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Artificial Intelligence Red Team Jobs: Exploring the New Frontier in Cybersecurity

Kavish

June 18, 2025

7 Min Read

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As artificial intelligence (AI) rapidly evolves, so does the complexity of digital threats. At the cutting edge of this battle is a niche but rapidly growing field: AI Red Teaming. Organizations are now assembling dedicated AI red teams to simulate adversarial attacks, probe weaknesses in AI systems, and ensure that emerging technologies remain secure, fair, and trustworthy.

In this article, we explore artificial intelligence red team jobs, their responsibilities, the skills needed, career prospects, and how they play a critical role in AI governance and safety.

What is an AI Red Team?

An AI red team functions like a traditional red team in cybersecurity, but with a laser focus on artificial intelligence. Their mission is to test, challenge, and break AI systems before real-world adversaries do. Unlike conventional security teams, AI red teams evaluate:

• Machine learning (ML) model vulnerabilities

• Biases in AI decision-making

• Prompt injection threats in LLMs

• Data poisoning attacks

• Model inversion and extraction

These professionals mimic malicious actors, aiming to identify vulnerabilities in AI systems ranging from facial recognition software to large language models (LLMs) like GPT.

Key Responsibilities of AI Red Team Jobs

Working on an AI red team involves a blend of offensive and defensive AI operations. Job descriptions may vary across organizations, but typical responsibilities include:

• Conducting adversarial testing on machine learning models and AI systems.

• Evaluating AI system robustness to manipulations or unexpected inputs.

• Simulating real-world attack scenarios to test AI model behavior under duress.

• Collaborating with blue teams to provide actionable mitigation strategies.

• Developing tools and frameworks for red teaming AI models at scale.

• Reporting risks and vulnerabilities to stakeholders with clarity and technical depth.

• Ensuring AI alignment, fairness, and compliance with ethical standards.

Skills Required for AI Red Team Roles

The ideal candidate for an AI red team role brings a multi-disciplinary toolkit. Below are core skillsets required:

1. Strong Foundations in Machine Learning and AI

Red teamers must deeply understand model architectures, training processes, overfitting, generalization, and deployment pipelines.

2. Adversarial ML and Model Exploitation Techniques

Skills in data poisoning, evasion attacks, GANs, adversarial examples, and robustness evaluation are vital.

3. Penetration Testing and Cybersecurity Expertise

Although AI red teams differ from traditional cybersecurity red teams, knowledge of common vulnerabilities, threat modeling, OWASP Top 10, and security frameworks adds great value.

4. Programming and Automation

Fluency in Python, familiarity with PyTorch, TensorFlow, and scripting capabilities in Bash, PowerShell, or Go is critical.

5. Prompt Engineering for LLM Testing

Red teamers targeting LLMs need advanced skills in prompt injection, jailbreak techniques, chain-of-thought manipulation, and guardrail bypassing.

6. Ethical Hacking Mindset

AI red teamers must approach systems with creativity, persistence, and curiosity, while maintaining the highest ethical standards.

Typical Roles in an AI Red Team

As AI red teams grow, roles become more specialized. Some of the top positions in this field include:

AI Red Team Engineer

Responsible for designing and executing red teaming scenarios, often focused on adversarial ML.

Prompt Security Analyst

Focuses on testing and breaking large language models through carefully crafted prompts to uncover security loopholes.

AI Security Researcher

Conducts academic-style research into AI vulnerabilities and publishes findings that inform red team tactics.

Technical Program Manager (AI Red Team)

Coordinates between stakeholders and technical staff, ensuring mission alignment, resource planning, and incident response.

Industries Hiring AI Red Team Professionals

The demand for AI red teamers is booming across sectors that deploy high-stakes AI systems. Major industries include:

• Big Tech (Google, Microsoft, Meta, OpenAI)

• Defense and Aerospace

• Finance and Banking

• Healthcare and Pharmaceuticals

• Government Agencies

• AI Startups and Research Labs

Organizations such as Anthropic, Scale AI, OpenAI, and Microsoft have already established AI red teams to ensure the responsible use of their AI technologies.

Salary Expectations and Career Growth

Due to the niche skill set and high impact, AI red team roles offer competitive compensation. Based on industry data from 2025:

• Entry-Level Positions: $110,000 – $140,000 annually

• Mid-Level Engineers: $150,000 – $190,000 annually

• Senior Red Team Leads: $200,000 – $300,000+

• Bonus and Equity are common in tech companies, further increasing total compensation.

Career growth in this domain is promising, with pathways to AI security leadership, chief AI ethics officers, or technical research roles at prestigious AI institutes.

Certifications and Education

Though formal degrees in AI, computer science, or cybersecurity are valuable, the field also welcomes self-taught experts with demonstrable skills. Recommended credentials include:

• Certified Ethical Hacker (CEH)

• Offensive Security Certified Professional (OSCP)

• AI/ML certifications from DeepLearning.AI, Coursera, or Google

• Research papers or published GitHub repositories in adversarial ML or AI security

Top Tools and Frameworks Used in AI Red Teaming

• TextAttack – NLP model adversarial testing

• IBM Adversarial Robustness Toolbox (ART)

• CleverHans – Adversarial attack and defense library

• SecML – Security evaluation of ML systems

• OpenAI Eval and Red Teaming tools – for LLM-specific vulnerabilities

• CyberSecML – Machine learning tools tailored for cybersecurity

Mastering these tools can dramatically improve one’s capabilities and effectiveness on the job.

How to Get Started in AI Red Teaming

For those looking to break into this field:

1. Learn the foundations of AI and machine learning.

2. Dive into adversarial machine learning—read research papers and apply attacks to open models.

3. Participate in AI red teaming competitions like those hosted at DEFCON and by Meta.

4. Build a portfolio demonstrating attack simulations, LLM prompt injection attempts, or robustness testing results.

5. Contribute to open-source projects or write technical blog posts detailing your exploits and solutions.

Future of AI Red Teaming

As the deployment of generative AI accelerates, AI red teaming is becoming indispensable. Red teamers will be central to the effort to make AI systems:

• Safe

• Reliable

• Non-discriminatory

• Resistant to exploitation

Governments, too, are likely to mandate AI red teaming as part of regulatory frameworks. Those who enter the field now will be pioneers in one of the most critical aspects of future technology governance.

AI Red Teaming vs. Traditional Red Teaming

While both AI and traditional red teaming aim to uncover vulnerabilities before adversaries do, their scopes are fundamentally different.

Key Differences Include:

• Target Systems: Traditional red teams focus on networks, servers, and applications, while AI red teams focus on models, data pipelines, and inference behavior.

• Attack Vectors: AI red teamers deal with model-specific attacks like prompt injection, adversarial examples, and hallucination induction—many of which don’t even exist in classical cybersecurity.

• Testing Tools: Tools like Metasploit and Nmap are standard in conventional red teaming. AI red teams, however, use model probing libraries, fuzzing frameworks, and synthetic data generators.

• Success Metrics: Traditional success is often measured in data exfiltration or system compromise. In AI red teaming, success might be getting a model to make a biased decision, leak training data, or generate harmful content.

Understanding these nuances is crucial for organizations looking to build or expand their AI security capabilities.

Common AI Red Team Attack Scenarios

AI red teams explore both technical and social dimensions of threats. Examples of scenarios they simulate include:

• Data Poisoning in Medical ML Models: Introducing corrupt training data to misdiagnose certain patient profiles.

• LLM Jailbreaks: Crafting prompts that trick models into generating unethical, false, or malicious content.

• Bias Probes in Hiring Algorithms: Identifying demographic biases in AI-driven hiring or loan approval systems.

• Model Extraction via API Access: Reconstructing the model by observing outputs over multiple queries.

• Hallucination Exploits in Autonomous Agents: Triggering incorrect inferences in AI-based customer service agents or autonomous systems.

These scenarios highlight the multifaceted and high-stakes nature of AI red teaming work.

Ethical Considerations and Legal Compliance

Ethics is at the heart of red teaming. While the goal is to simulate attacks, all actions must adhere to a strong ethical code. Organizations and red teamers must ensure:

• Strict internal policies around responsible disclosure.

• No harm to users or real-world systems.

• Compliance with GDPR, CCPA, and other data protection laws.

• Alignment with AI safety and fairness guidelines from institutions like NIST, IEEE, and OECD.

The best AI red teamers work hand-in-hand with AI ethics teams, compliance officers, and risk managers to ensure their work supports trustworthy AI.

Leading Organizations in AI Red Teaming

As of 2025, several high-profile companies and labs are pioneering efforts in this space:

• OpenAI Red Teaming Network – External researchers actively engaged in stress-testing ChatGPT and DALL·E.

• Google DeepMind Safety Team – Focused on testing alignment and robustness of frontier AI models.

• Anthropic – Known for their constitutional AI approach and red teaming initiatives.

• Meta AI Red Team – Working to ensure safety in large-scale LLMs and recommendation algorithms.

• Microsoft Azure AI Security – Developing enterprise-grade defenses against AI abuse.

These organizations are setting industry standards for how AI systems are stress-tested, secured, and made more reliable.

Resources to Learn More

For aspiring AI red team professionals or organizations looking to develop internal expertise, these resources are invaluable:

• Books:

  • Adversarial Machine Learning by Joseph & Nelson

  • AI Ethics by Mark Coeckelbergh

• Courses:

  • MIT’s Introduction to Adversarial Machine Learning

  • Udacity’s Secure and Private AI

  • Coursera’s AI For Everyone by Andrew Ng

• Research Papers:

  • Explaining and Harnessing Adversarial Examples (Goodfellow et al.)

  • Red Teaming Language Models with Language Models (OpenAI, Anthropic)

• Communities & Events:

  • DEFCON AI Village

  • OWASP AI Security Initiative

  • IEEE Secure AI Roundtables

  • LinkedIn and Discord AI Red Team groups

Final Thoughts: The Rise of the AI Red Teamer

As AI becomes more embedded in every layer of modern infrastructure, proactive security becomes non-negotiable. AI red team jobs are not just a career—they are a mission-critical function in preserving the integrity, fairness, and resilience of intelligent systems.

For those with the curiosity to explore how AI can fail—and the skills to ensure it doesn’t—this field offers unmatched opportunities, challenges, and rewards.