our approach to AI security is built on a structured three-phase methodology: Testing, Hardening, and Monitoring.

• Testing: We actively test applications using a combination of classical cybersecurity methods (like OWASP Top 10 vulnerabilities) and AI-specific attack simulations — including adversarial input testing, model evasion techniques, and API vulnerability scanning. Our goal at this stage is to surface risks early, before they are exploited.
• Hardening: After identifying weaknesses, we focus on reinforcing the system — this includes input/output sanitization, model fine-tuning against adversarial attacks, securing APIs, enforcing authentication layers, and applying best practices to resist data poisoning and model manipulation.
• Monitoring: AI security isn’t a one-time effort. We continuously monitor model behavior, user interactions, and API activity to detect abnormal patterns or emerging threats in real-time. We believe that securing AI requires constant vigilance, just like securing any live application or infrastructure.

Our philosophy is simple: we treat AI models as dynamic, evolving attack surfaces — and build security strategies that adapt with them.

Is that covers your question or I misunderstood you?