Resources
Enterprise AI governance, explained
Practical guides and plain definitions for the teams responsible for AI risk — written for security, privacy, and compliance leaders rather than for a search engine.
Guides
Discovery
Shadow AI Discovery
Find every AI app, model, and browser extension in use across your workforce — including off-network laptops and locally-run models.
Read the guideGovernance
Real-Time AI Governance
Author AI policy as code, simulate it in monitor mode, then enforce inline — from a coaching nudge to a hard block — with sub-10ms local decisions.
Read the guideData Protection
AI-Aware Data Loss Prevention
Detect PII, PHI, PCI, secrets, source code, and IP inside AI prompts and uploads — and redact sensitive spans inline, before the data ever reaches a model.
Read the guideRisk
Explainable AI Risk Scoring
A dynamic risk score for every AI interaction, combining weighted-factor scoring with behavioural anomaly detection — always explainable.
Read the guideSolution
Shadow AI: The Risk You Can’t See
Shadow AI is unsanctioned employee use of AI tools and models. Learn how it happens, why traditional controls miss it, and how to build visibility and control.
Read the guideCompliance
EU AI Act Compliance
Map AI usage and controls to the EU AI Act with framework-mapped reporting, explainable decisions, DPIA artifacts, and an immutable audit trail.
Read the guidePrivacy
Data Privacy & Residency for AI
Keep AI monitoring data inside your own tenant with a customer-hosted data plane, customer-owned encryption keys, and jurisdiction-aware capture.
Read the guide
Glossary
The terms that come up in every enterprise AI governance conversation.
- Shadow AI
- Unsanctioned use of AI tools, models, or extensions by employees, outside the visibility and approval of the security team.
- AI-aware DLP
- Data loss prevention that inspects and redacts sensitive content inside AI prompts and uploads before the data reaches a model, rather than inspecting network traffic after the fact.
- Inline enforcement
- Applying a policy decision synchronously at the moment of interaction — before data is sent — instead of detecting a violation retrospectively in logs.
- Customer-hosted data plane
- An architecture where all data processing runs inside the customer’s own tenant, so the vendor never takes custody of prompts, files, or monitoring data.
- Prompt injection
- An attack where crafted text causes a model to ignore its instructions, leak data, or take unintended actions on behalf of an attacker.
- Explainable risk score
- A risk rating that exposes the weighted factors behind it, so an analyst can see why an interaction was scored as it was rather than trusting a black box.
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