AI systems aren’t just “smart software.” They’re dynamic, adaptive and often opaque. Their very nature demands a new security paradigm — one grounded in proactive testing, adversarial thinking and continuous evaluation. 

This is where red teaming for AI comes into play. 

AI systems have attack surfaces that evolve, adapt and often behave in unpredictable ways. Treating them (and securing them) like static software components is a recipe for failure. 

Whether it’s generative AI (GenAI) misbehaving in customer support or a recommendation model being manipulated through data poisoning, the threats are real and they’re evolving. 

Some of the biggest risk factors are rooted in the fact that GenAI apps are: 

• Non-deterministic 

• Susceptible to prompt injections 

• Prone to hallucinations 

• Often over-trusted by end users 

Core Building Blocks of a Red Teaming Strategy 

To build an effective AI red teaming strategy, organizations must combine technical rigor with strategic clarity. Here’s a four-step roadmap: 

1. Define Your Threat Priorities

What are you trying to protect and what’s the threat? 

Whether it’s safeguarding training data, preventing prompt injection, or ensuring content safety, your red teaming strategy should align with real, business-driven concerns. Avoid generic checklists. Tailor your tests to the specific AI assets that matter most. 

2. Understand the System Context

Red teaming is not just about the model; it’s about the infrastructure around it. That includes: 

• The data pipeline 

• API integrations 

• User interface and inputs 

• Downstream applications or automation 

Every AI model is a software dependency and if it influences decisions, it’s part of your attack surface. Multiple modalities increase the attack surface. 

3. Choose the Right Mix of Tools and Techniques

You don’t need to choose between manual and automated. You need both because manual testing brings precision, context and creativity, while automated red teaming brings scale, speed and repeatability. 

4. Assemble the Right Team

This includes: 

• Data scientists who understand the model’s internals 

• ML engineers who can interpret outputs and behaviors 

• Security experts who think like adversaries 

• Legal and ethics teams, when dealing with compliance or safety boundaries 

Common Pitfalls to Avoid 

Here are four common missteps I’ve seen organizations make when building their AI red teaming strategy: 

Boiling the Ocean 

Trying to test everything at once often leads to analysis paralysis. Start with the most business-critical use cases and expand iteratively. 

Red Teaming Too Late 

Security should not be bolted on post-deployment. The earlier you start probing for weaknesses — ideally during model or feature development — the more time you have to course-correct. 

Chasing Novelty Over Relevance 

Fancy adversarial examples may be fun to demonstrate, but are irrelevant in production. Focus on plausible, real-world attack scenarios that would actually impact your users or business. 

Ignoring the Full Pipeline 

Attacks rarely target just the model. They exploit everything from inputs to infrastructure. Your red team should simulate the entire user journey. 

Best Practices for Effective AI Red Teaming 

Building a strong AI red teaming program isn’t about getting everything perfect from day one — it’s about building momentum through consistent, iterative progress. These best practices offer a practical foundation for organizations looking to operationalize red teaming as a core part of their AI development lifecycle. 

Start Small, Iterate Fast 

Pick one model, one use case and one attack type. Learn. Expand. 

Embrace Automation 

Use tools like to continuously and comprehensively probe your models, especially if there are multiple applications being developed. Red teaming every application once is not enough. You need speed and accuracy for repeated testing, which can only be achieved through an automated tool. 

Treat Red Teaming as QA for Security 

Red teaming should be part of the release gate every time an update is pushed to the application, including changes to: 

• Endpoint parameters 

• Dataset fine-tuning 

• System prompts 

It should be performed after every change to assess its security implications. This ensures proper feedback loops on corrective actions and avoids the risk of high AI technical debt when you are closer to production. 

Track Metrics That Matter 

Move beyond “we found a bug” to real metrics like: 

• Number of exploitable jailbreaks over time 

• Number of critical vulnerabilities over time 

• Impact of remediation 

Start your red teaming journey with intent, not ambition. Designate a lead with both AI literacy and a security mindset. Define success metrics, establish a feedback loop between testing and development and treat red teaming as a core engineering discipline — not a one-off exercise. The longer you wait to embed adversarial testing into your AI lifecycle, the harder it becomes to retrofit trust into your systems. Build small, test relentlessly and scale what works.