AI red teaming: How it works and why firms use it

AI red teaming is a testing practice that recreates attack scenarios to expose security and safety flaws in models, agents and applications. Teams run simulated attacks and unexpected inputs to observe how systems respond before they go live.

Red teams attempt to bypass safeguards, manipulate data or trick models using techniques such as prompt injection, data poisoning and jailbreak attempts. Tests cover standalone models, autonomous agents, systems that call external tools, retrieval-augmented generation workflows and connected APIs. Exercises are designed to mirror attacker behavior and to reveal failures such as unauthorized data access or unsafe agent actions.

Organizations use red teaming to find exploitable behaviors and integration gaps in data pipelines, APIs and network links. The exercises create evidence that systems have been tested under realistic threats and allow teams to map findings to standards such as the NIST AI Risk Management Framework, the EU AI Act and ISO 42001. Simulated attacks let incident response teams observe failure patterns, refine detection rules and reduce the time needed to contain real incidents.

Several consulting firms offer specialized red teaming services. CBIZ Pivot Point Security combines manual red-teaming techniques with governance services for regulated environments. Its testing spans APIs, data stores, network infrastructure, retrieval-augmented generation, agentic workflows and multi-component platforms. The firm targets prompt injection, data poisoning, model drift and bias failures and maps findings to frameworks including the NIST AI RMF, the EU AI Act and ISO 42001.

Reply provides a structured methodology that blends threat modelling, adversarial attack simulation and remediation guidance with continuous monitoring. Its engagements cover machine learning models, large language models and generative AI applications, and include generative AI risk assessments and compliance support for the EU AI Act. The company positions red teaming within broader security governance and risk management processes.

Mindgard applies offensive security techniques and academic AI research to replicate attacker behavior and test systems in realistic settings. Operating as an autonomous red team, it maps weaknesses and runs continuous runtime defenses intended to detect and stop attacks before they affect operations. The firm delivers technical findings for security and engineering teams to act on.

When selecting a red teaming provider, organizations should check whether tests cover the full AI stack: models, agents, APIs and data pipelines. They should evaluate whether attack simulations reflect current adversarial techniques and likely threat scenarios. Alignment with regulatory and governance frameworks matters for teams that must demonstrate compliance. The provider’s ability to integrate findings into internal security and risk processes affects how quickly fixes can be implemented. Ongoing testing and monitoring are used to detect regressions and new vulnerabilities as systems change.

With rising incident counts, organizations are running red teaming ahead of production to find and fix weaknesses and to document system responses to likely attack paths. The exercises provide concrete test results that teams use to prioritize remediation and to update controls before systems are exposed to live users.