Modern AI isn’t confined to training pipelines - it lives in dynamic, autonomous agents embedded in the systems that run your business. These systems aren’t running in isolated sandboxes - they’re embedded into decision loops, business workflows, and customer experiences.
But as these AI agents and workflows grow more powerful and proactive, the stakes get higher. If an AI agent can retrieve a document, it can cite it. If it can query a vector, it can trigger an action. And if it can access sensitive information, it can leak it—intentionally or not.
Traditional data architectures weren’t built to handle this level of dynamism or risk. They rely on fragmented systems - object stores, databases, vector engines, orchestration tools, etc. - each with its own access model. Stitching them together invites governance gaps, inconsistent enforcement, and latency that breaks real-time AI.
The Problem with Patchwork Permissions
In fragmented architectures, securing AI pipelines means managing policies in every system:
Access rules for files and objects
Column- and row-level filters for structured data
Separate ACLs for vector search and semantic retrieval
Custom checks for each application layer or microservice
This creates two major problems:
Risk of governance breach: If permissions aren’t consistently inherited across systems, agents and pipelines may retrieve or act on data they’re not authorized to access.
Latency in enforcement: Inference pipelines that recalculate access at every hop introduce delays - and AI pipelines demand real-time response.
This is where most architectures break. Agents stall. Policies drift. And data teams spend more time managing security glue code than building intelligent experiences.
The VAST Solution: Unified, Real-Time Governance
VAST takes a fundamentally different approach to security, one made possible because VAST is a single, unified platform for all your AI data, not a patchwork of separate tools.
By bringing storage, vector search, database, and compute together in one system, VAST delivers end-to-end governance natively. Permissions don’t get lost, lagged, or misapplied as data flows from raw ingestion to AI interaction.
From source to vector to response, VAST enforces a single, consistent security model across every stage of the pipeline. No stitching together identity policies. No coordinating access rules across disparate systems. Just one place to manage it all.
Here’s how it works:
Permissions are atomic across the stack: Whether data originates in S3, SharePoint, or elsewhere, a single access policy governs its use - persisting seamlessly across tables, vectors, and queries. VAST’s identity-aware policy engine enforces the same rules at every layer, ensuring consistent, end-to-end security without fragmentation or redefinition.
Fine-grained security is enforced through row- and column-level permissions, dynamic masking functions, and attribute-based access controls. The query engine passes end-user identity ensuring policies are applied per-user at runtime.
Vectors are governed as extensions of the source: Each embedding acts as metadata for the original data - bound by the same atomic permissions. Row-level filters and column masking apply in real time, ensuring RAG and semantic search operate within a unified, permission-aware security model.
Audit trails are first-class citizens: Every search, query, or serverless function is logged to a queryable table. This gives teams real-time visibility into how AI systems interact with data and makes proving compliance a matter of querying, not guessing.
This is what enables AI agents and applications to reason, retrieve, and act responsibly with no extra layers, no added latency, and no risk of policy drift. Just intelligent access to the right data, by design.
Real-Time Security for Real-Time AI
Autonomous agents and AI pipelines don’t wait. They ingest, infer, and act in milliseconds—so the security model must keep up.
Where traditional architectures delay enforcement through cross-system checks or post-hoc validation, VAST delivers instant, in-line security at every touchpoint. That’s because enforcement is built directly into the data platform: evaluated once, applied everywhere.
As agents traverse raw files, structured tables, and vector embeddings, VAST applies the same security policy across them all—without re-authentication, external lookups, or permission drift. This enables:
Sub-millisecond access decisions during RAG or semantic search
Consistent enforcement even as data transforms from source to vector to chunk
Latency-free policy enforcement that scales with AI pipelines, not against them
In agentic systems where actions depend on fresh, governed context, this kind of real-time, policy-aware architecture isn’t a luxury - it’s a requirement.
Built for the Age of Intelligent Applications
AI is transforming how enterprises build products, serve customers, and make decisions. But no matter how powerful the model or how autonomous the AI agent, trust is non-negotiable.
That’s why VAST’s unified platform is more than a performance story - it’s a governance architecture for the future of AI.
AI systems that see everything but only show what each user is allowed to
Chatbots that honor SharePoint permissions, down to the row
Search agents that retrieve only what’s permitted and nothing more
Recommendation engines that never surface off-limits content
Autonomous workflows that act with precision, not exposure
With VAST Data, security and governance are never an afterthought - it’s the backbone of real-time AI.
Want to build AI with governance you can trust? Join a live demo to see how VAST secures every step of the AI journey from raw data to vector to decision.
