If you’ve ever watched a heist movie, you know the drill: before the actual robbery, the crew runs through simulations, testing security systems, identifying weak points, and rehearsing their approach. AI red teaming works on the same principle – except instead of planning a heist, you’re preventing one.
But what does AI red teaming actually involve? Beyond the buzzwords and high-level concepts, what are teams physically doing when they red team an AI system? Let’s pull back the curtain on the methods, processes, and real-world examples that show how leading organisations are making their AI systems more secure.
Red Teaming in the AI Context: A Different Game
Traditional cybersecurity red teaming focuses on penetrating network defenses, exploiting software vulnerabilities, and testing incident response. It’s a well-established practice with decades of refinement. AI red teaming shares the adversarial mindset but operates in fundamentally different territory.
Here’s why: AI systems don’t just execute predetermined code – they generate outputs based on probabilistic models trained on massive datasets. This means vulnerabilities can emerge from the training data itself, from the way the model learns patterns, or from unexpected interactions between the model and its environment. You can’t simply scan for known vulnerabilities like you would with traditional software.
AI red teaming goes beyond traditional penetration testing by simulating dynamic, real-world threat scenarios that account for the unique characteristics of AI systems. It’s not just about breaking in – it’s about understanding how AI systems can be manipulated, misled, or exploited in ways their creators never anticipated.
The Three Methodologies: Choosing Your Approach
When it comes to actually conducting AI red teaming, organisations typically choose from three methodologies, each with distinct advantages and trade-offs.
Manual Testing: The Human Touch
Manual red teaming leverages human creativity and expertise to craft adversarial scenarios. Security experts with deep knowledge of AI systems manually create prompts, inputs, and scenarios designed to expose vulnerabilities.
The strength of manual testing lies in its ability to uncover nuanced, unexpected vulnerabilities that automated systems might miss. Human red teamers can think creatively, combining different attack vectors in novel ways, understanding context and subtext, and identifying risks that emerge from how humans actually interact with AI systems.
For example, a manual red teamer might discover that an AI customer service chatbot can be tricked into revealing customer data not through a direct attack, but through a carefully constructed series of seemingly innocent questions that build context over multiple interactions. This kind of sophisticated, contextual exploitation is difficult for automated systems to discover.
The downside? Manual testing is time-intensive and doesn’t scale well. A team of human experts can only test so many scenarios, which means coverage is necessarily limited.
Automated Testing: Scale and Speed
Automated red teaming uses AI systems and predefined rules to generate adversarial inputs at scale. These systems can rapidly test thousands or millions of variations, systematically probing for weaknesses across a wide range of attack vectors.
Automated approaches excel at finding known vulnerability patterns and testing exhaustively within defined parameters. They’re efficient, repeatable, and can provide comprehensive coverage of specific attack types. If you need to test whether your AI system is vulnerable to a particular class of prompt injection attacks, automated testing can run through countless variations in hours.
However, automated systems struggle with creativity. They’re excellent at exploring depth – testing many variations of known attacks – but less effective at discovering entirely new attack vectors. An automated system follows its programming; it won’t suddenly have an intuitive leap about a completely novel way to exploit your AI.
Hybrid Approach: Best of Both Worlds
The most sophisticated AI red teaming programs combine manual and automated methods. Human experts identify new attack vectors and develop initial adversarial scenarios, then automated systems scale those attacks across variations and edge cases.
This approach balances creativity with comprehensive coverage. Manual testing discovers new vulnerabilities and validates subtle risks, while automation ensures those vulnerabilities are thoroughly explored and that known attack patterns are consistently tested at scale.
For organisations serious about AI security, the hybrid approach represents the current best practice. It requires coordination between human expertise and automated tooling, but delivers the most robust security posture.
The Six-Phase Red Teaming Process
Regardless of methodology, effective AI red teaming follows a structured process. While specific implementations vary, most follow a similar arc from planning through remediation.
Phase 1: Scoping and Planning
Every red teaming exercise begins with clear boundaries and objectives. During scoping, teams identify which AI system or model will be tested, define its intended functionality and use cases, map out potential threat vectors, and establish measurable success criteria.
This phase also involves understanding the operational environment. Is this AI processing sensitive healthcare data? Managing financial transactions? Controlling physical systems? The context shapes which risks matter most and how testing should be prioritised.
Smart organisations align this phase with their AI Security Posture Management practices, using posture management to inventory all AI assets and define risk thresholds. This ensures red teaming targets the most critical components.
Phase 2: Adversarial Strategy Development
With scope defined, red teams develop specific attack scenarios that mimic real-world adversarial behavior. This might include model evasion techniques that generate inputs designed to mislead the AI’s decision-making, data manipulation strategies that test how the system responds to poisoned or biased data, prompt injection attacks that attempt to override the system’s instructions, or system exploitation that identifies potential vulnerabilities in the underlying architecture.
The key is thinking like an actual attacker. What would someone with malicious intent try to accomplish? How might they approach exploiting this specific system? Red teams draw on threat intelligence, known attack patterns, and creative brainstorming to develop realistic scenarios.
Phase 3: Execution and Testing
This is where the actual probing happens. Red teams execute their predefined scenarios, monitoring how the AI system responds under adversarial stress. They might use continuous penetration testing approaches, run attacks in sandbox environments, or conduct live testing under controlled conditions.
During execution, red teams carefully document everything: which attacks were attempted, how the system responded, any unexpected behaviors, and any successful breaches or exploits. This documentation becomes crucial for the analysis phase.
Phase 4: Monitoring and Measurement
Throughout testing, teams measure the system’s robustness and response effectiveness. They’re looking for patterns: which types of attacks succeed most often? Where are the weakest points? How does the system degrade under stress?
This phase requires both quantitative metrics (attack success rates, response times, system performance under load) and qualitative assessment (evaluating the severity and potential impact of discovered vulnerabilities).
Phase 5: Reporting and Analysis
Once testing concludes, the red team compiles comprehensive findings. A good red teaming report includes all vulnerabilities identified with specific examples, impact assessments that explain what could happen if each vulnerability were exploited, recommendations for remediation prioritised by severity, and quantified risk scores that help leadership understand and prioritise fixes.
The best reports don’t just list problems – they provide actionable guidance for making the system more secure. Each vulnerability should come with clear remediation recommendations that development teams can implement.
Phase 6: Mitigation and Retesting
The final phase involves actually fixing identified issues and validating those fixes work. Some red teams provide ongoing support during remediation, helping development teams implement effective countermeasures. Once fixes are deployed, follow-up testing confirms vulnerabilities have been addressed without introducing new issues.
This phase is critical because it closes the loop. Red teaming isn’t valuable if findings sit in a report gathering dust. The entire process aims toward a more secure system, which only happens when vulnerabilities are actually remediated.
Real-World Red Teaming in Action
Theory is helpful, but seeing how leading organisations actually implement red teaming brings the process to life. Here are five examples that demonstrate different aspects of effective AI red teaming.
OpenAI: Catching Bias Before Deployment
OpenAI’s red team discovered that their generative AI model could be manipulated into generating biased or harmful content when prompted with highly charged social or political issues. Rather than discovering this through user complaints after launch, they identified it during pre-deployment testing.
Their initial response included content warnings that flagged potentially harmful responses. However, they learned that overly aggressive filtering frustrated users, especially on nuanced topics. They’ve since refined their approach, improving the model’s ability to identify genuinely harmful content without over-censoring legitimate discussions.
This example illustrates an important principle: red teaming findings often require iteration. The first fix might not be the final fix, and organisations need to balance safety with usability.
Microsoft: Adapting to Multimodal Vulnerabilities
When Microsoft’s red team tested a vision language model, they made a crucial discovery: image inputs were significantly more vulnerable to jailbreaks than text-based inputs. Traditional text-based red teaming would have missed this entirely.
This finding forced Microsoft to evolve their testing approach. They shifted to system-level attacks that incorporated multiple input modalities, better mimicking how real adversaries would exploit the system. By thinking beyond text, they uncovered vulnerabilities that threatened the security of their generative AI applications.
The lesson: AI systems that process multiple types of inputs require red teaming that accounts for all those modalities. Text-only testing leaves blind spots.
Anthropic: The Importance of Cultural Context
Anthropic recognised that AI vulnerabilities don’t exist in a cultural vacuum. Their red team tests Claude across multiple languages and cultural contexts, working with on-the-ground experts rather than relying solely on translations.
This approach uncovered issues that monolingual, US-centric testing would miss. The same prompt might be benign in one cultural context but problematic in another. Idioms, cultural references, and social norms vary dramatically across languages and regions.
For organisations deploying AI globally, this example underscores the need for culturally diverse red teaming that accounts for how AI systems will be used across different populations.
Meta: Preventing Critical Infrastructure Vulnerabilities
Meta’s red teaming processes discovered a significant flaw in their Llama framework – designated CVE-2024-50050 – that could have allowed remote code execution. This wasn’t a theoretical risk; it was a critical vulnerability that could have been exploited to compromise systems using the framework.
Upon identification, Meta immediately patched the vulnerability and released updated versions. Because they found it through internal red teaming rather than after external exploitation, they could fix it without incident or data breach.
This example shows red teaming’s value at the infrastructure level. Vulnerabilities in AI frameworks affect not just one application but potentially thousands of systems built on that foundation.
Google: Strengthening Models Against Adversarial Examples
Google’s red team discovered their models could be easily manipulated through adversarial examples in specific training scenarios, leading to incorrect predictions or biased outputs. These weren’t obvious attacks – they were subtle perturbations that caused the model to fail in unexpected ways.
Google responded by implementing adversarial training techniques that exposed their models to adversarial examples during training, essentially teaching them to recognise and resist these attacks. This proactive approach made their models more robust before deployment.
The takeaway: red teaming findings often inform model training itself, not just post-deployment monitoring. The most effective security integrates throughout the AI lifecycle.
Red Teaming as Continuous Practice
Here’s what these examples collectively demonstrate: AI red teaming isn’t a one-time milestone to check off before launch. It’s a continuous practice that evolves alongside your AI systems and the threat landscape.
OpenAI continues refining their content moderation. Microsoft adapts their testing as they develop new multimodal capabilities. Anthropic expands their cultural testing as they enter new markets. Meta and Google continuously test new framework versions and model updates.
The most mature AI security programs treat red teaming as an ongoing capability, not a project. They maintain dedicated red teams or partnerships with specialised providers, conduct regular testing cycles, update their adversarial scenarios as new attack techniques emerge, and integrate findings back into development processes.
This continuous approach makes sense when you consider that both AI capabilities and attack techniques evolve rapidly. A model that was secure six months ago might be vulnerable to newly discovered attack vectors. Regular red teaming keeps your defenses current.
What This Means for Your Organisation
If you’re deploying AI systems, these examples offer a roadmap. You don’t need to build Meta’s or Google’s security infrastructure from day one, but you should understand what effective red teaming looks like and scale your approach to your risk profile.
Start by asking: What’s the worst thing that could happen if our AI system were compromised? How would an attacker approach exploiting it? What would the business impact be? These questions help you scope appropriate red teaming efforts.
Remember that methodology matters less than rigor and coverage. A thorough manual test can be more valuable than superficial automated testing, while comprehensive automated testing might catch issues manual review would miss. The best approach depends on your specific systems, resources, and risk tolerance.
Ready to See Your AI Through an Attacker’s Eyes?
At Aya Data, we’ve helped organisations across industries implement effective AI red teaming programs. Whether you need one-time assessments or continuous security testing, our team brings the expertise and tools to uncover vulnerabilities before they become incidents.
We specialise in all three methodologies – manual, automated, and hybrid approaches – tailored to your specific AI systems and risk profile. Our testing covers the full spectrum from large language models to multimodal AI systems, providing comprehensive security assessments across your AI portfolio.
Want to understand how secure your AI systems really are? Contact us today to discuss your AI red teaming needs or schedule a free consultation where we’ll assess your current AI security posture and recommend appropriate testing approaches.
In our next article, we’ll dive deeper into the attacker’s playbook – exploring specific vulnerability categories and attack strategies that red teams use to expose weaknesses in AI systems. Understanding how attacks work is the first step to defending against them.
