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.
Discover Mend AI
“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.”
“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.”
“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.”
