Runtime control layer
3LS Platform is the runtime control layer for enterprise AI use: policy, control, and observability before prompts, uploads, OAuth grants, and delegated tool calls leave your control across Codex, Claude, browsers, and MCP-connected systems.
Runtime decision record
A small set of runtime decisions security teams can explain, audit, and apply consistently.
See how people, assistants, and agentic tools are using AI across the organization.
Understand whether AI is being used for drafting, coding, research, data handling, or higher-risk workflows.
Detect PII, secrets, and restricted content before it becomes an incident or a compliance problem.
Allow, warn, block, or escalate before sensitive context moves through prompts, uploads, OAuth grants, or delegated tool actions.
Discover unmanaged AI use across browsers, assistants, coding tools, and agentic workflows before it becomes an exposure problem.
Identify usage across tools like Codex, Claude, browser-based assistants, MCP-connected workflows, and other agentic tools.
Understand which tools and workflows align with policy and which ones need review, coaching, or controls.
Track adoption, risky behavior, and sensitive usage patterns so security teams can act early.
Focus operator attention on the usage, content, and actions that actually change risk.
Classify whether AI is being used for drafting, coding, research, summarization, or sensitive data handling.
Highlight when prompts, outputs, or tool actions involve PII, credentials, or restricted internal data.
Apply the right response based on context, from simple visibility to warnings, blocks, and escalation.
Give security teams clear evidence and effective controls without turning every AI interaction into a manual review queue.
See usage patterns clearly.
Understand which tools are managed and which are shadow AI.
Respond to risky behavior with consistent controls.
Keep a clear audit trail of findings and outcomes.
Detect and manage shadow AI usage, understand how tools like Codex and Claude are being used, and apply the right controls with confidence.
From the blog
Research that explains the platform problem space: shadow AI, enterprise context, tool risk, and the visibility required before controls can work.
An AI vendor can secure its product, but it cannot see your copied data, approvals, internal policy, or tool entitlements. The enterprise still owns AI context security.
If agent safety lives in user settings, you do not have policy. You have uneven risk decisions across teams.
Organizations should stop treating AI governance as a procurement checklist. The real operating model is policy, control, and observability at the moment data or delegated authority moves into AI.