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Real-time visibility, safety, and security for your GenAI-powered agents and applications
Proactively test GenAI models, agents, and applications before attackers or users do
Deploy generative AI applications and agents in a safe, secure, and scalable way with guardrails.
Proactively identify vulnerabilities through red teaming to produce safe, secure, and reliable models.
The EU has formally sanctioned key players behind Russia’s coordinated disinformation ecosystem. These campaigns, long monitored by ActiveFence, reveal a complex strategy built on narrative laundering, infrastructure resilience, and long-term influence.
AI misuse isn’t hypothetical – it’s happening now. This blog introduces ActiveFence’s latest guide for operationalizing AI safety and security with six real-world strategies to move from principle to protection.
Threat actors are exploiting GenAI in the wild. Learn why true AI security must extend beyond infrastructure to detect and prevent real-world misuse.
See how easily multiple GenAI models, from LLMs to speech-to-speech, were tricked into divulging malicious code and weapon design instructions.
A federal judge has ruled that AI-generated content is not protected under free speech laws, expanding legal exposure across the AI ecosystem. What does it mean for AI platforms, infrastructure providers, and the future of GenAI safety?
The Take It Down Act explained: all you need to know about this new federal law targeting AI-generated intimate content, to stay compliant and prepared.
ActiveFence is expanding its partnership with NVIDIA to bring real-time safety to a new generation of AI agents built with NVIDIA’s Enterprise AI Factory and NIM. Together, we now secure not just prompts and outputs, but full agentic workflows across enterprise environments.
Discover how ISIS’s media arm is analyzing and adapting advanced AI tools for propaganda, recruitment, and cyber tactics in a newly released guide detailing AI’s dual-use potential.
Explore why generative AI continues to reflect harmful stereotypes, the real-world risks of biased systems, and how teams can mitigate them with practical tools and strategies.