AI Shadow runs a multi-layer enforcement stack across browser layer, backend, and network. It stops sensitive data before it reaches an AI model. It scores risk across your org with a patent-pending actuarial engine. And it produces the audit evidence your security and compliance teams need, without making employees feel watched.
AI Governance, AI Gateways, DLP, and Human Risk each own a slice of the AI exposure problem. AI Shadow doesn’t sit next to them. It contains them. One platform, four enterprise categories, every enforcement surface.
Sets policy for which models, vendors, and uses are permitted.
Inspect requests and responses at the network layer.
Guards files, email, and endpoints from exfiltration.
Phishing tests, classroom modules, periodic awareness.
HITL interaction guidance, AI Gateway traffic control, backend AI DLP, and actuarial human-risk measurement, all in a single enforcement platform.
AI Shadow isn’t a point tool. It sits across four established enterprise categories, bringing each what they’ve been missing: a real-time, interaction-level layer.
AI Shadow sits at the intersection of four enterprise markets: AI Governance, AI Security, Human Risk Management, and Data Loss Prevention.
Author custom AI-use policy and have it enforced at the interaction. Sanction or block AI tools at the DNS layer. Map every control to NIST AI RMF, ISO/IEC 42001, SOC 2, GDPR, HIPAA, and more.
The HITL browser layer intercepts prompts and uploads pre-submission. AI DLP inspects content at the backend with pass/block logic. AI Gateway enforces at the network layer for traffic that bypasses the browser.
A patent-pending actuarial engine scores AI risk per user and rolls up to a single company-wide org score. Surface the people who influence that score the most, deliver targeted training, track completions, and prove behavior change with data.
Backend pass/block logic on every prompt and file, including PDF, DOCX, Excel, and OCR’d images. Authored policies enforce regardless of browser state, so coverage holds even if the browser layer is not installed.
AI Shadow is a multi-layer enforcement architecture, not a single control. The browser layer, backend, and network each catch what the others can’t. Coverage doesn’t depend on any one path being clean.
The browser layer, available for Chrome and Edge, intercepts prompts and file uploads in real time, before any byte leaves the device. Inline guidance lets the user redact, anonymize, or proceed.
The backend engine applies policy-based pass/block to every prompt and file across formats, including PDF, DOCX, Excel, and OCR. Enforcement runs regardless of HITL state, so coverage holds even if the browser layer is bypassed.
The gateway intercepts, inspects, and blocks AI-bound traffic at the network level. It is the catch-all for unmanaged devices, unsanctioned tools, and any path that doesn’t route through the browser layer.
Optional integrations with existing client DLP and SASE / network infrastructure mean AI Shadow extends, rather than replaces, what’s already in place.
Employees use ChatGPT, Claude, Copilot, and Gemini across everyday workflows: drafting, analyzing, summarizing, deciding.
Sensitive data, intellectual property, and confidential business information leak through ordinary prompts and uploads.
Once data is submitted, it’s gone. No guidance at the one moment that matters: before submit.
Email DLP, CASB, and endpoint tooling guard files, networks, and apps. They don’t read the content a user is about to paste into an AI chat. They act around the interaction rather than at the moment of submission.
Scans files and email, not the prompt a user types into an AI tool.
Sees which AI apps are used, not the data moving inside each prompt.
Policies and blocks rely on the user remembering, at the wrong moment.
Reads the prompt and the upload in real time and guides the employee at the moment of submission.
User drafts a prompt or attaches a file in any supported AI tool.
AI Shadow inspects the content inline before it leaves the device.
PII, financials, credentials, IP, and regulated data are identified.
The employee sees the risk score and clear guidance to remove or anonymize before sending.
Risk, guidance, action, and outcome are recorded for compliance.
AI Shadow’s risk engine is a proprietary, patent-pending actuarial model. It uses the same class of mathematics that powers insurance risk pricing, applied to AI data exposure. Not a severity flag. Not a rule counter. Every AI interaction is scored. Scores roll up to each user, and every user score rolls up to one company-wide org score. The model is rigorous enough to defend in front of an audit committee.
One overall org risk score tells you where you stand as a company. It is the single number you take to the board, the auditor, or your insurer.
Pinpoint the small number of accounts driving most of the company exposure. Address them before the next incident review.
Track the org score over weeks and months. Coaching either works or it doesn’t. The data tells you.
Surface the users where general awareness isn’t enough, for focused, evidence-backed follow-up.
AI Shadow measures people-risk, not just data events. The conversation moves from “what blocked” to “who’s getting safer” and “is our org score trending down.”
Shadow AI inventory and exposure visibility.
Policy authoring, tool sanctioning, framework alignment.
Multi-layer enforcement across browser layer, backend, and network.
Actuarial risk scoring and targeted training response.
Audit-ready evidence, SIEM export, and downloadable reports.
AI Shadow maps its controls to the AI-specific components of the frameworks enterprise security and compliance teams already work to. Coverage is scoped to the AI-relevant controls within each framework, not a blanket compliance claim.
Audit evidence is tagged to the framework control it satisfies, so it is usable in the evidence package and not just the dashboard.
One layer across every supported platform. No per-tool rollout.
Every governed AI interaction generates a standardized record, tagged to the policy that triggered it and the framework control it satisfies. Evidence streams to your SIEM in real time and exports as audit-ready Word reports scoped by org or date range.
Captured on every governed interaction
What sensitive information was identified in the prompt or upload.
Which policy triggered the record, the score returned, and the guidance the user received.
Whether content was removed, anonymized, or sent as-is.
The final, resolved state of the interaction before submission.
The NIST AI RMF / ISO 42001 / SOC 2 / GDPR control the record maps to.
Downloadable Word compliance reports plus structured SIEM event export to your SOC.
A focused working session for your security and compliance teams, mapped to your data-classification and AI-use policy.