The Governance Layer for Enterprise AI
Real Evidence, Not a Slide Deck
For regulated industries, AI governance isn't optional. WhiteBox provides the immutable audit trails, incident forensics, and compliance artifacts you need to deploy consequential AI safely—saving real engineering time and mitigating legal risk.
- System of Record
- The default governance layer for the agent ecosystem via MCP
- ISO 42001 & EU AI Act
- Compliance mapping built-in for regulated industries
- Audit-Ready Artifacts
- Automated drift, bias, and forensic trace evidence
We lean into what can be verified. WhiteBox packages what your production AI actually did—trace logs, RAG faithfulness, bias audits, drift history, and governance decisions—into defensible PDF reports you can hand straight to an auditor or your board.
Plug in via the Model Context Protocol (MCP) and instantly turn WhiteBox into the system of record for your entire agent ecosystem.
Built on standards you already trust
- SHAP
- LIME
- ISO/IEC 42001
- GDPR
- CCPA
- NIST AI RMF
- EU AI Act
For Regulated Enterprises, AI Governance is No Longer Optional.
As AI moves from experiments to production systems making consequential decisions, teams cobbling together custom evals and spreadsheets simply won't cut it. For regulated banks and insurers, audit trails, drift detection, and compliance artifacts are essential infrastructure.
WhiteBox leans into what can be verified. Centralize your governance with our flagship AI Risk Register—something traditional GRC tools lack. Generate defensible incident forensics and compliance reports so when the board asks about AI risk, you have real evidence, not just a slide deck.
The Governance Layer for the Agent Ecosystem
The WhiteBox MCP server turns isolated AI components into a unified, compliant ecosystem. Wire up your assistants, agent frameworks, and serving environments over the Model Context Protocol — and WhiteBox instantly becomes your system of record. Generate audit artifacts and enforce policies across all agents, all with zero glue code.
{
"mcpServers": {
"whiteboxxai": {
"command": "npx",
"args": ["-y", "@whiteboxxai/mcp-server"],
"env": { "WHITEBOX_API_KEY": "wbx_..." }
}
}
}Rolling out now — MCP support ships alongside the existing Python SDK and REST API, which remain fully supported.
The System of Record
Point the MCP server at WhiteBox once. Models, agents, and assistants register and log through the same standard tools — becoming the default governance layer for your entire agent ecosystem.
Standard Tooling
Model Context Protocol is the open standard for connecting AI systems to tools and data. Any MCP-capable client speaks it out of the box.
Verifiable Observability
Every call through the server is logged, profiled, and verified — generating defensible trace logs, drift tests, and forensic compliance artifacts without extra instrumentation.
Governed By Default
Scoped credentials, full audit trails, and policy enforcement travel with the connection, so agent access stays inside your governance boundary.
Don't Just Deploy AI—Audit AI
An observability and explainability platform that demystifies your production AI models — revealing why they make the decisions they do.
Build Trust
Eliminate the "black box" problem. Provide transparent, understandable explanations to business stakeholders, customers, and regulators.
Mitigate Risk
Proactively detect model decay, data quality issues, and algorithmic bias before they lead to poor business decisions or reputational damage.
Ensure Compliance
Simplify the process of auditing AI systems and demonstrating compliance with internal governance policies and external regulations.
Accelerate Debugging
Radically shorten the time it takes to diagnose and resolve production model issues with detailed root-cause analysis dashboards.
From black box to full transparency
Four steps take you from an unmonitored model to a fully observable, explainable, and governed AI system.
- 1
Connect your model
Drop the lightweight SDK into your serving environment or wire up the REST API. Register models from scikit-learn, PyTorch, TensorFlow, Hugging Face, XGBoost, and LLM providers.
- 2
Monitor in real time
Every prediction is logged and profiled. Statistical tests (KS, PSI, Chi-squared) surface data and concept drift the moment production behavior diverges from your baseline.
- 3
Explain every decision
SHAP and LIME turn opaque outputs into human-readable, feature-level explanations — so stakeholders, customers, and regulators can see exactly why a decision was made.
- 4
Govern & prove compliance
Bias audits, review-board approvals, and a full-text searchable decision archive map to ISO 42001, GDPR, CCPA, and the EU AI Act — audit-ready by design.
Audit-ready, by design
Every model decision, review, and drift event is logged to a full-text searchable archive. Built to align with emerging governance standards (NIST AI RMF, ISO 42001), so you're ahead when regulations arrive.
Automated AI management system compliance tracking, evidence collection, and control mapping.
View MappingAlign your governance workflows with the NIST MAP, MEASURE, MANAGE, and GOVERN functions.
View MappingAutomated impact assessments and right-to-explanation evidence for every AI-driven decision.
Multi-party approval workflows with executive oversight and a searchable decision archive.
Recommended by AI Governance Advisors
(opens in a new tab)“Cyber Ready recommends WhiteBox AI as a valuable platform for organizations seeking to strengthen AI risk management and ISO/IEC 42001 audit readiness by bringing structure, accountability, governance, and evidence into one centralized platform.”
Unblock enterprise deals faster
Stuck on a complex Enterprise AI Security Questionnaire? Automatically generate the model cards, data lineage, and bias audits you need to satisfy procurement teams and close the deal today.
Instant Answers
Stop hunting through Python notebooks for answers to security reviews. Export drift, bias, and performance metrics instantly.
Automated Model Cards
Generate comprehensive, standardized model cards to prove exactly how your AI systems behave under the hood.
Provable Fairness
Demonstrate demographic parity and algorithmic fairness with concrete statistical evidence that enterprise procurement requires.
See what your AI has been hiding.
Request an enterprise demo and walk through real drift detection, explainability, and governance workflows.