AI runtime guardrails

AI is unpredictable once it’s live. Your defenses shouldn’t stop when deployment does. Runtime guardrails enforce security on every LLM interaction — in real time, in production.

Challenges

A perfect prompt is not a guarantee

Traditional security assumes that if you write the rule correctly, it holds. LLM-powered applications don’t work that way and that’s exactly where the real risk begins.

Instructions aren’t deterministic

A system prompt that says “never do X” is a rule, not a lock. Attackers use conversational, social-engineering-style prompts to convince a model to override its own instructions in the same way someone might talk a person into breaking a rule they know they shouldn’t.

A single conversation can compromise the system

It only takes one successful jailbreak to trigger a data leak or an unsafe response. Static, pre-deployment scanning can’t catch a risk that only exists inside a live interaction.

Shift-left alone isn’t enough

Finding vulnerabilities before deployment is still essential, but pre-deployment was never designed to catch a model being talked out of its own rules in real time. Without a deterministic enforcement layer, that failure happens silently and no one catches it.

Opportunities

Real-time enforcement, built for how AI actually runs

A prompt can look airtight and still not hold under pressure. Runtime guardrails don’t hope it does, they inspect every interaction with your AI application and catch the moment it doesn’t, every time.

Inspect every interaction in real time

Every prompt is checked before it reaches your model, and every response is checked again before a user sees it. That’s two checkpoints, not one.

React the way the moment calls for

Alert your team, block the interaction outright, or automatically obfuscate sensitive data. Configure it per guardrail, and tighten enforcement as your confidence grows.

Stay protected as long as it’s live

Pre-deployment scans check your code once, before it ships. Guardrails check every interaction after that, for as long as the application runs.

Check your AI security posture

Measure your AI security program against industry frameworks including OWASP, NIST, ISO/IEC, and the EU AI Act. Identify gaps, prioritize improvements, and generate actionable recommendations.

The solution

Mend AI

Mend AI’s Runtime Guardrails inspect LLM inputs and outputs as they happen, enforcing security and safety policies for as long as your AI application is live — not just before it ships.

Prompt injection and jailbreak detection

PII protection with alert, block, or obfuscate

Harmful content filtering

Live dashboards and event logs

Integrates with the AI stack you already run

Discover Mend AI

Immediate insights MTTR Fast, secure, compliant

Immediate insights

“The biggest value we get out of Mend is the fast feedback loop, which enables our developers to respond rapidly to any vulnerability or license issues. When a vulnerability or a license is disregarded or blocked, and there is a policy violation, they get the feedback directly.”

Markus Leutner, DevOps Engineer for Cloud Solutions

MTTR

“One of our most indicative KPIs is the amount of time for us to remediate vulnerabilities and also the amount of time developers spend fixing vulnerabilities in our code base, which has reduced significantly. We’re talking about at least 80% reduction in time.”

Andrei Ungureanu, Security Architect

Fast, secure, compliant

“When the product you sell is an application you develop, your teams need to be fast, secure and compliant. These three factors often work in opposite directions. Mend provides the opportunity to align these often competing factors, providing Vonage with an advantage in a very competitive marketplace.”

Chris Wallace, Senior Security Architect

AI moves fast. Your security should too.

Take a tour

Recent resources

[AI Security Governance: A Practical Framework for Security and Development Teams](/content/resources/research-reports/ai-security-governance-framework/ "AI Security Governance: A Practical Framework for Security and Development Teams"/index.html)

AI Security Governance: A Practical Framework for Security and Development Teams

[A CISO’s Guide to Securing AI from the Start](/content/resources/white-papers/a-cisos-guide-to-securing-ai-from-the-start/ "A CISO’s Guide to Securing AI from the Start"/index.html)

A CISO’s Guide to Securing AI from the Start

[Runtime protection: Threats, technologies, and 5 best practices](/content/blog/runtime-protection/ "Runtime protection: Threats, technologies, and 5 best practices"/index.html)

Runtime protection: Threats, technologies, and 5 best practices

[What Is a Prompt Injection Attack? Types, Examples & Defenses](/content/blog/what-is-a-prompt-injection-attack-types-examples-defenses/ "What Is a Prompt Injection Attack? Types, Examples & Defenses"/index.html)

What Is a Prompt Injection Attack? Types, Examples & Defenses