Why You Should Switch to Klave AI

AI Is More Than Inference. Your Security Should Be Too. 

The future isn't just about bigger models. It's about secure, hybrid, and agentic workflows. Here’s how to secure them. 

The AI Dilemma Most Aren't Talking About 

The current AI revolution is dominated by centralised, all-knowing, closed source large language models (LLMs) from a few Big Tech players. For enterprises, this creates an immediate, fundamental conflict. You cannot use your most sensitive customer data, financial records, or proprietary IP with these models. The risks of data leakage, loss of sovereignty, and lack of traceability are simply too high. 

As a first line of defence, Confidential Inference emerged. This technique, which involves running models in secure enclaves, is a good start. It protects a user's query and the model's weights during computation. 

However, this is no longer enough. AI is evolving beyond simple prompts. The real vulnerability lies in the new, complex AI workflow. Modern AI uses Agents, RAG pipelines, and MCP (Model Context Protocol) to access your private documents, databases, and APIs. This entire workflow exposes your sensitive data at every step. 

Simultaneously, the market is moving to Small Language Models (SLMs) and local inference, whether on premises or in a private cloud. This is a critical step forward, enabling better data sovereignty and control. Despite this, "local" alone is not a complete security solution. It doesn't inherently protect your model's IP, its weights, or your data in use from a privileged attacker. 

Protecting modern AI requires a new playbook. You must secure both the inference step, whether it's on a large model running on the cloud or your own local SLM, and the entire agentic workflow. 

The New AI Attack Surface: Why your sensitive data is at risk 

When we talk about an "AI workflow," we aren't just talking about a prompt and a response. The modern AI stack is a complex, multi-step process: 

• AI Agents: These are the actors. They are autonomous systems designed to pursue goals. 
• RAG (Retrieval-Augmented Generation): This is the agent's knowledge. It is the pipeline that retrieves relevant information from your private documents and databases to "ground" the AI. 
• MCP (Model Context Protocol): This is the connective tissue. It is the standard agents use to access all these resources: databases, APIs, and file systems. 

At every step of this process, your sensitive data is exposed. Every interaction exposes data in memory, in transit, or at rest. This includes: 

• Exposed Credentials: Agents need API keys and database credentials to function. Where are those stored, and who can see them? 
• Exposed Data: RAG pipelines retrieve and process your most sensitive private documents, exposing that content to the agent and the MCP server. 
• Exposed IP: Your proprietary prompts and the unique combination of data you retrieve are your "secret sauce," and they are left vulnerable. 

This is the painful trade-off enterprises face: choose sharp, context-aware intelligence by giving AI everything, or choose privacy by starving it of data. It's a false and dangerous choice. 

The Market Correction: The Rise of Small, Powerful, and Private AI 

The "AI bubble", propped up by giant, power hungry, and costly LLMs running in a few centralised clouds, is showing its limits. The future of AI isn't just about bigger models. 

The real revolution is the move toward a sustainable, secure, and hybrid architecture. This is driven by two trends: powerful Small Language Models (SLMs) and capable Hardware (AI ready purpose-built hardware: Apple silicon, NVIDIA DGX Spark, etc.). 

For the enterprise, this is a massive win, delivering flexibility while staying secure. It unlocks the ability to design hybrid AI workflows that span both cloud resources and on-prem infrastructure. Moving sensitive workloads to on-premises or private cloud instances is the critical first step toward solving the data sovereignty and handling crisis. It makes security approachable by giving you back control over where your critical data goes, while retaining the flexibility to use public models for non-sensitive tasks. 

This new model is Hybrid AI: running sensitive workloads and proprietary models locally, while retaining the option to use powerful cloud models for less-sensitive tasks. 

The Missing Piece: On-prem is Not Confidential 

Running workloads on-premise solves the "where" but not the "what." While local inference is a necessary first step toward data sovereignty, it isn't a complete security solution. It only raises a new set of crucial, unanswered challenges: 

• IP Protection: Your proprietary model weights and algorithm IP remain vulnerable to inspection and theft, even on your own private infrastructure. 
• Run-Time Data Exposure: Sensitive data is exposed in memory during use, unprotected from a malicious privileged administrator or a compromised host system. 
• Auditing and Trust: You lack a cryptographic method to verify and audit exactly what the AI workflow is doing, creating security blind spots. 

The Klave Blueprint: Securing the Entire AI Workflow 

Klave AI is the platform that completes the modern hybrid strategy, transforming any AI workflows into provable confidential security. 

Klave is built to solve for this exact problem. It secures your entire AI stack, from agent, to RAG, to inference, whether your models are third party or your own proprietary SLMs running on prem. 

Our critical differentiator is that Klave secures both inference and the agentic workflow. 

• For inference: It allows you to run your own models within secure enclaves (Intel SGX & TDX, AMD SEV, NVIDIA), protecting your model IP (weights) and your users' queries even on your own hardware. 
• For agentic workflows: It goes beyond inference to run both the agents (MCP clients) and the MCP servers inside additional secure enclaves. 

This isn't just a "Trust me Bro!" moment, it's a provable fact. Klave's platform uses a built-in attestation workflow leveraging Remote Attested TLS (RA-TLS). This cryptographically proves that your system, its code and environment are secure and untampered with before a single byte of your data is processed. This isn't just a black box hoping to be secure; it's a verifiable, sealed environment that gives you a cryptographic receipt of its integrity. 

This means your data, prompts, and credentials never leave this protected, attested hardware. 

Let's make this tangible with a Before/After scenario in financial services: 

Imagine an AI agent needs to access a customer's portfolio data from a database and a proprietary market analysis PDF to generate a personalized recommendation.  

Before Klave, the entire workflow is a security liability: the sensitive RAG-retrieved documents and data, and the original user query are all processed in the clear in local memory. This means the data is instantly vulnerable to an insider threat (like a privileged administrator) or an external attacker who compromises the host machine. Furthermore, if the agent uses a public LLM, this sensitive data is transmitted and processed in the clear on a third-party server, resulting in a complete loss of data confidentiality and sovereignty.  

With Klave, the entire process, from agent workflow orchestration to data retrieval and final inference, is executed in attested secure enclaves. This sealed, cryptographically proven environment ensures the integrity and confidentiality of your data even against privileged actor (including cloud provider), while it's being actively used. This is the critical difference between hoping your security holds and cryptographically proving it does at every step of the workflow. 

The Three Pillars of a Truly Confidential AI Strategy 

Klave’s approach provides the new standard for enterprise AI, built on three pillars: 

1. Absolute Confidentiality 

Your intellectual property, customer data, and credentials are never exposed. By running the entire agentic workflow in secure enclaves, your data is encrypted end to end: at rest, in transit, and most importantly, in use. This confidentiality is continuously proven by attestation. 

2. End to End Auditability 

For any CISO, this is non-negotiable. Because the entire workflow runs in an attested enclave, every agent action becomes verifiable, private, and auditable by design. This tamper proof governance is critical for compliance and traceability. You get a cryptographic log of exactly what data your agent accessed and why, eliminating security blind spots. 

3. Unmatched Flexibility and Integration 

Klave supports the hybrid future: "run any model, whether open source or private, large or small" and meets you where you are.  

This flexibility extends to your adoption path: 

• Turnkey Product: You can use Klave AI as a productised, out of the box solution for Confidential Inference and Private RAG. 
• Developer Building Blocks: Your technical teams can leverage Klave's core building blocks, including SDKs and APIs, to create fully custom, secure AI agents, MCP servers, and entire confidential workflows. 

Whether you need a ready-made solution or a powerful platform to build on, the core security guarantees remain. This secure by design architecture simplifies your security stack, letting your developers focus on building, not on complex cryptography. 

Stop Leaking, Start Building 

The "AI bubble" of cloud based, closed-source, centralised models is a massive risk. The future is hybrid, agentic, and distributed. 

You cannot build this future on a broken security model. "Confidential Inference" alone is not enough, and "local inference" alone is not fully secure. You must secure the entire workflow: the agents, the RAG pipelines, the MCP servers, and the data, at its core. 

Klave AI is the essential platform built for this new reality. It’s the only way to adopt advanced AI without the painful trade-off between performance and privacy. Your data stays confidential, your models stay secure, and your insights stay yours. 

Ready to build AI you can actually trust? See how Klave AI protects your entire AI workflow. 

Learn more about Klave's Private RAG by reading our documentation.