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Editor’s note: Louis will lead an editorial roundtable on this subject at VB Transform this month. Secure your spot today.
Artificial intelligence models are facing increasing threats. With 77% of businesses already experiencing adversarial attacks on their models, and 41% of these attacks exploiting prompt injections and data poisoning, it is clear that attackers are outpacing current cybersecurity measures.
To combat this trend, it is crucial to rethink how security is integrated into the models being developed today. DevOps teams must transition from a reactive defense to continuous adversarial testing at each stage.
The Importance of Red Teaming
Safeguarding large language models (LLMs) throughout the DevOps process requires red teaming to be a fundamental element of the model creation process. Rather than viewing security as a final obstacle, as is common in web app pipelines, continuous adversarial testing must be woven into every phase of the Software Development Life Cycle (SDLC).
Adopting a more integrative approach to DevSecOps fundamentals is becoming necessary to mitigate the growing risks of prompt injections, data poisoning and the exposure of sensitive data. Severe attacks like these are becoming more prevalent, occurring from model design through deployment, making ongoing monitoring essential.
Microsoft’s recent guidance on planning red teaming for large language models (LLMs) and their applications offers a valuable methodology for initiating an integrated process. NIST’s AI Risk Management Framework reinforces this, highlighting the necessity for a proactive, lifecycle-long approach to adversarial testing and risk mitigation. Microsoft’s recent red teaming of over 100 generative AI products underscores the importance of combining automated threat detection with expert oversight throughout model development.
As regulatory frameworks, such as the EU’s AI Act, mandate rigorous adversarial testing, integrating continuous red teaming ensures compliance and enhanced security.
OpenAI’s approach to red teaming incorporates external red teaming from early design through deployment, confirming that consistent, preemptive security testing is vital to the success of LLM development.
The Limitations of Traditional Cyber Defenses Against AI
Traditional cybersecurity methods are inadequate against AI-driven threats because these threats differ fundamentally from conventional attacks. As adversaries’ techniques outpace traditional defenses, new red teaming strategies are essential. Here are some examples of the various tradecraft specifically designed to target AI models throughout the DevOps cycles and once deployed:
- Data Poisoning: Attackers insert corrupted data into training sets, causing models to learn inaccurately and leading to persistent errors until detected, eroding trust in AI-driven decisions.
- Model Evasion: Adversaries introduce subtle input changes to enable malicious data to evade detection systems by exploiting the limitations of static rules and pattern-based security controls.
- Model Inversion: Systematic queries against AI models allow adversaries to extract confidential information, potentially exposing sensitive or proprietary training data and posing ongoing privacy risks.
- Prompt Injection: Attackers create inputs to deceive generative AI into bypassing safeguards, producing harmful or unauthorized outcomes.
- Dual-Use Frontier Risks: In a recent paper, Benchmark Early and Red Team Often: A Framework for Assessing and Managing Dual-Use Hazards of AI Foundation Models, researchers from The Center for Long-Term Cybersecurity at the University of California, Berkeley emphasize that advanced AI models lower barriers, enabling non-experts to carry out sophisticated cyberattacks, chemical threats, or other complex exploits, reshaping the global threat landscape and intensifying risk exposure.
Integrated Machine Learning Operations (MLOps) compound these risks, threats, and vulnerabilities. The interconnected nature of LLM and broader AI development pipelines amplifies these attack surfaces, necessitating enhancements in red teaming.
Cybersecurity leaders are increasingly adopting continuous adversarial testing to counter these evolving AI threats. Structured red-team exercises are now crucial, realistically simulating AI-focused attacks to uncover hidden vulnerabilities and address security gaps before attackers exploit them.
Strategies Employed by AI Leaders to Outpace Attackers with Red Teaming
Adversaries are rapidly advancing their use of AI to develop new forms of tradecraft that challenge existing cyber defenses. Their aim is to exploit emerging vulnerabilities as effectively as possible.
Industry leaders, including major AI firms, have responded by integrating systematic and sophisticated red-teaming strategies at the core of their AI security. Instead of viewing red teaming as a periodic check, they implement continuous adversarial testing by combining expert human insights, disciplined automation, and iterative human-in-the-middle evaluations to identify and mitigate threats proactively before exploitation.
Their rigorous methodologies enable them to pinpoint weaknesses and fortify their models against evolving real-world adversarial scenarios.
Specifically:
- Anthropic relies on rigorous human insight as part of its ongoing red-teaming methodology. By tightly integrating human-in-the-loop evaluations with automated adversarial attacks, the company proactively identifies vulnerabilities and continually enhances the reliability, accuracy, and interpretability of its models.
- Meta scales AI model security through automation-first adversarial testing. Its Multi-round Automatic Red-Teaming (MART) generates iterative adversarial prompts systematically, exposing hidden vulnerabilities and narrowing attack vectors across expansive AI deployments.
- Microsoft leverages interdisciplinary collaboration as the foundation of its red-teaming strategy. Using its Python Risk Identification Toolkit (PyRIT), Microsoft merges cybersecurity expertise and advanced analytics with disciplined human-in-the-middle validation, accelerating vulnerability detection and providing detailed, actionable intelligence to enhance model resilience.
- OpenAI harnesses global security expertise to bolster AI defenses at scale. By combining insights from external security specialists with automated adversarial assessments and rigorous human validation cycles, OpenAI proactively addresses sophisticated threats, particularly focusing on misinformation and prompt-injection vulnerabilities to maintain robust model performance.
In essence, AI leaders understand that staying ahead of attackers necessitates continuous and proactive vigilance. By embedding structured human oversight, disciplined automation, and iterative refinement into their red teaming strategies, these industry leaders establish the standard and define the playbook for resilient and trustworthy AI on a large scale.
As attacks on LLMs and AI models evolve rapidly, DevOps and DevSecOps teams must collaborate to address the challenge of enhancing AI security. Here are five impactful strategies security leaders can implement immediately:
- Integrate security early (Anthropic, OpenAI)
Embed adversarial testing into the initial model design and throughout the entire lifecycle. Identifying vulnerabilities early reduces risks, disruptions, and future costs.
- Deploy adaptive, real-time monitoring (Microsoft)
Static defenses are insufficient to protect AI systems from advanced threats. Utilize continuous AI-driven tools like CyberAlly to detect and respond to subtle anomalies swiftly, minimizing the window for exploitation.
- Balance automation with human judgment (Meta, Microsoft)
Pure automation overlooks nuances; manual testing alone is not scalable. Combine automated adversarial testing and vulnerability scans with expert human analysis to ensure precise, actionable insights.
- Regularly engage external red teams (OpenAI)
Internal teams develop blind spots. Periodic external assessments reveal hidden vulnerabilities, independently validate defenses, and drive continuous improvement.
- Maintain dynamic threat intelligence (Meta, Microsoft, OpenAI)
Attackers evolve tactics continuously. Integrate real-time threat intelligence, automated analysis, and expert insights continually to update and strengthen defenses proactively.
By implementing these strategies collectively, DevOps workflows can remain resilient and secure while staying ahead of evolving adversarial threats.
The Vital Role of Red Teaming
AI threats have become too sophisticated and frequent to rely solely on traditional, reactive cybersecurity methods. Organizations must embed adversarial testing continuously and proactively at every stage of model development to stay ahead. By combining automation with human expertise and adapting defenses dynamically, leading AI providers demonstrate that robust security and innovation can coexist.
Ultimately, red teaming is not just about safeguarding AI models. It is about ensuring trust, resilience, and confidence in a future shaped increasingly by AI.
Join me at Transform 2025
I will be hosting two cybersecurity-focused roundtables at VentureBeat’s Transform 2025, taking place on June 24–25 at Fort Mason in San Francisco. Register to participate in the discussions.
One of my sessions will focus on red teaming, AI Red Teaming and Adversarial Testing, delving into strategies for testing and strengthening AI-driven cybersecurity solutions against sophisticated adversarial threats.
