Interaction Guard is Maetra's user-side control layer for AI tools in browsers, desktop apps, and work surfaces where people paste prompts, upload files, and interact with external AI services. It helps organizations see and control AI use before sensitive data leaves approved workflows.
Most companies now have two AI adoption patterns at the same time. There are approved systems with procurement, security review, and contracts. There is also everyday use of public AI tools, embedded copilots, browser apps, and personal accounts by team members, contractors, and other protected users. Interaction Guard is built for that second surface: the human interaction point where data is copied, pasted, uploaded, or submitted.
The problem Interaction Guard solves
Traditional controls often miss the context of AI use at the browser and app layer. A network log may show traffic to an AI site, but it may not know whether the user was signed into a corporate account, a personal account, an approved tenant, or an unreviewed tool. It may not know whether the interaction involved customer data, source code, secrets, workforce records, strategy documents, or harmless text.
That context matters. The same prompt can be acceptable in an approved enterprise AI workspace and unacceptable in a personal account. A file upload may be fine for public marketing copy and risky for contracts, medical notes, source code, or regulated customer records.
Interaction Guard adds policy context at the point of use so teams can govern the behavior, not just the domain name.
What Interaction Guard monitors
Interaction Guard can track app access, prompt paste, prompt submission, file uploads, account context, app trust level, protected identities, departments, policy matches, and effective action. It can distinguish observation from enforcement and store only the level of content needed for the organization's retention policy.
The goal is not to collect everything users type. The goal is to create enough signal to enforce policy, guide people, and provide evidence without retaining unnecessary sensitive content.
Policy actions
Policies can monitor, warn, require justification, redact, or block. In observe mode, a team can measure AI usage patterns and tune rules before enforcement. In enforce mode, sensitive interactions can be stopped or redirected before the data is submitted.
A warning might explain that a user is about to paste confidential customer information into an unapproved tool. A justification step might allow a reviewed exception with evidence. A redaction action can reduce exposure. A block can prevent high-risk data from leaving the approved environment.
Browser and account context matters
AI governance is weaker when it treats all browser use the same. Interaction Guard is designed around the reality that users move between accounts, browser profiles, apps, extensions, and AI services. It can help identify whether the user is using an approved corporate account or an unmanaged personal account, and whether the target app is approved, monitored, restricted, or blocked.
This account-aware approach is especially important for shadow AI. The problem is not simply that people use AI. The problem is that organizations often cannot tell which tools are being used, under which account, with what data, and whether the use matches policy.
Interaction Guard vs network DLP
Network DLP and secure web gateways are useful, but they are not enough for AI workflows. Interaction Guard is closer to the user action. It understands AI-specific events such as prompt submission, file upload, app trust, account type, and guard matches. That context helps reduce both blind spots and unnecessary friction.
For example, a broad block on AI sites may push teams into workarounds. A contextual guard can allow approved use, monitor low-risk behavior, warn on questionable behavior, and block only the interactions that create real exposure.
How Interaction Guard helps compliance and security
Interaction Guard creates evidence that security, privacy, compliance, and audit teams can use. It can show which AI apps are used, which policies matched, what action was taken, whether the user justified the interaction, and how activity trends by app, department, action, or category.
This helps teams answer practical questions: Are protected users using personal AI accounts for company work? Are sensitive documents being uploaded to unapproved tools? Which departments need more guidance? Are warnings reducing risky behavior? Which policies are too noisy?
Best use cases for Interaction Guard
Interaction Guard is a strong fit for protecting customer records, source code, contracts, credentials, regulated data, confidential strategy, and workforce information during AI use. It also helps organizations steer users toward approved tools instead of relying on policy documents that are hard to enforce at the moment of action.
The outcome is a more practical AI acceptable-use program. Teams can still benefit from AI tools, while the organization gets visibility, controls, and evidence around the interactions that matter.
Interaction Guard makes AI usage governance operational at the browser and app layer. It closes the gap between written AI policy and the real moments when data is pasted, uploaded, submitted, or blocked.