AI Security 101: What is AI Red Teaming?

Trust in AI depends on security. That’s why 84% of CISOs* are now responsible for overseeing how AI is deployed across their organizations, ensuring safety is embedded from the start.

These systems can behave in unexpected ways, creating vulnerabilities that malicious actors could exploit. To build trustworthy AI, organizations need a way to expose weaknesses before they cause damage. This is the role of AI Red Teaming (AIRT).

What is AI Red Teaming?

AI red teaming is the process of stress-testing AI systems by simulating real-world adversarial attacks and misuse scenarios.

Much like traditional cybersecurity red teams, AI red teams leverage adversarial techniques, taking a holistic approach to test the AI model as well as probe the components of the overall system. Flaws such as prompt injections, bias, evasion tactics, or unsafe outputs are targets to flag and remedy.

The key goal is to identify AI security and safety risks early so they can be mitigated before attackers or system failures exploit them.

These risks can include:

• AI jailbreaking: Strategies used to subvert an AI system's safety features, allowing unintended or harmful actions to take place.
• Over-permissioned agents: Red teams can identify where AI agents may have excessive or unnecessary access to systems, data, or APIs, allowing attackers to escalate privileges, exfiltrate sensitive information, or cause unintended actions if the agent is manipulated.
• Prompt Injection: A major vulnerability in AI systems where malicious inputs can manipulate model outputs, potentially bypassing safety protocols to extract sensitive information.

I recently walked through a prompt injection example that opened a system backdoor on the CISO Series.

Without rigorous testing, organizations won't notice these types of breaches until it's too late. AI red teaming provides the assurance layer needed to deploy AI responsibly.

See How Anthropic’s Jailbreak Challenge Put AI Safety Defenses to the Test

How AI Red Teaming Works

AI red teaming is not a single test but a structured process that leverages the expertise of a diverse and specialized community of security researchers. It often follows stages like these:

1. Scoping and Planning
   • Define which AI systems or models will be tested.
   • Establish objectives, such as evaluating safety, robustness, or ethical risks.
      
2. Threat Modeling
   • Identify potential attack vectors specific to AI, such as prompt injection, data poisoning, or model extraction.
   • Consider both malicious misuse and unintended failures.
      
3. Scenario Design
   • Create realistic test cases that simulate how bad actors, or even ordinary users, might interact with the system.
      
4. Execution and Testing
   • Run adversarial prompts, model manipulations, or data-driven attacks.
   • Observe how the AI responds under stress and where it produces harmful, biased, or exploitable outputs.
      
5. Analysis and Reporting
   • Document vulnerabilities, categorize risks, and map them to potential business or compliance impacts.
   • Provide prioritized recommendations for mitigation.
      
6. Remediation and Re-testing
   • Apply fixes, update safeguards, or retrain models as needed.
   • Re-run tests to validate that the vulnerabilities have been resolved.

By following this structured approach, organizations move beyond one-off experiments and instead build a repeatable feedback loop that strengthens AI systems over time.

AI Red Teaming vs. Traditional Red Teaming

Traditional red teaming and red teaming for AI share a core philosophy: think like an adversary to uncover weaknesses. However, there are important distinctions:

Both are valuable efforts, and together, they form a comprehensive security strategy. Traditional testing secures systems, while AI red teaming secures the intelligence running inside them.

Building Trustworthy and Resilient AI Systems

AI is advancing at a pace that makes its benefits and risks inseparable, and red teaming is one of several practices that can help organizations better understand how their AI systems might behave under pressure and where improvements are needed. 

It is not a silver bullet, but it provides valuable insights that complement other security and governance measures.

Discover how human-led AI Red Teaming can uncover your critical AI vulnerabilities

_{*Survey methodology: Oxford Economics surveyed 400 CISOs from April to May of 2025. Respondents represented four countries (US, UK, Australia and Singapore) and 13 industries (Telecommunications, Real Estate/Construction, Utilities, Government/Public Sector, Consumer Goods, Education, Retail, Banking/Financial Services/Insurance, Retail/Ecommerce, Manufacturing, Healthcare, Transport/Logistics, and Not-for-profit/Non-profit). 70.5% of respondents worked at publicly-held organizations, while the other 29.5% worked for private organizations. Roughly 2 out of 5 respondents work at smaller organizations (between 1,000 and 2,500 employees); respondents from organizations with at least 10,000 FTEs make up 27% of the sample. Finally, revenue breakdowns are evenly split across 5 revenue buckets: Less than $500m; $501m to $999m; $1b to $4.9b; $5b to $9.9b; and $10b and more.}