Data protection
Prompts
Detect broad categories of sensitive content such as PII, credentials, and sensitive keys and secrets.
Meridian AI security operations
AI Atlas
Map AI usage from the endpoint before it becomes unmanaged risk.
AI Atlas uses the Aegis endpoint agent to monitor enterprise AI usage where work happens: on the endpoint. It gives organizations visibility into sensitive prompt data, risky agent skills and MCP servers, token consumption, estimated usage costs, and the ability to identify users connecting their personal AI accounts.
OpenAI's Codex
Anthropic's Claude Code
Google's Gemini CLI
GitHub Copilot in VS Code
Endpoint level detection
AI risk is already outside the central console.
Employees use local AI harnesses, editor integrations, and agent tooling before security teams have a complete inventory. AI Atlas analyzes endpoint AI harness activity as security context, leveraging the power of Aegis to get started immediately, no setup required.
Extension review
Skills and MCP
Inspect installed agent extensions for suspicious instructions, risky tools, and malicious behavior.
Operational visibility
Usage
Understand AI usage patterns without waiting for every workflow to move into one approved tool.
Identity context
Accounts
Differentiate approved company accounts from personal AI accounts connected to work activity.
Prompt DLP and extension review
Inspect the AI work that usually stays invisible.
Atlas monitors AI usage for sensitive data and pairs that with deep skill and MCP security detection and visibility. This helps teams separate normal AI adoption and acceptable use from activity that needs coaching, policy, or containment.
Prompt data leak detection
Monitor user prompts for sensitive data patterns such as PII, credentials, secrets, passwords, and other regulated or confidential information.
Skill and MCP security monitoring
Inspect installed agent skills and MCP servers for suspicious behaviors including exfiltration patterns, obfuscated instructions, and unsafe configuration drift.
AI usage observability
Track token counts, estimated token costs, user adoption, and personal AI account usage so teams can govern AI activity with real data.
Operating model
Give AI adoption a security workflow, not another blind spot.
Collect from endpoints
Aegis observes AI harness activity where employees already work: terminals, editors, agents, and local tooling.
Classify the activity
Atlas separates prompt DLP findings, extension risk, usage context, and account identity into reviewable security evidence.
Govern with context
Security teams can coach users, review unsafe local components, and report AI adoption with clearer operational context.
Detect sensitive data entering AI systems
Identify risky local AI agent extensions
Measure AI usage, cost, and account identity across endpoint users
Need endpoint AI visibility?
See what users are sending to AI before risk becomes policy debt.
Monitor prompt leakage, local AI extensions, token usage, estimated cost, and account identity with Meridian AI Atlas.