A Radical Transparency Approach to AI Security
AI is already everywhere. It’s time we got serious about securing it.
Securin’s AI Risk Nutrition Labels
AI deployment is outpacing security visibility, and many leaders don’t even know what threats to look for, let alone how to test for them.
Anyone can buy, deploy or build on top of an AI model, without knowing anything about what’s going on under the hood. Many organizations building with AI today, or integrating it into their products don’t know:
If they’re vulnerable to prompt injection or encoding attacksHow their models behave under adversarial or multi-turn manipulationIf their models can be jailbrokenWhat any of the first three things on this list even mean…That's why Securin has introduced our "AI Risk Nutrition Labels" - the first structured security report card for AI models – including LLMs like ChatGPT, Grok, and in-house models.
What’s on the AI Label?
Securin’s radical approach to AI transparency gives users immediate insight into exactly what they can expect from their AI model, including:
Why Now?
Despite growing evidence of successful prompt injections, jailbreaking, and model manipulation, most organizations continue to deploy AI with little or no real understanding of the risks. Securin’s approach changes all that.
Food nutrition labels transformed consumer safety. Securin’s AI Risk Nutrition labels bring the same level of transparency to AI systems, so organizations of all kinds can make more informed decisions around deployment.
Security to Model: Securing Artificial Intelligence to Strengthen Cybersecurity
Kiran testified before Congress at an AI cybersecurity hearing, emphasizing the urgent need for proactive security frameworks as AI becomes central to cyber operations. Watch the full video.
Ready for more?
For a full break down of our methodology, data, and approach, start with our blogs!
Introducing Securin’s “AI Nutrition LabelsWhat happens when an AI model does something it shouldn’t? A well-crafted prompt – the conversational equivalent of malware...
