As GenAI begins to roll out across more organizations, it’s becoming clear that it is not just another IT tool but a transformative capability that affects multiple functions, from legal and compliance to product, data, and risk. The wide-reaching impact of GenAI, combined with its unpredictable and opaque nature, introduces new layers of complexity, risk, and responsibility for organizations.
To manage this complexity, AI governance is the critical foundation. It provides a framework that defines how AI should be used, what risks need to be addressed, who is accountable, and how oversight is enforced. AI governance goes beyond meeting compliance requirements. It’s about earning trust and being responsible to stakeholders and society at large.
But governance alone doesn’t verify whether GenAI systems behave as expected. Frameworks provide direction, but not validation. The key question remains: how can organizations ensure their GenAI behaves safely and responsibly in the real world?
In this article, we explore how GenAI red teaming fills that gap, putting governance principles to the test and turning policy into practice.
What is AI Governance?
AI governance is the framework that guides the responsible use of AI within an organization. It sets the foundation for how AI systems should be designed, deployed, and monitored to ensure they align with legal, ethical, and operational standards. Governance frameworks may be guided by regional regulations like the EU AI Act, international best practices such as the OWASP Top 10 for LLMs and GenAI, global standards like ISO/IEC 42001, or suggested/supported by external advisors such as Deloitte, EY, and others.
Effective AI governance brings together stakeholders from a variety of teams to define and uphold principles around fairness, safety, transparency, and accountability.
For traditional AI, governance is often easier to apply due to its predictability and explainability. But GenAI is fundamentally different. Unlike rule-based or predictive models, GenAI systems are non-deterministic and difficult to explain. They can generate different outputs for the same input and can respond in ways that are biased, misleading, offensive, or even leak sensitive information. Because of these characteristics, organizations need a GenAI-specific approach to ensure it behaves safely and responsibly.
What is GenAI Red Teaming?
GenAI red teaming simulates adversarial attacks to uncover vulnerabilities in your GenAI models or applications. This goes beyond standard safety testing by actively adopting the mindset of a malicious actor. The goal is to expose a wide range of potential risks, such as:
- Can your GenAI be tricked into revealing confidential data about the AI itself, the company, and its stakeholders?
- Does your GenAI show bias toward certain users or regions?
- Can users bypass safety controls to generate harmful outputs?
By identifying these vulnerabilities early, organizations can address them proactively before any real harm occurs.
Read more about the fundamentals of GenAI red teaming in our previous article.
Red Teaming’s Role in AI Governance
While AI governance defines principles, sets risk boundaries, and assigns accountability, these frameworks need to be tested and validated to ensure they hold up under real-world conditions; this is where red teaming is indispensable.
When integrated effectively, governance and red teaming reinforce each other:
- Governance defines acceptable and unacceptable behaviors; red teaming tests whether GenAI adheres to those behavioral expectations.
- Governance assigns risk ownership; red teaming provides risk owners with actionable insights to drive improvements and mitigate risks.
- Governance evolves over time; red teaming uncovers patterns and new threats that enable organizations to continuously refine their governance frameworks.
Best Practices for Aligning GenAI Red Teaming with AI Governance
To ensure GenAI red teaming is both effective and aligned with AI governance objectives, we’ve gathered practical best practices from our work with organizations. These help turn red teaming into more than just a test but a valuable part of the governance process.
1. Prioritize Red Teaming Based on Use Case Risk
While most organizations define broad governance principles like fairness, safety, and privacy, not every GenAI application carries the same level of risk across these areas. For example, a credit-scoring model might raise greater concerns around fairness, while a customer-facing chatbot might pose greater risks related to privacy or misinformation.
Start by reviewing each GenAI use case through the lens of your governance priorities, then apply a tiered risk classification to determine which applications should be prioritized for red teaming. This ensures limited resources are focused on those with the highest real-world impact.
2. Involve the Right Teams at the Right Time
Different teams focus on different aspects of red teaming. For example, security teams tend to focus on how to handle the “fails”, while compliance teams are just as concerned with what qualifies as a “pass” and whether it truly meets regulatory expectations. If red teaming is run in isolation, these perspectives are lost, and so are the risks they would have flagged.
Therefore, involve the right teams at the right stages. Engage product and compliance leads early to define scope, include technical and governance experts during test design, and ensure the responsible business unit reviews and acts on the findings. This cross-functional approach helps uncover overlooked risks and ensures findings are evaluated through the right lens.
3. Define Evaluation Criteria Before Testing
To support AI governance effectively, red teaming should follow a consistent evaluation standard. That means deciding upfront what counts as acceptable behavior, what doesn’t, and how to treat borderline outputs. Questions like whether a hallucinated bank account number is a failure should be answered before testing begins. This reduces ambiguity, speeds up review, and helps teams take confident action on findings.
4. Capture Patterns and Feed Back into Governance
Red teaming can uncover issues that existing governance policies don’t fully account for. For example, if a chatbot keeps generating fake but realistic-looking personal data, that might not break any current rule, but it still poses a risk. Findings like this can help teams update review checklists, adjust risk classifications, or create new testing requirements. Governance doesn’t need to change all at once, but red teaming provides real-world signals that help it evolve in a more practical and informed way.
5. Ensure Results Lead to Action
To turn findings into impact, establish a clear process for handling results. Prioritize risks by severity, assign clear ownership for follow-up, and ensure critical issues are reported to leadership. This drives timely remediation, reinforces accountability, and strengthens your organization’s overall approach to AI safety and risk management.
Final Thoughts
As organizations move quickly to adopt GenAI, it can be challenging to balance fast innovation with strong governance and proper testing. While the rush to build is understandable, it’s important to plan early for responsible use. By making GenAI red teaming part of your AI governance, you shift from reacting to problems to actively preventing them. This approach leads to safer, more reliable GenAI and positions your organization for long-term success.
