Quick Testing: AI Security Assessment

Assessment targets AI-enabled application surfaces and control boundaries

• AI-enabled applications, assistants, and agentic workflows in business context
• Model interaction paths, prompts, tools, plugins, and retrieval components
• Sensitive data flows across user input, memory, context, and external systems
• Identity, authorization, and approval boundaries around AI-enabled actions

How AI-enabled systems behave under adversarial interaction and misuse

• Prompt injection, indirect prompt injection, and instruction override attempts
• Testing for data leakage, excessive disclosure, and unsafe retrieval behavior
• Abuse of tools, actions, and connected systems through model-mediated workflows
• Evaluation of guardrails, validation logic, output controls, and human oversight
• Adversarial misuse scenarios aligned to realistic business impact

Guided by practical adversarial testing of LLM-enabled applications and secure design principles for AI-integrated systems.

Results emphasize validated abuse paths and practical security impact

• Demonstrated abuse paths and validated security weaknesses
• Evidence of unsafe data exposure or unauthorized action potential
• Identification of weak control boundaries across prompts, tools, retrieval, and approvals
• Clear indication of what was achievable under realistic conditions

Provides practical input

• Validation of application-layer controls around model usage, tools, retrieval, and data access
• Input for AI governance, documentation, and risk treatment aligned with the EU AI Act
• Support for AI management system design and oversight in line with ISO/IEC 42001
• Risk-oriented recommendations for AI-enabled workflows and misuse scenarios relevant to ISO/IEC 23894