Very Good Security (VGS) vs Trussed for AI Data Protection

The core difference: protecting values vs governing actions

VGS and Trussed are not interchangeable controls. They address related data protection concerns, but they operate at different points in an enterprise AI system.

VGS is best understood as a way to reduce exposure of sensitive values. Its architecture centers on vaulting, tokenization, aliases, and proxy-mediated redact or reveal flows. That pattern is useful when the primary risk is the propagation of raw sensitive data through applications, APIs, prompts, logs, or downstream systems.

Trussed is positioned as a runtime governance and security layer for enterprise AI agents. Its focus is policy enforcement, agent identity, permissions, tool governance, least privilege, monitoring, and audit logging. That pattern is useful when the primary risk is what an AI agent is authorized to do, which tools it can access, and how its runtime decisions are monitored and governed.

VGS vs Trussed: architectural fit

Enterprise architects should evaluate these tools by enforcement point, not only by vendor category. A tokenization or vaulting layer reduces where sensitive values appear. A runtime governance layer controls behavior after an AI agent begins reasoning, retrieving context, calling tools, or acting on behalf of a user or system identity.

Where controls belong in an AI data protection architecture

AI data protection spans data handling, model context, retrieval, agent execution, tool access, and audit evidence. A single control point rarely covers the full path from user intent to agent action.

1. Application and data entry layer

   Use collection, redaction, or tokenization patterns when sensitive values enter applications, APIs, or workflows.

2. Vault and token layer

   Store raw sensitive values in controlled locations and use aliases where applications do not need plaintext.

3. Retrieval and context layer

   Prevent unnecessary sensitive data from entering prompts, embeddings, context windows, and downstream logs.

4. Agent runtime layer

   Enforce policy over agent behavior, identity, permissions, task execution, and runtime decisions.

5. Tool and action layer

   Apply least privilege to individual tools, reveal requests, system calls, and agent-to-agent interactions.

6. Audit and monitoring layer

   Capture structured evidence for tokenization, reveal, policy decisions, blocked actions, tool calls, and outcomes.

Implementation considerations for enterprise AI systems

The practical implementation question is not simply whether sensitive data should be protected. It is where each control should sit, what it should enforce, and what evidence it should produce.

If the immediate concern is raw sensitive data propagation, tokenization and vaulting can reduce the number of systems that handle plaintext. If the immediate concern is agent autonomy, tool access, or runtime authorization, policy enforcement and least privilege controls need to operate during agent execution.

Production AI systems may require both categories of control. Tokenization can reduce the amount of sensitive data that enters the AI workflow. Runtime governance can then determine whether an agent, identity, or tool is permitted to perform a specific action with the data or context available to it.

Practical guidance for selecting the right control mix

Use the enterprise risk model to decide whether the priority is data exposure reduction, runtime action governance, or both. The decision should reflect how the AI system collects data, builds context, retrieves information, invokes tools, and records evidence.

• Evaluate tokenization and vaulting when applications or workflows do not need plaintext sensitive values.
• Evaluate runtime governance when AI agents need policy enforcement over identity, permissions, tools, and actions.
• Evaluate context controls when sensitive data may enter prompts, embeddings, model context windows, or downstream logs.
• Evaluate audit requirements across tokenization, reveal, policy decisions, blocked actions, tool calls, and outcomes.
• Evaluate combined patterns when the risk includes both raw data propagation and agent action authorization.