AI Governance
What Is AI Governance?
- Accountability
- Risk management
- Oversight
Short answer
AI governance is the set of policies, roles, processes and records an organization uses to make sure AI is used responsibly, lawfully and safely. It assigns accountability, defines allowed uses, assesses risk, and checks that the rules are followed. It covers AI you build as well as AI you buy or staff use on their own.
Maskflare Engineering Updated
What does AI governance actually cover?
Imagine three requests landing in one week. Marketing wants a new AI image tool. Engineering wants to put an LLM into the support product. HR wants a tool to rank applicants. Governance is what lets you answer all three consistently: who decides, on what criteria, with what evidence, and who checks later.
In practice it has five parts:
- Accountability: named owners, a committee or role that decides, and clear escalation.
- Inventory: a list of AI systems and tools in use, built or bought, with purpose and owner.
- Risk assessment: judging each use for harm to people, legal exposure and security, in proportion to its impact.
- Policies and controls: rules such as an acceptable-use policy, plus technical measures that enforce parts of them.
- Monitoring and records: logs, reviews, incident handling and the evidence to show an auditor.
Which frameworks and laws shape it?
| Source | What it is | Status |
|---|---|---|
| NIST AI Risk Management Framework 1.0 | A US framework with a Govern function and further functions for mapping, measuring and managing AI risk; released 26 January 2023 | Voluntary |
| ISO/IEC 42001:2023 | An international standard for an AI management system, published December 2023, that organizations can be certified against | Voluntary, certifiable |
| EU AI Act | EU regulation with risk-based duties for providers and deployers; AI literacy (Art. 4) applies from 2 February 2025; Annex III high-risk rules from 2 December 2027 under Regulation (EU) 2026/1744 | Binding in the EU |
| GDPR | Applies whenever AI processes personal data, including prompts | Binding in the EU |
Most organizations use these as scaffolding rather than starting from scratch. A NIST-style structure helps you organize risk work, ISO 42001 gives you a certifiable management system if customers ask for one, and the EU AI Act and GDPR set the legal floor if you operate in Europe.
Where do technical controls fit?
Governance decides; controls carry out. If the policy says personal data must not reach unapproved AI providers, something has to detect and stop it. If the risk committee approves ChatGPT for the company workspace only, something has to notice personal accounts. Without controls, governance is paperwork. Without governance, controls are arbitrary.
| Governance need | Typical technical control |
|---|---|
| Know what AI is in use | Discovery of AI apps, API keys and agents |
| Keep personal data out of providers | Masking or blocking before the request leaves |
| Guide staff at the moment of risk | Warnings with justification |
| Show who changed which rule | Audit trail with approvals |
| Control agents and tools | Policy for MCP and agent access |
What does Maskflare cover, and what doesn't it?
Maskflare supplies controls for the "AI use" side of governance. AI app discovery lists the AI apps on managed devices with users and upload volume, as metadata only. The Workforce agent and gateway mask personal data and secrets using 574 detectors across 53 packs. User coaching adds warnings, and the console provides roles, four-eyes approval of rule changes and an exportable audit trail. Maskflare's own SOC 2 Type II and ISO 27001 certifications are in progress.
It does not maintain a register of the AI systems you build, classify them under the EU AI Act, run risk assessments, manage vendor questionnaires, track model performance or bias, or replace a data protection impact assessment. Those need a governance process and, for many teams, a dedicated governance or GRC tool. See what is a DPIA for AI tools for one assessment Maskflare's records can feed. It also isn't an AI-SPM product, which looks at the configuration of your cloud AI assets.
How do you start without a big program?
- Name an owner and a small decision group: security, legal, one business lead.
- Find out what is already in use. Ask teams, check expenses, and use discovery on managed devices.
- Write a one-to-two page acceptable-use policy and publish an approved tools list.
- Put basic controls in front of the biggest risk, usually personal data and secrets going into AI tools.
- Run assessments for the few uses that matter most, such as anything touching hiring, credit or health.
- Review every quarter and keep the evidence.
Frequently asked questions
Is AI governance the same as AI compliance?
No. Compliance is meeting specific legal and contractual requirements. Governance is the broader system of ownership, policy and oversight that helps you meet them and manage risks laws don't cover.
Do small companies need AI governance?
Some version of it, yes: an owner, a short policy, a list of approved tools and basic controls. The scale should match the risk and the size of the organization.
Is ISO 42001 mandatory?
No. ISO/IEC 42001:2023 is a voluntary, certifiable management-system standard. Some customers or procurement teams may ask for it.
Is Maskflare an AI governance platform?
No. Maskflare provides technical controls for AI use, including discovery, masking, warnings and audit evidence. It does not manage model inventories, risk classification or governance workflows.
What is the difference between AI governance and AI usage control?
Governance is the management discipline. Usage control is the technical enforcement of the rules about how people and applications use AI, one of the tools governance relies on.