What is AI red teaming? A practical guide for security teams
AI red teaming is the practice of testing AI systems from an attacker’s perspective to find security weaknesses before they are exploited in the real world. AI systems are no longer limited to answering questions in a chat window. They can search internal documents, call APIs, read emails, write code, query databases, trigger workflows, and act through connected tools. As these systems move…
AI red teaming is a critical practice for security teams in the era of advanced AI systems. It involves testing AI systems from an attacker's perspective to identify potential security weaknesses before they can be exploited in real-world scenarios. This form of adversarial testing is essential for understanding how AI systems could be manipulated, misled, or abused by malicious actors.
The OWASP Top 10 for Agentic Applications outlines several key risks such as agent goal hijacking, tool misuse, privilege abuse, and insecure inter-agent communication, all of which can be addressed through thorough AI red teaming exercises. These exercises encompass testing the AI model, user inputs, connected APIs and tools, RAG pipelines, internal documents, permissions, identity controls, AI agent memory, workflow logic, human approval steps, logging, monitoring, and incident response mechanisms.
By adopting AI red teaming as a structured extension of traditional security practices, organizations can proactively safeguard their AI systems against potential threats and ensure they operate safely within their intended business contexts.
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