From Lab to Launch:

Securing AI Where it Matters Most

Readable

Testable

Accountable

Securin’s radical transparency approach is transforming AI security.

AI isn’t just code—it’s a complex ecosystem. AI models can be jailbroken, agents create new attack surfaces, and interconnected AI systems can be chained together by attackers to amplify threats across the deployment stack. Meanwhile, AI regulations remain fragmented and uncertain. Securin delivers comprehensive AI security, addressing model risk, agent-to-agent risk, and policy risk, so organizations stay ahead of attacks and compliance requirements. Through deep research, AI red teaming, and AI workloads risk assessment, Securin ensures AI accountability, resilience, and governance. From AI nutrition-label transparency to inference-layer red-teaming, our approach makes AI systems accountable, delivering structured insights so CISOs, regulators and builders can trust what they’re deploying.

Gen AI Models

Don’t just deploy AI workloads — defend it. Securin’s AI Nutrition Labels expose risks, data leaks, and weak points so you can see your models like an attacker does and strengthen them under real-world pressure.

Model Context Protocol

Your GenAI system’s biggest risk isn’t what the model says. It’s what it’s allowed to do. Over-permissioned tools, unsanitized inputs, and weak controls in AI agents create real attack paths.

Policy

As AI threats grow, security can’t wait for regulations to catch up. Securin’s NAVIGATE framework gives organizations a proactive roadmap to stay ahead of attacks through securing and governing AI workloads.

Gen AI Models

The label every AI model needs.

Most AI models ship with no transparency, and no idea how they’ll behave under pressure. Securin’s AI Nutrition Labels show at a glance how your AI model measures up – and what it might expose. They’re a security report card for every AI model, from foundation models like GPT and Grok to in-house developed or fine-tuned models. Clear, easy-to-interpret summaries of how a given model performs under real-world adversarial pressure:

Risk Category Resilience
Data Privacy Practices
Governance & Transparency

These aren’t theoretical model scores, they’re real documented vulnerabilities that any organization can understand – and act on. Where do the metrics come from, and how did we bring it all together? Learn more about Securin’s approach, and what’s at stake.

MCP Servers

The exploit isn’t coming for your AI model. It’s coming through it.

Tomorrow’s AI breaches won’t just be rogue LLMs spouting unfiltered responses. They’ll come from legitimate tools at the Agent’s disposal, operating as intended, under the wrong assumptions. It’s not a flaw in the model, it’s a breach in the logic layer – and it’s already here. When AI agents interact, risk multiplies:

Unrestricted shell execution
Mutable tool descriptions - aka “rug pulls”
Excessive file / network permissions
Path traversal routes with no sanitization

A handful of over-permissioned, under-patched nodes can carry a disproportionate share of exposure. If you’re securing AI networks, this is where you need to look next. Because the danger is no longer just in what the model says, it’s in what it’s allowed to do. Find out where the real risks are hiding in live MCP servers

Policy: From AI regulation to AI readiness

AI adoption is moving faster than regulations with Executive Orders and policies outlining what should happen, but instead are fragmented and uncertain, leaving organizations exposed to risk. In the meantime attackers are taking the opportunity to run amok. Securin’s NAVIGATE framework helps organizations chart a secure, future-proof path before legislation catches up. Built by AI red teamers and policy analysts, NAVIGATE helps teams shift from passive guardrails to active testing in real-world conditions. It’s a first-of-its kind roadmap for secure, responsible AI implementation. Eight pillars. One roadmap. Built for the AI era. Without enforcement, it’s up to security teams to own AI security. Are you ready? We are.

Frequently Asked Questions

AI models generate outputs by predicting patterns from training data. They don’t follow fixed rules – any safety rules are layered on top, not baked in. Attackers exploit this by designing adversarial prompts to bypass this safety layer, extract training data, or force the model into states that were never tested during development. These risks apply whether you fine-tune Grok/ChatGPT/Claude, or build a custom LLM (large language model).

Resources

June 9, 2025

Introducing Securin’s “AI Nutrition Labels”

What happens when an AI model does something it shouldn’t? A well-crafted prompt – the conversational equivalent of malware - can trick AI assistants into ignoring security guardrails