Author: Evie Roybal

Large language models are increasingly embedded inside SaaS platforms, internal copilots, customer-support systems, developer tools, search products, autonomous agents and business workflows. As soon as an LLM can retrieve confidential information, call an API or perform an action on behalf of a user, the security problem extends far beyond the model itself. LLM security testing…

Know More

AI applications are increasingly connected to information that traditional chatbots never touched: customer records, internal documents, source code, support tickets, financial data, employee knowledge bases, proprietary research and private API responses. That access makes AI systems useful, but it also creates a new security problem. Information that was previously protected behind an application interface may…

Know More

Prompt injection has become one of the defining security problems of modern AI applications. As large language models move beyond simple chat interfaces and gain access to internal documents, APIs, business systems and autonomous tools, the consequences of manipulating model behavior become substantially more serious. A prompt injection attack attempts to influence an AI system…

Know More

Artificial intelligence is no longer isolated inside experimental models or research environments. AI is now embedded in customer support platforms, SaaS products, internal knowledge systems, developer tools, financial workflows, analytics platforms and autonomous software agents. As these systems gain access to company data, APIs and real-world actions, securing the model alone is no longer enough.…

Know More