Build secure, scalable agentic AI workflows with Rubrik Annapurna and Pinecone

The future of enterprise AI is agentic. We're moving beyond basic prompt-response chatbots. Enterprises are now building autonomous, multi-step AI workflows—powered by agents that retrieve data, reason across systems, and take action across SaaS apps and internal databases.

But that kind of technical sophistication introduces complexity. GenAI, including agents and RAG, requires real-time, secure enterprise data access without sacrificing performance or compliance. It must retrieve and process vast amounts of proprietary information, perform rapid similarity searches, and operate at scale while honoring strict access controls and governance policies.

In short, the success of agentic AI for production workloads hinges on a secure, performant foundation.

That’s why Rubrik and Pinecone partnered to build it. Rubrik Annapurna, powered by Pinecone’s vector database, provides the infrastructure to deploy generative AI at scale, with RAG and agentic workloads in mind.

Here’s what makes it work and why it matters.

Why security is foundational to agentic AI

AI agents aren’t chatbots. They work asynchronously, operate across multiple systems, and often make decisions without human input. Moreover, they depend on real-time access to context-rich data across diverse sources to do their jobs.

Your infrastructure needs to support that autonomy. Specifically, it must:

1. Ingest and index dynamic data at scale.
2. Retrieve semantically relevant information instantly.
3. Enforce strict security and compliance at every step.

Most homegrown solutions break here. They create latency issues, duplicate data, or open up compliance risks. Traditional data lakes are too static, and legacy search isn’t built for vector workloads.

That’s precisely the gap Pinecone and Rubrik Annapurna were designed to fill.

A purpose-built stack for secure RAG and agents

Rubrik Annapurna runs on top of Rubrik Security Cloud, an enterprise-grade platform for data protection and governance. It gives AI systems real-time, secure access to sensitive enterprise data without custom ETL, duplication, or shadow pipelines.

That pipeline connects directly to Pinecone, an AI-native vector database built for low-latency, high-scale semantic search. It retrieves relevant data from billions of vectors in milliseconds.

Annapurna and Pinecone form the foundation for secure, scalable agentic workflows that are ready for production.

Credit: Rubrik

Embedding workflows: Controlled pipelines for intelligent retrieval

One of Annapurna’s most powerful features is its embedding workflow, which is designed to securely transform enterprise data into AI-ready vectors tailored to specific use cases.

Within Rubrik Security Cloud, users define:

• Which data sources to include (SaaS, cloud, on-prem)
• What sensitive data should be allowed or excluded
• Who can access the resulting embeddings

Once set, Annapurna handles ingestion, parsing, normalization, and security. It then generates embeddings using models like OpenAI’s text-embedding-3 and others. Those embeddings are stored in Pinecone, isolated by namespace for security and performance.

No brittle ETL. No manual filtering. No bolt-on security layers.

Annapurna automates the complete transformation and access control pipeline, ensuring agentic apps get the secure, up-to-date, and context-rich data they need.

Pinecone: The vector database fueling secure, agentic AI systems

Behind the scenes, Pinecone powers the semantic search infrastructure that makes Annapurna’s output actionable. Its architecture was designed from the ground up for AI workloads, including those driven by agents.

Built specifically for AI workloads, Pinecone delivers:

• Millisecond-latency vector search at high throughput
• Built-in freshness—new embeddings are indexed and searchable within milliseconds
• A cloud-native architecture that separates storage and compute, enabling effortless scaling
• Enterprise security features, including encryption at rest and in transit, SSO, RBAC, and private endpoints

Traditional databases can’t meet vector search performance, scale, or security demands. Pinecone can handle billions of vectors and thousands of concurrent queries while enforcing strict data boundaries.

Embeddings generated via Rubrik Annapurna can be stored, queried, and refreshed in real time, fueling AI agents with the performance and context they need to act knowledgeably.

Why it matters

This integration raises the bar for secure, production-grade AI infrastructure. Instead of stitching together custom workflows, teams get a platform that handles:

• End-to-end data security and governance
• Embedding generation with real-time updates
• Vector storage and high-speed retrieval for production workloads at scale
• Fine-grained access control

Whether you’re building a support agent pulling from internal docs, a security assistant correlating alerts, or a healthcare AI retrieving clinical data without exposing PHI, this stack supports it.

And it’s secure by design.

Future-proofing your AI stack

Agentic systems are no longer experimental, they’re becoming the default. However, without the right foundation, even the best ideas get stuck at the prototype stage.

Pinecone and Rubrik Annapurna provide the infrastructure to go from concept to production securely, at scale, and without complexity. It’s a future-proof stack for real-world AI.

Download the whitepaper to dive deeper into the architecture, use cases, and security model behind the integration.