AI Governance
What Is the Difference Between AI Governance and AI Compliance?
The two terms are used interchangeably, but they are not the same job. Kriv AI helps regulated enterprises run governance as a standing program and treat compliance as one of its outputs.
AI governance is the ongoing program an organization runs to set policy, assign accountability, and manage AI risk. AI compliance is the narrower job of meeting specific legal requirements, such as the EU AI Act, inside that program. Governance decides how AI is overseen; compliance proves specific rules are met. Kriv AI's work starts at $200 per hour.
context
Two Terms, Two Different Jobs
Buyers often ask for one and mean the other. A useful test is scope and time horizon: governance is a standing management system for AI, and compliance is a point-in-time answer to a specific rule.
What AI governance covers
The NIST AI Risk Management Framework, released as AI RMF 1.0 on January 26, 2023, is described by NIST as a framework "to better manage risks to individuals, organizations, and society associated with artificial intelligence (AI)" and is "intended for voluntary use." Its GOVERN function is described as "a cross-cutting function that is infused throughout AI risk management and enables the other functions of the process," and it is the part that cultivates a culture of risk management and puts accountability structures in place so that "the appropriate teams and individuals are empowered, responsible, and trained."
In practice that means a model inventory, risk tiering, a decision-making body, defined owners, human review points, and ongoing monitoring. None of it is tied to one regulation. It exists whether or not a regulator is asking.
What AI compliance covers
Compliance is the act of meeting requirements that an outside authority has set. The EU AI Act is a clear example. Article 17 says that "providers of high-risk AI systems shall put a quality management system in place that ensures compliance with this Regulation," and lists what that system must document, including a regulatory compliance strategy, testing and validation procedures, data management, post-market monitoring, serious incident reporting, and record-keeping.
Notice the structure. The regulation asks for a management system, so compliance depends on governance being there. Compliance names the obligation; governance is how an organization keeps meeting it.
ai gap
Why Compliance Without Governance Breaks Down
Organizations that treat compliance as a checkbox audit tend to produce evidence once and let it age. Models drift, vendors change versions, staff add tools nobody inventoried, and the file that satisfied last quarter's review no longer describes what is running. Governance is what keeps the evidence current: a live inventory, monitoring that flags change, and named owners who are accountable when it does.
The reverse also holds. A governance program with no mapping to specific rules can be thoughtful and still miss a binding requirement. The two need to be built together, with each regulation mapped to the controls and evidence the governance program already produces. Different sectors add their own rules on top, such as model risk expectations for banks, state insurance rules, and validation requirements for regulated life sciences work.
capabilities
How the Two Fit Together in an Engagement
Governance foundation
Policy, a model and vendor inventory, risk tiers, a governance committee charter, and human-in-the-loop review gates. This is the standing program.
Regulatory mapping
A control map that ties each applicable requirement, for example EU AI Act obligations or sector rules, to a specific control and owner in the program, so a new rule becomes a mapping exercise instead of a new project.
Evidence and monitoring
Audit trails, monitoring results, and review records kept current as a by-product of running the program, so compliance evidence is always ready and never rebuilt from scratch.
Assurance and reporting
Periodic reviews and board-ready summaries showing what is governed, what is compliant, and where gaps remain.
differentiation
Where This Page Fits Among Our Other Pages
This page explains the conceptual difference. If you want the practical build, see our guide to building an AI governance framework for a regulated enterprise, or our AI governance consulting overview. For a general list of terms, our AI glossary defines each in one place.
engagement
How an Engagement Works
A scoped engagement usually starts with an inventory of the AI systems in use and the rules that apply to them, moves to a governance design and regulatory control map, and ends with monitoring and reporting your team can run without us. We work alongside your compliance, legal, and technology leads and do not resell any vendor's product.
tiers
What You Get at Each Tier
1. Enterprise / regulated (banks, health systems, insurers, life sciences)
A full governance program design with regulatory mapping, evidence and monitoring design, and board-level reporting.
2. Fractional CTO / AI governance lead
Ongoing ownership of the governance program, committee support, and regulatory mapping updates as new rules and AI systems arrive.
3. Specialized advisory
A single session or second opinion on whether an existing program covers both governance and specific compliance obligations.
rate card
Kriv AI's Rates for This Work
These are Kriv AI's own published rate floors, not an industry average.
| Track | Kriv hourly rate | Typical engagement model | Minimum engagement |
|---|---|---|---|
| Enterprise / regulated (banks, broker-dealers, payment processors) | From $200/hr | Fixed-scope project or retainer | $8,000 |
| Fractional CTO / AI governance lead | $300 to $400/hr | Part-time, ongoing (monthly) | $8,000 |
| Specialized advisory (vendor evaluation, second opinion) | $400 to $700/hr | Hourly, per-session | Varies by engagement |
| Small business | $150/hr | Referred to Kriv AI's partner network | n/a |
get a quote
How to Get a Real Quote
The rates above are floors, not a quote. Actual price depends on how many AI systems are in scope, which regulations apply, and how much policy and monitoring documentation already exists. Book a discovery call and we will scope it honestly.
Straight answers
Frequently asked questions about What Is the Difference Between AI Governance and AI Compliance?
What is the main difference between AI governance and AI compliance?
Governance is the ongoing program that sets policy, assigns accountability, and manages AI risk. Compliance is meeting the specific legal or regulatory requirements that apply, and it sits inside that program.
Can an organization be compliant without governance?
Briefly, yes, for a single audit. But evidence goes stale as models, vendors, and tools change, so compliance without governance is hard to sustain. Rules such as EU AI Act Article 17 also expect a management system.
Is the NIST AI RMF a compliance requirement?
NIST describes it as intended for voluntary use. It is a governance and risk management framework, though its functions can be mapped to binding requirements.
Which comes first?
Governance usually comes first, because it produces the inventory, ownership, and monitoring that compliance evidence depends on. In practice the two are designed together.
What does the EU AI Act require of high-risk AI providers?
Article 17 requires providers of high-risk AI systems to put a quality management system in place covering areas such as testing and validation, data management, post-market monitoring, incident reporting, and record-keeping.
What does this cost with Kriv AI?
Kriv AI's rates start at a $200/hr floor for enterprise and regulated work, with an $8,000 minimum engagement. Specialized advisory runs $400 to $700/hr.
Talk to the team that would do the work
Bring your requirements to a working session with the person who'll actually deliver.
Book a Discovery Call