AI red team: the profession that tests the limits of generative systems

Professionals working in artificial intelligence need to understand not only what a tool can do, but also how it can fail. This is where AI red teamers come in, responsible for identifying vulnerabilities in models and applications before malicious users can exploit them.

The growth of these systems, which can generate text, analyze documents, and perform tasks with varying levels of autonomy, has increased demand for this role. Companies need to ensure that tools comply with security limits, protect confidential information, and respond appropriately to unexpected situations.

What does an artificial intelligence red teamer do?

The concept of red teaming originates from a security practice that simulates an adversary’s behavior to test a system’s defenses. In AI, the goal is to identify situations where a model provides inappropriate responses, discloses sensitive data, or performs actions it should block.

The U.S. National Institute of Standards and Technology (NIST) defines AI red teaming as a structured effort to identify flaws, vulnerabilities, and undesirable behaviors in these systems.

In practice, professionals create test scenarios to verify whether protections work. For example, they might try to convince a chatbot to ignore its instructions, trick an assistant into revealing confidential information, or insert malicious commands into a document the tool needs to process.

The work isn’t just about finding flaws. It’s also about recording the conditions under which they occur, assessing risks, and communicating results to teams responsible for product development and security.

What skills are required to work in this field?

Requirements vary by role. Some positions focus on cybersecurity and require knowledge of programming, application testing, APIs, and vulnerability analysis tools. Others prioritize content evaluation, identifying unexpected behaviors, and understanding risks associated with generative models.

A Red Team Engineer position posted by Anthropic, developer of Claude, requires experience in security testing, techniques for bypassing model protections, and analyzing systems that use AI agents. The job description also highlights the ability to automate tests and turn technical findings into concrete improvements.

The role also requires critical thinking, creativity, and communication skills. Since models can respond differently to similar commands, professionals need to formulate hypotheses, test variations, and document results so other teams can reproduce the problems encountered.

How can you develop AI skills for this profession?

Understanding how generative models work helps explain why they might misinterpret instructions, present false information, or follow commands that conflict with their rules.

For those looking to enter the field, a first step is to learn concepts like prompt injection—a technique that tries to manipulate a system through malicious instructions—and jailbreaking, a set of strategies to bypass a model’s restrictions.

Microsoft’s guide on red teaming language models recommends structured tests to identify risks and assess whether protection measures work. The company emphasizes that finding flaws is only one step: results must guide subsequent evaluations and corrections.

Learning can start with controlled tests on AI tools, observing how they react to ambiguous instructions, contradictory requests, and different contexts. To advance professionally, however, it’s necessary to complement this exploration with technical knowledge and security practices aligned with the desired role.

What is the salary of an AI red teamer?

Salary depends on seniority, specialization, company, and country. A concrete example comes from a position at Anthropic for a red team engineer in system protection: the company lists an annual salary range of $320,000 to $405,000 for the role in the United States.

This amount does not represent the average salary of all red teamers. The position requires prior experience in security, penetration testing, or application protection, as well as specific knowledge of vulnerabilities in AI systems.

The field is not limited to engineers. Red team groups can include security specialists, researchers, content analysts, and professionals with knowledge of risks associated with specific applications. The composition depends on the system being tested and the problems the company needs to investigate.