We built BeyondGuard after watching a string of “Grok-style” incidents where public chatbots went off the rails – swearing at users, amplifying hateful content, and being steered into crazy directions with a single clever prompt. At the same time, every security team we spoke with was asking the same questions: “What if an LLM leaks our data? What if a prompt-injection silently bypasses our policies? How do we know if the model is being manipulated in production?” There was clearly a gap between the excitement around LLMs and the level of control and observability that security, privacy and compliance teams actually need. The problem we’re trying to solve is simple to describe but hard to execute: make it boringly safe to use powerful LLMs on real, sensitive data. BeyondGuard focuses on three things: (1) preventing sensitive data from leaving the organization in the first place, (2) enforcing security & compliance policies at runtime so the model can’t be tricked into ignoring them, and (3) giving teams full auditability so they can see when prompts, tools or external content are trying to manipulate the model. Our first prototype was just a thin PII-redaction layer in front of popular LLM APIs. As we started red-teaming our own stack (and letting friendly attackers break it), it quickly evolved into a full policy and monitoring engine: prompt-injection defenses, data-loss prevention, risk scoring, and controls mapped to frameworks like OWASP LLM Top 10 and MITRE ATLAS. We’re launching now because we think the conversation has to move from “Can we build something with LLMs?” to “Can we build it without creating the next headline-level leak or meltdown?” Curious to hear from you all: what scares you most about putting LLMs in front of real users – data exfiltration, brand risk from “rogue” outputs, compliance… or something else entirely?

BeyondGuard

AI security and governance platform, adopting generative AI

AI security and governance platform, adopting generative AI