AI red teaming is a proactive security tactic which runs simulated attacks to expose hidden weaknesses in AI models and applications before they are deployed. Rather than just verifying if an AI model functions accurately, this approach intentionally attempts to manipulate the system to uncover vulnerabilities such as biased outputs, harmful content generation, or security breaches. Netskope One AI Red Teaming elevates this practice by replacing slow, manual testing with automated adversarial simulations. Using its library of over 18,000 distinct adversarial scenarios, Netskope systematically stress-tests private models to ensure they are safe and resilient before and after reaching production. AI red teaming differs from traditional red teaming because, while they both recreate adversarial tactics, they focus on different attack surfaces:

• Traditional red teaming concentrates on conventional IT infrastructure, probing networks, servers, and applications to expose gaps in standard technical defenses.
• AI red teaming focuses on the unpredictable behavior of the AI model itself. It probes for non-deterministic vulnerabilities, such as prompt injections, and jailbreak attempts.

Netskope One AI Red Teaming includes replication of sophisticated multi-turn attacks (such as "skeleton key" or "crescendo" attacks) that try to trick the model into bypassing its own safety guardrails or leaking sensitive training data. Netskope also integrates these automated stress tests directly into CI/CD pipelines, actively defending against model risks every time code is updated.

The AI attack landscape is rapidly evolving, with cybercriminals actively developing new exploitation techniques to target Large Language Models (LLMs) and agentic architectures. The most common AI attack vectors include:

• Prompt injections: Attackers use manipulative linguistic exploits to override an AI system's instructions and alter its intended behavior.
• Jailbreaks: These are attempts to circumvent built-in safety guardrails, forcing the AI model to ignore its own safety rules. These attacks can be highly effective, succeeding nearly 20% of the time, often taking less than a minute and only five or six interactions to crack standard safeguards.
• Indirect prompt injections: These occur when malicious prompts are secretly embedded within documents or websites; when the AI processes this external content, its behavior is manipulated.
• Data extraction attacks: Techniques designed to pull sensitive information and secrets directly from a model's underlying training data.
• Multi-turn attacks: Sophisticated, multi-stage conversational exploits, such as "skeleton key" and "crescendo" attacks, where adversaries attempt to trick LLMs by layering interactions to bypass safety guardrails that lack full session context.
• Tool poisoning: A threat specifically targeting autonomous, agentic AI, where an AI agent is manipulated or tricked into interacting with a malicious external tool.

Increasingly, yes. Major regulations now explicitly mandate or strongly encourage red teaming. The EU AI Act includes a requirement for adversarial testing for high-risk AI models. NIST's AI Risk Management Framework also recommends red teaming as a core part of securing AI systems.

When organizations build and host their own private AI applications, they take on the full responsibility for securing those models and complying with wider data security and protection regulations such as GDPR and HIMSS.

Yes, AI red teaming can definitely be automated. In fact, Netskope One AI Red Teaming is designed specifically to automate adversarial simulations, effectively replacing slow and unscalable manual testing.

It achieves this automation with a library of over 18,000 adversarial scenarios and seed prompts to systematically stress-test your private models against threats such as prompt injections and jailbreaks. You can seamlessly integrate these automated stress tests directly into your CI/CD pipelines via APIs, ensuring that every single code change or model update is automatically screened for vulnerabilities before it ever reaches production.

Red teaming significantly improves and accelerates secure AI development by automating the discovery of vulnerabilities and seamlessly embedding security directly into the development pipeline. Here is how it enhances the process:

• Speeds up innovation: By replacing slow, manual security reviews with automated adversarial testing, development teams can deploy AI features much faster without compromising on safety.
• Seamless CI/CD integration: Red teaming can be integrated directly into your CI/CD pipelines using APIs. This ensures that every single code change or model update is automatically screened for new security risks before it is ever released into a live production environment.
• Proactive model hardening: It empowers developers to simulate motivated attacker behaviors, such as complex multi-turn attacks, to actively try and "trick" the model into bypassing guardrails or leaking sensitive data. By finding and fixing these vulnerabilities before the model interacts with a customer or employee, teams avoid the costly process of patching security gaps after they are exposed to the world.
• Continuous risk tracking: It shifts model testing from passive observation to an active defense by running scheduled simulations that track how risks change across all tests on the same model. This ensures that rapid model updates never inadvertently introduce new security gaps or increase your risk profile.