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With artificial intelligence (AI) advancing at a breakneck speed, cybersecurity now stands at the critical intersection of two realities: AI is fortifying cyber defences while also enabling more sophisticated and potent attacks.
PwC’s 2026 Global Digital Trust Insights survey captures the pressure on security leaders. Around 60% of business and technology leaders are making cyber risk investment one of their top three strategic priorities in response to geopolitical uncertainty. It also points that only 6% feel confident across all vulnerabilities surveyed, while AI enablement is now a leading priority for cyber budgets, managed services and talent gaps.
That gap between ambition and resilience is where the concept of AI red teaming is gaining ground and steadily moving into enterprise security programmes.
The financial stakes are also rising. IBM's 2025 Cost of a Data Breach report put the global average cost of a data breach at a staggering USD 4.44 million.
While traditional security testing asks whether systems can withstand known attacks, AI red teaming asks a broader question: how could an AI system fail, misbehave, leak data, produce harmful outputs, follow malicious instructions or create business risk when deployed in real environments?
For organisations adopting generative AI, agentic AI and AI-enabled security tools, red teaming is becoming a practical route to safer deployment, stronger governance and better evidence for compliance, including obligations under the EU AI Act.
The cybersecurity community eventually embraced the term, associating it with the practice of learning to think like the enemy.
A red team in cybersecurity is a group of authorised testers that behaves and thinks like a real attacker. Its job is to probe systems for failures, expose weaknesses and help defenders close gaps before adversaries exploit them.
AI red teaming applies that mindset to AI models, applications and workflows to identify how they could fail, be manipulated or cause harm. In AI environments, the test surface is broader as red teams deploy a range of tactics and techniques including prompt attacks, data poisoning, exfiltration, and adversarial examples.
The next stage is scenario design and execution. Red teamers create adversarial prompts, attack chains and misuse cases to expose blind spots, including prompt injection, model evasion, content policy violations and attempts to extract sensitive training data. They then probe the system, observe its behaviour and record failure cases, assessing whether safeguards activate as intended and whether unexpected behaviours emerge.
Some tests are manual, while others use automated prompt generation or adversarial models.
The final stage is remediation. Findings are shared with development, security and risk teams so they can strengthen controls before deployment or release. Mitigations may include stronger input filtering, refined fine-tuning, tighter tool permissions, policy updates, improved monitoring and retesting. In enterprise settings, this creates an evidence trail for AI governance, cybersecurity assurance and responsible deployment.
Having established a dedicated AI red team in 2018, the US tech giant has been among the industry leaders in this segment, addressing a growing landscape of security risks. In 2024, Microsoft announced the release of PyRIT (Python Risk Identification Tool), an open automation framework to red team generative AI systems. The tool helps researchers identify flaws in their own AI systems, including Copilot.
In a whitepaper, the firm highlighted several key takeaways from red teaming 100 generative AI products. Chief among them is that generative AI systems have introduced novel cyberattack vectors along with amplifying existing security risks. As a result, AI red teams need to be attuned to both new and old cyberattack vectors.
Secondly, AI red teaming relies heavily on human expertise and cannot be automated entirely. Humans are required for subject matter expertise, cultural competence, and emotional intelligence. While tools like PyRIT can scale up operations, keeping humans in the red teaming loop is essential to identifying impactful AI safety and security risks.
Google
Google’s AI red team focuses on simulating real-world adversaries targeting AI deployments, including nation states, cybercriminals, hacktivists and malicious insiders. Its approach emphasises realistic attack scenarios, rules of engagement and the need to test AI systems once they are integrated into products and given access to sensitive information or actions. It leverages the latest insights from threat intelligence teams including Mandiant, the Google Threat Intelligence Group, content abuse red teaming from Trust and Safety, and adversarial research from Google DeepMind.
A core part of Google’s security strategy is automated red teaming, where its internal team constantly attacks Gemini in realistic ways to uncover potential security weaknesses in the model.
Among its key learnings from AI red teaming, Google noted AI systems operate probabilistically, making them great at recognising patterns and resilient to uncertainty. However, this can work in favour of attackers, allowing them to intentionally probe AI models to discover the point at which they begin behaving incorrectly. To tackle this, Google's AI red team has adapted its methods of attack, including prompt attacks and data poisoning, to test a range of system defences, using tactics, techniques and procedures realistic for real-world adversaries.
IBM
IBM's X-Force red team follows an iterative, continuous testing process to stress- test risky vulnerabilities across AI systems. Its work spans model safety and security testing, generative AI application testing, and AI platform security testing.
IBM has also used red team LLMs to generate adversarial and open-source datasets that have helped to improve its Granite family of models on watsonx, as well as Aurora, an open-source community multilingual model.
OpenAI
AI behemoth OpenAI’s approach combines external human red teaming with automated methods. Its red teaming campaigns include scoping, selecting testers with diverse background, deciding model access, and determining the format of their final reports. OpenAI has rigorously applied red teaming to test its DALL-E 2 image generation model and LLM model GPT-4 to reduce harmful outputs.
OpenAI also notes that red teaming has limits because risks change over time and some findings can create information hazards if mishandled.
AI red teaming is a testing of AI systems to identify how they could fail, be manipulated, leak data or produce harmful outputs.
Why is it called red teaming?
The term comes from military and cybersecurity exercises where a red team acts as the adversary and a blue team defends.
What is a red team in cybersecurity?
A red team in cybersecurity is an authorised group that simulates real attackers to test an organisation’s systems, controls and response capabilities.
Why does AI red teaming matter for enterprises?
It helps enterprises find and fix AI risks before systems are deployed into customer, employee or operational workflows.
PwC’s 2026 Global Digital Trust Insights survey captures the pressure on security leaders. Around 60% of business and technology leaders are making cyber risk investment one of their top three strategic priorities in response to geopolitical uncertainty. It also points that only 6% feel confident across all vulnerabilities surveyed, while AI enablement is now a leading priority for cyber budgets, managed services and talent gaps.
That gap between ambition and resilience is where the concept of AI red teaming is gaining ground and steadily moving into enterprise security programmes.
The financial stakes are also rising. IBM's 2025 Cost of a Data Breach report put the global average cost of a data breach at a staggering USD 4.44 million.
While traditional security testing asks whether systems can withstand known attacks, AI red teaming asks a broader question: how could an AI system fail, misbehave, leak data, produce harmful outputs, follow malicious instructions or create business risk when deployed in real environments?
For organisations adopting generative AI, agentic AI and AI-enabled security tools, red teaming is becoming a practical route to safer deployment, stronger governance and better evidence for compliance, including obligations under the EU AI Act.
Understanding AI red teaming
The term ‘red teaming’ has its genesis in the Cold War, when the US military carried out simulation exercises with red teams acting as the Soviets and blue teams representing the US and its allies.The cybersecurity community eventually embraced the term, associating it with the practice of learning to think like the enemy.
A red team in cybersecurity is a group of authorised testers that behaves and thinks like a real attacker. Its job is to probe systems for failures, expose weaknesses and help defenders close gaps before adversaries exploit them.
AI red teaming applies that mindset to AI models, applications and workflows to identify how they could fail, be manipulated or cause harm. In AI environments, the test surface is broader as red teams deploy a range of tactics and techniques including prompt attacks, data poisoning, exfiltration, and adversarial examples.
How AI red teaming works
An effective AI red teaming usually begins with scope. Teams decide what is being tested, whether it is a large language model (LLM), APIs, or data pipelines. They also define the attack surfaces to target and the harms to simulate, such as sensitive data exposure, unsafe outputs, model misuse or business process manipulation.The next stage is scenario design and execution. Red teamers create adversarial prompts, attack chains and misuse cases to expose blind spots, including prompt injection, model evasion, content policy violations and attempts to extract sensitive training data. They then probe the system, observe its behaviour and record failure cases, assessing whether safeguards activate as intended and whether unexpected behaviours emerge.
Some tests are manual, while others use automated prompt generation or adversarial models.
The final stage is remediation. Findings are shared with development, security and risk teams so they can strengthen controls before deployment or release. Mitigations may include stronger input filtering, refined fine-tuning, tighter tool permissions, policy updates, improved monitoring and retesting. In enterprise settings, this creates an evidence trail for AI governance, cybersecurity assurance and responsible deployment.
How leading enterprises are applying AI red teaming
MicrosoftHaving established a dedicated AI red team in 2018, the US tech giant has been among the industry leaders in this segment, addressing a growing landscape of security risks. In 2024, Microsoft announced the release of PyRIT (Python Risk Identification Tool), an open automation framework to red team generative AI systems. The tool helps researchers identify flaws in their own AI systems, including Copilot.
In a whitepaper, the firm highlighted several key takeaways from red teaming 100 generative AI products. Chief among them is that generative AI systems have introduced novel cyberattack vectors along with amplifying existing security risks. As a result, AI red teams need to be attuned to both new and old cyberattack vectors.
Secondly, AI red teaming relies heavily on human expertise and cannot be automated entirely. Humans are required for subject matter expertise, cultural competence, and emotional intelligence. While tools like PyRIT can scale up operations, keeping humans in the red teaming loop is essential to identifying impactful AI safety and security risks.
Google’s AI red team focuses on simulating real-world adversaries targeting AI deployments, including nation states, cybercriminals, hacktivists and malicious insiders. Its approach emphasises realistic attack scenarios, rules of engagement and the need to test AI systems once they are integrated into products and given access to sensitive information or actions. It leverages the latest insights from threat intelligence teams including Mandiant, the Google Threat Intelligence Group, content abuse red teaming from Trust and Safety, and adversarial research from Google DeepMind.
A core part of Google’s security strategy is automated red teaming, where its internal team constantly attacks Gemini in realistic ways to uncover potential security weaknesses in the model.
Among its key learnings from AI red teaming, Google noted AI systems operate probabilistically, making them great at recognising patterns and resilient to uncertainty. However, this can work in favour of attackers, allowing them to intentionally probe AI models to discover the point at which they begin behaving incorrectly. To tackle this, Google's AI red team has adapted its methods of attack, including prompt attacks and data poisoning, to test a range of system defences, using tactics, techniques and procedures realistic for real-world adversaries.
IBM
IBM's X-Force red team follows an iterative, continuous testing process to stress- test risky vulnerabilities across AI systems. Its work spans model safety and security testing, generative AI application testing, and AI platform security testing.
IBM has also used red team LLMs to generate adversarial and open-source datasets that have helped to improve its Granite family of models on watsonx, as well as Aurora, an open-source community multilingual model.
OpenAI
AI behemoth OpenAI’s approach combines external human red teaming with automated methods. Its red teaming campaigns include scoping, selecting testers with diverse background, deciding model access, and determining the format of their final reports. OpenAI has rigorously applied red teaming to test its DALL-E 2 image generation model and LLM model GPT-4 to reduce harmful outputs.
OpenAI also notes that red teaming has limits because risks change over time and some findings can create information hazards if mishandled.
Essential related reads:
- Ireland and NIS2 Directive: A new era of cybersecurity governance
- DORA regulation: Bolstering cyber resilience across the EU financial sector
- What makes Ireland a hotbed for cybersecurity talent
FAQs: AI Red Teaming
What is meant by AI red teaming?AI red teaming is a testing of AI systems to identify how they could fail, be manipulated, leak data or produce harmful outputs.
Why is it called red teaming?
The term comes from military and cybersecurity exercises where a red team acts as the adversary and a blue team defends.
What is a red team in cybersecurity?
A red team in cybersecurity is an authorised group that simulates real attackers to test an organisation’s systems, controls and response capabilities.
Why does AI red teaming matter for enterprises?
It helps enterprises find and fix AI risks before systems are deployed into customer, employee or operational workflows.
