate and apply NIST RMF controls to AI workflows, verifying compliance through continuous monitoring.
Authorization & Continuous Monitoring: Maintain an ongoing assessment process, enabling adaptive risk mitigation and up-to-date accreditation of AI assets.
AI Act (EU Artificial Intelligence Act)
Transparency & Explainability: Provide tools to explain model decisions, comply with forthcoming regulatory requirements, and ensure ethical AI usage.
Risk-Based Controls: Align practices with the AI Act’s risk-tiered approach, ensuring high-risk AI systems receive stronger safeguards.
Documentation & Governance: Maintain structured documentation, data lineage, and audit trails to meet compliance obligations and support accountability.
Defense & Attack Techniques in AI
Adversarial Defense Mechanisms: Deploy advanced protective measures against adversarial examples, data poisoning, and model extraction attempts.
Threat Modeling & Simulation: Leverage simulation environments to test resilience against known and emerging AI-specific attack vectors.
Adaptive Security Policies: Use intelligent policy frameworks that learn from detected attacks, dynamically strengthening defenses in response to evolving threats.
By covering recognized industry standards, regulatory frameworks, and known adversarial tactics, our AI Security Platform empowers your organization to confidently adopt AI-driven solutions, maintain robust compliance, and stay ahead of sophisticated attackers.
Ready to learn all about AI Security?
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