Employee AI Copilots
Secure ChatGPT, Claude Cowork, Microsoft Copilot, Gemini, Slack AI, and other employee copilot apps across prompts, files, browsers, plugins, computer use, and SaaS workflows.
ChatGPT, Claude Cowork, Microsoft Copilot, Gemini, Slack AI
Computer use attempted a spreadsheet export outside the approved task.
Prompt included customer PII copied from a support ticket.
Finance workbook summary cited a restricted tab.
Secure ChatGPT, Claude Cowork, Microsoft Copilot, Gemini, Slack AI, and other employee copilot apps across prompts, files, browsers, plugins, computer use, and SaaS workflows.
3 field failure modes become adversarial campaigns tailored to this deployment.
Asset Management, Runtime Security keep the workflow bounded after launch.
Built for this workflow
# Apply the solution playbook. # $ ga solutions apply internal-employee-agents deployment: internal-employee-agents assets: - copilot apps - browser sessions - local files test_against: - Sensitive data pasted into public chatbots - Slack and browser summarizers are prompt-injection surfaces - Claude Cowork expands the desktop control boundary runtime_controls: - AI Security Asset Management - AI Runtime Security evidence: traces,citations,owners
Field evidence
Employee AI Copilots deployments fail when the model gets more trust than the workflow can safely absorb. These examples become concrete tests, not generic awareness copy.
Samsung engineers uploaded chip schematics to ChatGPT and Amazon lawyers warned staff after the bot echoed internal code—proof that one careless employee prompt can exfiltrate crown-jewel IP, customer data, or HR records.
PromptArmor researchers showed a single crafted Slack message could manipulate AI summaries and expose data from supposedly private channels. The same pattern applies when ChatGPT, Claude Cowork, or browser copilots summarize webpages, tickets, docs, and email.
Claude Cowork-style apps can combine local files, browser sessions, plugins, scheduled tasks, and computer use. That is useful for employees, but it requires a control plane around file grants, browser actions, plugin supply chain, and app-level data movement.
How General Analysis helps
The playbook connects discovery, automated red teaming, and runtime protection so controls stay specific to the deployment instead of becoming a generic policy layer.
Map every copilot app, workspace, connected folder, browser profile, plugin, MCP server, and knowledge source employees can attach to AI systems.
Apply an on-device proxy or LLM gateway policy layer for prompts, files, browser actions, plugin calls, computer-use events, redactions, approvals, and audit logs.
Inventory copilot apps, browser sessions, local files and the identities, tools, and data paths attached to the workflow.
Turn field failures into adversarial prompts, multi-turn tests, tool-use probes, and policy traps for this deployment.
Apply copilot data controls, proxy-gated actions, and escalation rules where the workflow needs them.
Prompt, file, browser, and tool traces
FAQ
Practical answers for deploying employee ai copilots with controls that security, legal, and operators can inspect.
General Analysis treats employee copilots as a data-movement problem, not just a chatbot policy problem. We map each app, workspace, browser profile, connected folder, plugin, MCP server, and SaaS connector; route observable traffic through an on-device proxy or LLM gateway; and apply policy to prompts, file reads, browser actions, screenshots, tool calls, uploads, shares, and model outputs. Safe actions continue normally, while risky actions are redacted, blocked, downgraded to read-only, or sent to human approval with a complete trace.