Protect your AI applications and agents from attacks, fakes, unauthorized access, and malicious data inputs.
Control your GenAI applications and agents and assure their alignment with their business purpose.
Proactively test GenAI models, agents, and applications before attackers or users do
The only real-time multi-language multimodality technology to ensure your brand safety and alignment with your GenAI applications.
Ensure your app is compliant with changing regulations around the world across industries.
Proactively identify vulnerabilities through red teaming to produce safe, secure, and reliable models.
Detect and prevent malicious prompts, misuse, and data leaks to ensure your conversational AI remains safe, compliant, and trustworthy.
Protect critical AI-powered applications from adversarial attacks, unauthorized access, and model exploitation across environments.
Provide enterprise-wide AI security and governance, enabling teams to innovate safely while meeting internal risk standards.
Safeguard user-facing AI products by blocking harmful content, preserving brand reputation, and maintaining policy compliance.
Secure autonomous agents against malicious instructions, data exfiltration, and regulatory violations across industries.
Ensure hosted AI services are protected from emerging threats, maintaining secure, reliable, and trusted deployments.
🚀 New Report: Essential AI Red Teaming for Product Teams →
Many companies are racing to integrate generative AI (GenAI) into their products, chatbots, copilots, virtual assistants, or recommendation engines. But when your AI application is public-facing and speaks on behalf of your brand, every response is a reflection of your reputation.
And unlike non-AI products, AI doesn’t just fail in predictable ways like bugs or crashes. An AI system might get jailbroken into producing harmful content, manipulated through a prompt injection to leak sensitive business or customer data, or pushed into giving unsafe or misleading advice.
For a customer-facing AI assistant, these failures aren’t just uncomfortable user experiences that risk low retention; they can damage brand trust, create compliance issues, or even become legal liabilities.
That’s where AI red teaming comes in. By stress-testing your system against real-world threats, you can uncover vulnerabilities before bad actors exploit them.
Think of it as two sides of the same coin:
For product teams developing GenAI systems, whether it’s a business app that leverages AI or a new version of an LLM, red teaming has become a must-have practice. It’s not enough to build powerful AI features; you need to embed safety from the start.
If you’re a product manager, you already live by the practices that shaped modern product development:
Each transformed how teams build software by embedding discipline into the process, not treating it as an afterthought.
AI red teaming should be next.
For product teams, red teaming becomes the safety and security practice that ensures your AI is resilient under real-world conditions. It’s the equivalent of stress-testing your roadmap against worst-case user behaviors, giving you the confidence that your launch won’t be derailed by adversarial use or unintended consequences.
By embedding red teaming into the same workflows you already trust, like sprint planning, CI/CD pipelines, and continuous feedback loops, you make safety and security continuous parts of building, not a one-time checkbox.
The most effective teams don’t treat red teaming as a one-off. They build it into their DNA. Just like agile transformed how teams iterate, and DevOps transformed how teams ship, red teaming transforms how teams responsibly scale AI.
The outcome isn’t just safer products. It’s stronger trust with users, smoother regulatory alignment, and fewer surprises after launch.
Without it, red teaming risks becoming a box-ticking exercise. With it, product teams can simulate how real adversaries operate, learn from those insights, and continuously harden their systems.
Our new report, Essential AI Red Teaming Tools and Techniques for Product Teams, breaks down:
This blog post only scratches the surface. The full report, Essential AI Red Teaming Tools and Techniques for Product Teams, provides the detailed tools, workflows, and frameworks you need to:
👉 [Download the guide here] to learn how to operationalize red teaming and build AI systems that are safe, resilient, and ready for the real world.
Don’t leave AI safety to chance.
Discover principles followed by the most effective red teaming frameworks.
Prompt injection, memory attacks, and encoded exploits are just the start. Discover the most common GenAI attack vectors and how red teaming helps stop them.
LLMs with RAG bring powerful personalization, but also new security risks. Explore how ActiveFence’s Red Team uncovered ways attackers can exfiltrate secrets from AI memory.