Integrating AI into corporate systems creates new security challenges beyond traditional web application testing. While standard checks for authorization and API security remain relevant, the introduction of language models requires evaluating how AI interprets data and interacts with external tools.
AI components vary in risk based on their capabilities. Simple chatbots have limited impact, whereas RAG-based systems can access restricted internal documents. AI agents pose the highest risk, as they can perform actions in connected systems like CRM or calendars using service accounts.
A critical vulnerability arises when AI search components access files with broader permissions than the end-user. If the system fails to verify user authorization before the AI processes a document, the model can inadvertently expose sensitive information that would otherwise be restricted.
Prompt injection allows attackers to manipulate AI behavior by embedding malicious instructions within data sources like documents or emails. This can lead to unauthorized data access, XSS attacks if the application fails to sanitize AI output, or unauthorized actions performed by AI agents.
CISO-level security assessments must focus on the architecture rather than just the model itself. Key areas include verifying user permissions before AI processes data, restricting service account privileges, and ensuring that AI-generated output is treated as untrusted input.
