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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →AI governance sets the rules, responsibilities, and oversight for AI across its lifecycle. AI safety assesses and reduces the risk that a system will behave in harmful ways in its intended context and foreseeable conditions. They are distinct but connected: governance assigns people to act on safety evidence, while safety work supplies evidence for governance decisions.
What is the difference between AI governance and AI safety?
Governance is about how an organization or institution makes and oversees AI decisions. Safety is about whether a particular AI system can cause harm in its context, and how that risk can be prevented, detected, contained, or addressed. Governance is broader: it can also cover legal duties, privacy, fairness, transparency, accountability, and other responsibilities.
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| Question | AI governance | AI safety |
|---|---|---|
| Main question | Who is responsible, what rules apply, and how will oversight work? | What harmful behavior could occur, and how can it be prevented or mitigated? |
| Typical work | Policies, risk ownership, approval criteria, legal and regulatory mapping, documentation, monitoring, and incident escalation. | Hazard analysis, system evaluations, robustness and misuse testing, safeguards, monitoring, incident response, and safe shutdown or correction. |
| Scope | Organization, ecosystem, and AI lifecycle. | A system in a defined use context, assessed across its lifecycle. |
| Evidence | Assigned owners, documented processes, compliance records, and review decisions. | Evaluation results, observed behavior, hazard and incident evidence, and whether controls work. |
| Relationship | Ensures safety work is assigned, reviewed, and acted upon. | Provides evidence that informs governance decisions. |
These are practical descriptions, not a universal job chart imposed by a framework. In many organizations, the same teams may contribute to both.
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What AI governance is responsible for
AI governance establishes how AI is built, acquired, approved, deployed, monitored, and retired. It connects risk management to organizational priorities and specifies how decisions are documented, challenged, and corrected.
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NIST describes the Govern function of its AI Risk Management Framework (AI RMF) as one that “cultivates and implements a culture of risk management within organizations designing, developing, deploying, evaluating, or acquiring AI systems.” Its outcomes include policies and processes, assigned roles, lifecycle and third-party considerations, and understanding and documenting applicable legal and regulatory requirements. NIST AI RMF Core
- Deciding who can propose, build, buy, approve, deploy, and retire an AI system.
- Setting risk-review expectations and approval or escalation routes.
- Identifying which legal, regulatory, contractual, and organizational requirements apply.
- Requiring evidence and records that support decisions throughout the system’s lifecycle.
- Assigning owners to monitor performance and respond when conditions or risks change.
What AI safety is responsible for
AI safety examines a system’s behavior in its intended use and foreseeable conditions, including misuse or other adverse conditions. The work identifies hazards, considers the likelihood and consequences of harm, and develops controls to reduce risk. Safety is not just a pre-release test: it involves monitoring and intervention as the system is used and circumstances change.
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The OECD AI Principles say that AI systems should be “robust, secure and safe throughout their entire lifecycle” and function appropriately without unreasonable safety or security risks in normal use, foreseeable use or misuse, or other adverse conditions. The principles also call for systems to be overrideable, repairable, or safely decommissioned when needed. OECD AI Principles
- Identifying ways system behavior could create harm in its context.
- Testing robustness, misuse risks, and safeguards against relevant hazards.
- Monitoring for unsafe behavior or changing conditions after deployment.
- Containing, correcting, overriding, or decommissioning a system when needed.
How governance and safety work together
Governance determines who owns safety decisions, what evidence is required, when review occurs, and how concerns are escalated. Safety work produces evaluations and operational evidence that decision-makers can use to approve, restrict, change, or stop a system. Neither is effective on its own: testing without clear ownership can leave findings unaddressed, while governance without evidence cannot make informed safety decisions.
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Illustrative example: An organization sets an accountable owner, defines review criteria for an AI customer-support tool, and specifies how incidents must be escalated. Safety evaluation then tests the tool against those criteria, documents failures or hazards, and reports the results. Governance determines whether the evidence supports launch, requires changes, or calls for further review.
How the NIST and OECD frameworks fit the distinction
NIST AI Risk Management Framework
NIST AI RMF 1.0 was released on January 26, 2023. It is voluntary and organizes risk-management activity around four functions: Govern, Map, Measure, and Manage. Governance is intended to inform and support the other risk activities, rather than sit apart from them. NIST says trustworthiness should be considered during pre-design, design and development, deployment, use, and test and evaluation. NIST AI Risk Management Framework · NIST AI RMF FAQs
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OECD AI Principles
Adopted in 2019 and updated in 2024, the OECD AI Principles set out five values-based principles and five recommendations. They describe ongoing risk management across lifecycle phases, taking account of an actor’s role, context, and ability to act. OECD AI Principles
Does following an AI framework satisfy the law?
No. NIST describes the AI RMF as voluntary; using it does not by itself establish compliance with every applicable law. Legal duties depend on factors such as jurisdiction, sector, the organization’s role, and the system’s use. Governance should identify and document the requirements that apply to the specific system and situation.
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