WonderFence for Runtime AI Oversight

As generative AI systems move from experimentation into real products, the hardest part isn’t getting them to work, it’s keeping them working safely once real users, real data, and real edge cases enter the picture.

In production, AI doesn’t always behave the way it did in testing as models update, prompts shift, and users behave in ways that teams can’t fully predict. Risks rarely show up as obvious failures. Instead, they emerge gradually through subtle changes in tone, accuracy, or policy alignment that only become visible after systems are already live. For teams responsible for AI-powered products, that creates a constant tension between moving fast and staying in control.

WonderFence was built for that moment. It’s the layer that operates where AI actually interacts with the world, providing continuous safety, security, and policy-aligned protection for production GenAI and agentic systems.

Why Production Is Where Risk Really Lives

In many organizations, AI governance, adapted from traditional software governance and compliance frameworks, has been designed for a world where systems changed slowly. Policies are written at the start of development, reviews happen at fixed intervals, and controls assume that models and users will behave consistently over time. 

In practice, that approach breaks down quickly as platform-native guardrails are often rigid, hard to tune, and prone to false positives. They struggle to reflect the nuance of different products, industries, and risk tolerances.

Once a system is live, even small changes can have outsized impact when a new feature introduces an unexpected interaction pattern, a fine-tuned model responds differently to the same prompt, or a global audience brings cultural and linguistic complexity that static controls were never designed to handle. Without real-time visibility, teams are left reacting after users or regulators surface issues.

WonderFence addresses that gap by monitoring AI behavior as it happens. It detects safety, security, and policy risks in under 150 milliseconds, allowing teams to respond without degrading performance or user experience.

Adaptive Guardrails That Reflect Real Policies

At the core of WonderFence is a policy-driven approach to protection, designed to reflect how organizations actually operate. Teams define their own policies based on internal standards, regulatory obligations, and how their products are used, and those policies remain the foundation for enforcement. Rather than relying on one-size-fits-all rules, WonderFence applies protections in a way that stays aligned with each organization’s requirements while supporting consistent, predictable behavior in production.

Those policies are then supported by adaptive tuning that helps them stay effective as systems evolve in real time. As models change and real-world usage shifts, WonderFence adjusts detection and enforcement to reduce false positives and maintain accuracy, without changing the underlying policy intent. The result is stronger protection and a more stable user experience, even as AI behavior and risk conditions change.

This flexibility matters for teams operating across multiple models and vendors. WonderFence centralizes guardrail performance metrics into a single observability layer, giving teams clear visibility into how policies are applied and enforced across live systems. Instead of piecing together signals from different tools, teams can track consistency, identify gaps, and generate evidence that shows safeguards are working as intended. As deployments scale, this shared view makes it easier to support audits, meet reporting requirements, and demonstrate ongoing compliance without slowing teams down.

Built For Speed Without Sacrificing Experience

One of the biggest fears around runtime protection is latency. If safeguards slow systems down or interrupt users unnecessarily, they quickly become a blocker rather than an enabler. WonderFence was designed to avoid that tradeoff with ultra-low latency enforcement and automated response workflows that enable teams to apply actions like safe replies, session rules, or multi-strike warnings without additional engineering overhead. These workflows operate quietly in the background, preserving the experience users expect while ensuring interventions are consistent and predictable .

For product leaders, that means fewer surprises in production. For engineers, it means clear signals about where behavior is going off track. For security and compliance teams, it means confidence that policies are actually being enforced in real time, not just documented.

Coverage That Matches How AI Is Used Today

Modern AI systems are rarely text-only, and they’re almost never monolingual. WonderFence supports text, image, audio, and video inputs across more than 20 languages, with culturally nuanced detection that reflects how people actually interact with AI around the world.

This multimodal, multilingual coverage is critical for global deployments where risk can surface differently depending on context. It allows teams to identify issues that might otherwise remain invisible until they cause real harm or reputational damage.

Powered By Adversarial Intelligence

WonderFence is built on Rabbit Hole, Alice’s adversarial intelligence engine informed by years of global trust, safety, and security research. That foundation brings a deeper understanding of how systems fail in practice, not just in theory.

Instead of relying on generic rules, WonderFence benefits from continuous exposure to emerging misuse patterns, deceptive behaviors, and novel attack vectors. Ongoing expert adjustments help improve detection accuracy and reduce noise, strengthening reliability compared to default guardrails.

A Critical Piece Of The WonderSuite 

Within WonderSuite, WonderFence plays a distinct role. While WonderBuild helps teams uncover vulnerabilities before launch and WonderCheck evaluates drift and regressions in production, WonderFence is the always-on layer that protects systems in real time.

Together, they turn governance from a set of disconnected activities into a living system. Policies defined once can be applied consistently across testing, evaluation, and runtime protection. Insights from production feed back into improvement cycles, helping teams stay ahead of evolving risk instead of chasing it.

Operating AI With Confidence

As AI systems become more autonomous and more embedded in our everyday, the cost of losing control only grows. Organizations launching GenAI apps and agents need safeguards that move at the same speed as their innovation without overwhelming teams with noise that makes every signal look urgent. Because excessive alerts and false positives slow decision-making, erode trust in guardrails, and increase overhead —  pulling teams away from the issues that actually matter.

WonderFence is here to make that possible. By combining real-time detection, adaptive policy enforcement, live observability, and seamless integration into production environments, it helps teams operate AI systems responsibly and predictably at scale.

AI continuously changes. The question is whether the systems that protect it can keep up. WonderFence is built to do exactly that, so teams can advance unafraid.

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