As organizations scale their use of AI agents, they quickly run into a foundational problem: how do you ensure these agents operate safely without stifling their ability to solve complex tasks?
Static rules and keyword searches are no longer sufficient. To truly govern an AI agent, you need to understand the intent behind its actions. You need AI to help you govern AI and the best way to do that is with an LLM acting as a judge, enforcing semantic policies in real-time.
While the concept of an LLM judge sounds straightforward, making it work in production is incredibly difficult. Here’s why it’s so hard, and how Rubrik Agent Cloud powered by our Semantic Governance AI Engine (SAGE) cracks the code.
Why Governing AI Agents with LLMs is Hard in Practice
Using a generalized LLM to monitor your AI agents introduces two massive friction points that break enterprise workflows:
Slow, Costly Inference: General-purpose LLMs are massive and resource-intensive. Waiting several seconds for them to evaluate every prompt and response is impractical. When an agent runs hundreds of actions a second, these delays quickly compound into minutes, severely stalling workflows. Additionally, relying on a frontier model like GPT 5.6+ or Claude Opus 5 doubles inference costs, as every token is processed twice—first as output, then through a verifier—using an expensive heavyweight model.
Trust Requires Extreme Accuracy: Generalized models are good, but good isn't enough for domain-specific security and governance. In the world of enterprise AI security, even a 2–3% improvement in accuracy is the difference between catching a critical data leak and letting one slip through. You need a model that is an absolute expert in your specific policy boundaries and understands the context of your data, identities and agents.
Getting to Real-time AI Governance
To build an AI enforcement system that is both lightning fast and highly accurate, Rubrik acquired Predibase, a leader in AI infrastructure. Combining Predibase’s deep expertise in training and serving Small Language Models (SLMs) with Rubrik’s domain knowledge in security, we built Rubrik Agent Cloud, and at its core SAGE (Semantic AI Governance Engine), to overcome the limitations of traditional LLM-as-a-judge workflows.
We uniquely engineered SAGE to provide real-time, intelligent governance with these core design principles:
1. Real-time Enforcement: To solve the latency and cost problem, we drew on our pedigree of building state-of-the-art inference stacks. The Predibase team created open-source LoRAX, a low latency framework for LLM inference, and pioneered numerous other innovations that fundamentally changed how to efficiently serve AI models at scale.
While SAGE is a unique architecture, we leverage the same deep engineering principles to operate at a fraction of the latency of traditional inference engines. Specifically, our engine's configuration is meticulously optimized for high throughput, employing a number of critical innovations under the hood:
High-Throughput Batching: SAGE is designed to handle a massive volume of concurrent inputs seamlessly. Rather than slowing down under the weight of simultaneous requests, like sending 100 queries at once to a generalized API like ChatGPT, our batching process maintains blazing fast performance at enterprise scale.
Prefix Caching: We eliminate redundant compute using prefix caching and prefix-caching aware routing to replicas. This involves finding ways to maximize prefix hits across policies, being smart about how prefill and decode batches intermingle during inference, and being thoughtful about how we auto-scale our replicas to handle cache aware routing with load balancing. Together, this means we can deliver over a P50 of < 100ms and P99 < 1s even at very high RPS, making inline blocking (runtime governance) with SLMs non-disruptive to most day to day workflows.
Custom Kernels & Memory Management: We utilize state-of-the-art GPU kernels and highly optimized memory management techniques at both the model layer and the KV cache layer to squeeze every ounce of performance out of our hardware, to help prevent bottlenecks.
By optimizing how our governance models are served, SAGE ensures your agents process policy checks in real-time without the compute bloat.
2. Uncompromising Accuracy: SAGE is Context-Aware and Built for Governance
When it comes to accuracy, the key differentiator for SAGE is context. A standard LLM-as-a-judge evaluates prompts and responses in a vacuum. SAGE, however, is more than just a model. SAGE understands your full business context to achieve higher levels of accuracy by evaluating the complete picture:
User-Intent & Session Dynamics: SAGE evaluates true intent behind user interactions throughout multi-turn agent sessions. It continuously validates that an agent's actions remain strictly aligned with the user's authorized goals to help prevent rogue behaviors and malicious actions.
Identity & Risk Posture: SAGE sees the human behind the agent. It evaluates the user's role, whether their identity has been compromised previously, and if their credentials were recently rotated or have been in use for months.
Data Sensitivity: SAGE understands the data your agent is trying to access. It knows if the data is sensitive or proprietary, if it is necessary for the task at hand, if it has been manipulated, and whether it is securely backed up.
Behavioral History: By analyzing actions in the current session and past sessions, SAGE can instantly detect if the current behavior is anomalous.
Organizational Principles: SAGE maps all of the above telemetry against your customized corporate guidelines and specific business guardrails.
3. Operationalizing Context with Intelligent Policy Design and SLMs: Context serves as the foundation for understanding. SAGE then relies on domain-specific Small Language Models (SLMs) to enforce your guardrails with that contextual understanding.
When it comes to model accuracy, our team literally wrote the paper on customizing small language models (see our research that demonstrates how specialized SLMs outperform best-of-breed LLMs). For SAGE, we leverage domain-specific models fine-tuned for governance and policy enforcement, trained through reinforcement learning. This approach delivers high levels of accuracy with a smaller model footprint.
However, a great model is only half the equation. We found that the actual policy definition matters most for enforcement. In an AI-governed system, your policy is the prompt: it tells the model exactly what it needs to do and how to operate.
To maximize accuracy, SAGE utilizes an intelligent workflow called Adaptive Policy Improvement. Instead of relying on static, vague instructions, this powerful prompt optimization process codifies your policy and iterates with you in real-time to reduce ambiguity. It works by:
Clearly defining expected agent behaviors
Providing specific examples of violations and approved actions
Proactively identifying ambiguous guardrails and suggesting refinements to administrators before a violation occurs
Enabling continual improvement through the ingestion of real-world violation examples, as well as feedback provided by you on any false positives
When you pair deep data, identity and agent context with a highly optimized policy and our fine-tuned SLMs, SAGE catches the nuances that generalized LLMs completely miss.
SAGE maximizes recall and precision, simultaneously minimizing false positives and false negatives. Through intelligent routing and escalation workflows across the enforcement engine, SAGE scans broadly in real-time while allocating compute efficiently to verify high-stakes blocking actions in the critical path of everyday work.
The Results: Head-to-Head Policy Enforcement Benchmarks
To validate the efficiency of Rubrik Agent Cloud, we put our governance engine to the test. In a head-to-head comparative analysis against OpenAI’s leading model (GPT-5.2) using a standardized set of user-agent interactions, SAGE delivered decisive results:
10-30x Faster Processing: SAGE processed messages at a fraction of the latency, eliminating the bottleneck of real-time AI monitoring.
Higher Accuracy: SAGE detected policy violations correctly more often than GPT-5.2, proving that domain-specific models paired with optimized policy definitions beat generalized knowledge.
Reduced Overhead: SAGE significantly reduced the compute overhead typically associated with running an AI judge in real-time.
The Future of Agentic Security is Fast, Accurate, and Actionable
The promise of autonomous AI is massive, but so is the risk of deploying them without proper guardrails. Until now, organizations have been forced to choose between keeping their data secure and keeping their agentic workflows fast.
With Rubrik Agent Cloud and SAGE, that trade-off is obsolete. By combining the blazing speed of a specialized inference architecture with the surgical precision of a SLM tailored for governance, we’ve removed the friction from AI security. No slow downs and no ballooning costs—just highly accurate semantic enforcement.
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