Intelligence and control for the AI context layer. A layered architecture that classifies, tokenizes, and governs sensitive data as it flows through enterprise AI systems.
In traditional systems, data lived in structured environments: databases, applications, APIs. Security controls were designed around those boundaries. Access was applied to tables, files, or services.
AI systems operate differently.
Modern AI workflows assemble context dynamically. A single inference request may combine information from structured databases, PDFs, knowledge bases, SaaS systems, and external tools before sending that context to a model for reasoning.
Once data becomes part of that context, the boundaries that traditional security relied on disappear. Sensitive information can appear anywhere inside the prompt, the retrieved documents, or the generated response.
Protecto was built to operate at this moment. It sits between enterprise data and AI reasoning systems, providing intelligence and control over how sensitive information flows through AI applications.
Protecto is designed as a layered platform that analyzes data, understands context, and applies policy during AI workflows. Each layer contributes a specific capability that allows AI systems to safely operate on enterprise information.
Enterprise data rarely exists in clean, structured formats. Sensitive information appears across documents, PDFs, emails, scanned records, and conversational text.
Protecto uses AI-driven classification to detect sensitive entities across both structured and unstructured content. Instead of relying on pattern matching, the classification engine analyzes the semantic meaning of data to accurately identify sensitive information.
The system identifies categories including:
This matters because sensitive meaning is context-dependent. Protecto’s classification engine handles the hard cases: sensitive meaning spread across sentences, expressed in mixed languages, and embedded in inconsistent formatting or typos.
The result is a persistent, structured understanding of where sensitive data exists within enterprise content.
Sensitive meaning rarely appears as a single field, and cleanly formatted. User prompts are often expressed in mixed languages, inconsistent formatting or typos, making simple pattern detection or NLP based approaches insufficient.
Sensitive values must often be protected without removing them entirely from the data. This is where traditional approaches break down.
Conventional masking replaces values with random characters or generic placeholders. This works for storage-level protection, but it destroys the semantic structure that language models depend on. Masked data produces degraded reasoning, broken retrieval, and incoherent responses.
Protecto uses semantic tokenization: sensitive values are replaced with structured tokens that preserve the type, position, and referential relationships of the original data.
The AI system continues to reason over the document. It understands that same person who used credit card is requesting refund. Summarization, extraction, and question-answering continue to work. But the underlying sensitive values are never exposed to the model.
Tokenization services operate at high throughput and maintain referential integrity across documents and datasets.
Traditional masking breaks AI reasoning because entity relationships disappear. Tokenization must preserve semantic structure and referential integrity across entire documents while still protecting the underlying sensitive values. Getting this wrong degrades reasoning quality and model output.
Tokenized values are mapped to original sensitive data within a secure vault. This creates a strict architectural separation between the AI processing layer and the sensitive data storage layer.
Sensitive values are encrypted at rest with enterprise-grade key management.
Every token access event is logged for compliance, forensics, and traceability.
AI systems operate on tokenized data. Sensitive values never enter the inference path.
Token-to-value mapping is governed by access policies. Only authorized workflows can restore original values.
This architecture means that even if a model, prompt, or response is logged, intercepted, or cached, the underlying regulated data is not present in those artifacts.
Tokens must be reversible for authorized workflows while remaining cryptographically secure and fully isolated from AI processing pipelines. The vault must support strict 'acid' properties while handling large volume data.
Protecto integrates with enterprise identity systems including Active Directory and modern IAM platforms. These integrations allow the system to understand who is interacting with an AI system and what permissions they hold.
Traditional Role-Based Access Control (RBAC) governs access to systems and datasets. A user either has access to a table or they don’t.
But AI systems create a new problem. A model that has access to a knowledge base can generate responses that combine information from multiple sources. The output might contain data elements that the requesting user should not see, even though the model itself was authorized to access the source material.
Protecto introduces an additional layer of control that operates on the information contained inside AI-generated responses, not just on the systems that produced them.
AI systems can generate responses that combine multiple data sources, meaning access control must operate not only on datasets but also on the information contained inside generated responses. Traditional RBAC was never designed for this.
Identifying sensitive data is necessary but not sufficient. The same data element may be appropriate to surface in one context and restricted in another. Enterprise confidential information such as discount details and intellectual property are spread over many parts of the document
The Context Intelligence layer evaluates multiple signals during each AI interaction:
These signals are combined to determine the exposure risk associated with each request. A request to summarize a document may be appropriate for one user and restricted for another.
Rather than relying on static access rules, Protecto evaluates each interaction dynamically, allowing AI systems to operate with the correct level of visibility per user.
The correct exposure decision depends on multiple signals at once: the prompt intent, the data sources used, the identity of the user, and enterprise policy. All of these must be evaluated dynamically during each AI interaction, not preconfigured in a static rule table.
Protecto evaluates each AI interaction through a policy decision engine called the CBAC agent. This is the runtime enforcement layer: where classification, context, identity, and policy converge into an access decision.
The CBAC agent analyzes several factors simultaneously:
User Identity
Prompt Intent
Data Sensitivity
Policy Rules
Based on this analysis, Protecto determines what information should be visible to the requesting user. Two users asking the same question may receive responses with different levels of detail depending on their role and the sensitivity of the data involved.
This decision process occurs in real time during AI inference. There is no batch review, no manual approval queue. The CBAC agent evaluates and enforces policy inline, allowing enterprises to deploy AI assistants and agents without exposing restricted information.
Security decisions must be made in real time during AI inference while simultaneously evaluating identity, intent, data sensitivity, and policy rules. The controls should be intelligent to adapt to new context, data, and requests.
Many enterprise AI applications rely on retrieval-augmented generation (RAG) to answer questions using internal knowledge bases. This is where risk concentrates: the retrieval step pulls documents from storage, and the generation step surfaces their contents to end users.
Protecto includes GPTGuard, a secure RAG layer that protects sensitive information across this entire pipeline.
Documents retrieved from vector databases are evaluated for sensitive content before they enter the generation context.
Tokenized data maintains semantic meaning so model reasoning quality is preserved even on protected content.
Generated responses are filtered according to user permissions before delivery, ensuring restricted data is never surfaced.
This allows organizations to build AI assistants that operate on internal knowledge without exposing confidential information embedded inside documents.
Retrieval systems may pull documents containing mixed-sensitivity information. The platform must selectively protect sensitive segments within those documents without degrading retrieval accuracy or model reasoning quality.
Protecto is designed to operate at enterprise scale. The platform provides services that support high-volume AI workloads while maintaining strict security guarantees.
Strict data isolation between tenants with independent policy configurations, encryption keys, and audit trails.
Real-time inference APIs designed for low-latency inline processing during AI workflows.
Batch document classification and tokenization for large-scale data preparation across enterprise content stores.
Comprehensive logging for every classification, tokenization, and policy decision for regulatory traceability.
Deployment environments include:
The platform processes millions of documents and AI interactions per day while maintaining consistent security guarantees across all deployment models.
The system must enforce classification, tokenization, and policy decisions across millions of documents and real-time AI interactions simultaneously, while maintaining low latency, strict tenant isolation, and consistent security guarantees across every deployment model.
AI systems are becoming the primary interface to enterprise knowledge. As models gain access to more context, the risk of exposing sensitive information increases proportionally.
Protecto introduces intelligence and control at the point where enterprise data becomes AI context.
By combining classification, contextual understanding, tokenization, and policy enforcement into a unified runtime architecture, the platform enables organizations to deploy AI systems that reason over enterprise information while keeping sensitive data protected.
AI operates with the context it needs. Enterprises maintain the safeguards required to operate responsibly.