Everything ShadowWatch does to govern AI use without slowing down the work.
A lightweight browser plugin that discovers AI tools, detects sensitive data, and defends with adaptive guardrails you can prove to an auditor.
Every capability sits under one framework: Discover, Detect, Defend. Find every AI tool in use, catch sensitive data before it leaves, enforce guardrails by risk level, then prove it with an audit trail.
- DiscoverEvery AI tool your team uses.
- DetectSensitive data before it leaves.
- DefendAdaptive guardrails by risk level.
- Prove itAn audit trail you can hand over.
Discover every AI tool in use
Visibility into AI use across any browser your team uses, not just the tools on a list.
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Detect Any AI Tool
Automatically detect interactions with ChatGPT, Claude, Gemini, Copilot, and any other AI tool your team uses, including tools not on any list. Detection is automatic and requires no configuration.
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Live AI-Use Visibility
See AI interactions across your monitored browsers, with usage by tool and user. Captured at submit time, not keystroke by keystroke, so visibility is not surveillance.
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Track the AI-Site Lifecycle
Every AI site moves through three states (Monitored, Under Review, and Ignored) so you can triage newly discovered AI with intent, not a binary allow/block list.
Detect sensitive data in real time
Nudges that steer employees toward approved AI and away from risk, without slowing down the work.
Warn Before They Trust
The AI Presence Indicator warns employees before they trust an AI on an unknown site: the fake-Slack, AI-pretending-to-be-your-CEO scenario that DLP misses. Once per tab, admin-customizable, and logged.
Define What's Sensitive
Set which data types matter for your organization (PII, source code, financials, secrets) and warn or block when they show up in AI interactions.
Start from Policy Templates
Begin with industry-specific policy templates for regulated industries and customize from there. No blank-page setup.
Defend with adaptive guardrails
Enforcement you turn up as you mature. Start with audit mode, add warnings, then selectively block the highest-risk transfers.
Control Who Uses What
Apply role-based policies so different teams can use different AI tools on managed browsers, with permissions tied to role, department, or data sensitivity.
Set the Response by Risk Level
Choose how to respond by risk level: log only, warn the user so they self-correct, or block the submission. Move from observation to enforcement when you're ready.
Block at Submit, Not After
Sensitive data is intercepted at submit time, before it reaches any AI tool, so the block happens before the loss, not after. Employees get a nudge toward an approved path instead of a dead end.
Prove it: audit & evidence
A defensible, queryable record of what was shared, when, and by whom, with sensitive patterns auto-redacted.
Keep a Queryable Audit Trail
Every AI interaction on your monitored browsers is logged with timestamps, users, and context. Search and filter by user, tool, policy, or risk level.
Export Audit Evidence
Export the evidence auditors and regulators actually ask for (interaction logs, policy posture, and redacted records) in a format you can hand over.
AI Presence Indicator
A warning before employees trust an AI they weren't expecting
Employees are running into AI where it doesn't belong: an AI assistant injected into a page that looks like a known app, a chat that impersonates an executive, a tool that quietly exfiltrates what's typed. DLP and DNS filters don't flag it, because the page itself looks fine. The AI Presence Indicator watches for AI on sites where your team wouldn't expect it and shows a one-time, admin-customizable warning so the employee stops and looks before they trust it.
Once per tab
Fires once per tab so it warns without nagging, then gets out of the way.
Admin-customizable
Customize the message and the sites it fires on per policy.
Logged
Every event lands in the audit trail so you can see where it fired.
The boundary: the indicator warns, it doesn't block. It surfaces the risk; the employee decides. It catches the browser-chat variant of AI impersonation. It would not catch a deepfake video call (the 2024 Arup fraud was a video call, not a browser session), and we won't pretend it would.
Integrations
Alert where you already work, and pull governance data into your stack
Security teams route alerts to the tools they already use and pull AI-governance data into their SIEM, GRC, or automation stack. For how we handle the data behind all of it (local risk scoring, auto-redaction, retention), see how we handle your data →.
Alert Where You Already Work
Get policy-violation alerts on Slack, email, webhook, Splunk HEC, or your SIEM via CEF, and route them to the people who need to see them.
API, MCP & SIEM Access
Enterprise plans get API and Model Context Protocol (MCP) access to pull AI-governance data into your SIEM, GRC, or automation stack, so agents and workflows query it the same way they query your other systems.
Go deeper
See how ShadowWatch compares to what you already run, what it costs, and how it maps to the frameworks you answer to.