Red Teaming in the Age of AI

Humans love testing boundaries. A kid might learn a lot about how machines work by trying to break them. People experiment with what’s possible by trying to take something apart and put it back together. And one way to help make sure your technology systems are safe is to try to break in yourself, which is called red teaming. Red teaming is undergoing some transformation, however, with new considerations like hybrid work and the proliferation of AI. It’s not dead, and it matters more than ever.

We’ve gone from pen testing to enterprise strategy

Penetration testing checks to see if there’s a door open. Red teaming assumes an intruder will find it, wherever it is, and exploit that open door, so it evaluates what is likely to come next. This shift from checklist-style testing to adversary simulation has reshaped how organizations think about security. Modern red teams will go beyond the networks intophysical access and even social engineering, exposing the full spectrum of risk.

The lesson for leadership here is that red teaming isn’t just a technical exercise a lone ethical hacker completes in the void. It’s a strategic lens that can highlight how technology, processes, and even people will hold up under real-world pressure and context. This is an important aspect of executing and managing security as the landscape continues to undulate and shift beneath the tech stack. This house you’ve built—is there a window open you’ve forgotten about, or worse, did someone open the window thanks to a request they thought you made? Red teaming can account for it.

Red teaming meets AI

Generative AI has expanded the attack surface. Vulnerabilities like prompt injection or data leakage can slip through unnoticed, so regulators are increasingly expecting red teaming for AI models. But where red teaming is a mature methodology in cybersecurity, the definition and approach aren’t quite as clear.

That said, frameworks like the EU AI Act and NIST’s red-teaming initiatives are pushing enterprises to prove they’ve assessed risks before deploying AI at scale. But it isn’t only outside vulnerabilities. Red teaming can also reveal weaknesses in the model itself, such as unintended biases or points where machine outcomes diverge subtly from what the user intended.  For executives, this is a critical point for maintaining customer trust and ensuring technical safety.

Regulatory and risk dimensions

Compliance isn’t a checkmark. Organizations have to build resilience under scrutiny. Regulators want evidence that these AI products will hold up against real-world adversaries from both human and other AI sources.

Red teaming in AI introduces new layers of complexity because AI models don’t have static vulnerabilities; they behave probabilistically. A model that appears safe in one context might generate harmful outputs in another. This makes repeatability and consistency, which are cornerstones of traditional testing, much harder to achieve. Other challenges include: 

• Bias, fairness, and accountability: Boards are realizing that a biased model can spark just as much damage as a data breach.
• Lack of Standardization NIST, the EU AI Act, and other bodies are beginning to outline requirements, but methods differ, leaving organizations in a gray area. 
• Resource and Skills Gaps AI red teaming requires a blend of expertise (someone has to understand the intersection of machine learning, adversarial research, security, ethics, and compliance), but few organizations have all of these skills in-house. Outsourcing helps, but it can introduce confidentiality and oversight concerns.

See also: The Cost of Poor Software Quality Is Higher Than Ever

Methodologies for integrating red-teaming into AI

The Center for Security and Emerging Technology (CSET) is clear about how these challenges change some fundamentals about red teaming. Because these models aren’t static, red teaming cannot prove that a vulnerability doesn’t exist. Rather, it’s a “snapshot of possible outcomes under specific conditions.” Traditional practices matter still, but require more layers.

AI-driven red teaming platforms

It might seem counterintuitive to use AI to fight AI in AI products, but these platforms can simulate the sheer scope of attack possible. They can fire off thousands of simulated attacks on a model in hours, suggest remediation, and harden system prompts in real time. This takes red teaming from periodic exercises to a continuous security posture able to run alongside development cycles.

Integration with DevSecOps

Rather than make security episodic or something happening at the last stage before deployment (or even riskier, after deployment), DevSecOps principles can integrate security throughout the pipeline. AI can automate these practices so thatvulnerabilities discovered during adversarial testing can feed directly into engineering backlogs, shortening remediation time. Some security teams could also experiment with red team as a service models, where testing is provisioned on demand and scoped to evolving business needs.

Sector-specific red teaming

Even when talking specifically about AI, red teaming should be sector-specific. Health institutions might be more concerned about phishing or malware targeting patient data than nation-state attacks, for example. Red teaming should take these concerns into consideration to build realism into the practice. Additionally, AI-heavy sectors (though at some point this will be all of them) can also invest in multimodal red teaming, ensuring models that process text, images, or audio can withstand adversarial input across formats.

Bringing in company culture

Though perhaps the biggest shift is one of culture. The new mantra of attack yourself first is shaping investment decisions across industries. The red teaming as a service market is expected to reach over $22 billion by 2030. And a survey of adoption from HackerOne and the SANS Institute noted a strong majority of respondents using AI to simulate more realistic attacks in their red teaming efforts. 

From test to trust

The current state of red teaming reflects a simple truth: testing alone is no longer enough. Enterprises must simulate, challenge, and stress-test their systems continuously if they want to maintain resilience in the face of accelerating AI-driven threats, and yes, that includes using AI to combat those threats. It provides a pathway to compliance, a safeguard for reputation, and, ultimately, a foundation of trust.