VAPT Security

  Artificial intelligence is becoming part of everyday business applications. Companies are using AI-powered assistants, customer-facing chatbots, document-search systems, automation platforms and other LLM-based applications. But an AI application becomes a different security problem when it connects to company data, APIs, internal applications or automated workflows . The question is no longer simply whether an AI model can generate a correct answer. The more important question is: What could happen if someone deliberately manipulated the AI application? This is where AI Security Testing becomes relevant. VAPT Security, operated by Nathan Labs Advisory, describes its AI Security Testing approach as an assessment of the complete AI application , rather than focusing only on the underlying model. The scope includes the model, application, data and integrations surrounding it. Why AI Applications Need a Different Security Perspective Traditional application security testing focuses...

  A security alert is not a security response. That distinction matters because modern attacks rarely arrive as one obvious event. An attacker may begin with a compromised credential, move through a cloud environment, access an API, establish persistence, and gradually escalate privileges while individual events appear harmless in isolation. For a UAE organization operating web applications, APIs, cloud workloads, endpoints, and distributed infrastructure, the problem is not simply generating more alerts. The problem is determining which signals represent an actual attack, what those signals mean together, and what should happen next . This is where Managed Detection & Response (MDR) becomes important. Nathan Labs, through VAPT Security, lists Managed Detection & Response among its core cybersecurity services alongside VAPT, advanced adversarial testing, DevSecOps and continuous security, web and mobile testing, network and infrastructure penetration testing, cloud securit...