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Bureau of AI
Category: Trustworthy AI Principles

Safe

Simply put

In general usage, 'safe' means free from danger or harm, or not causing danger or harm. The evidence provided does not contain any AI governance or model risk management definition of this term; instead it references a television miniseries, a dictionary entry for the common English word, and a software company of the same name.

Formal definition

The evidence packet does not supply a technical or domain-specific definition of 'Safe' relevant to AI governance or model risk management. The only definitional source (Cambridge English Dictionary) defines the ordinary-language adjective as 'free from danger or harm, or not causing danger or harm.' Remaining sources refer to unrelated entities (Harlan Coben's 'Safe' television miniseries and Safe Software). No practitioner-level definition can be responsibly generated from this evidence; a proper entry would require sources addressing AI safety, model safety, or related governance concepts, which are out of scope of the material provided.

Why it matters

The evidence provided for this entry does not contain any AI governance or model risk management source. The only definitional material is a general-English dictionary entry defining 'safe' as 'free from danger or harm, or not causing danger or harm,' alongside references to an unrelated television miniseries and a software company that happen to share the name. As a result, no domain-specific significance can be responsibly asserted here from this evidence.

This matters for readers because 'safe,' 'safety,' and related constructions do carry substantial technical weight in AI governance discussions generally, but that weight cannot be documented from the sources supplied. Presenting an ordinary-language definition as though it were a governance or model risk concept would risk conflating a common adjective with specialized meanings that require dedicated, authoritative sources. Practitioners relying on precise language should treat this entry as a placeholder rather than a substantive term of art.

A usable practitioner entry would need evidence addressing concepts such as AI safety, model safety, or safety-related controls within a governance or risk framework. Because such sources are out of scope of the material provided, this entry does not attempt to characterize any regulatory treatment, standard, or practice, and no such claim should be inferred from it.

Who it's relevant to

Glossary editors and content reviewers
This entry is most relevant to those maintaining the glossary, because it flags a term whose supplied evidence is insufficient for an AI governance or model risk definition. It should be revisited once sources addressing AI safety or model safety are available.
Readers seeking an AI safety definition
Practitioners looking for a governance or risk meaning of 'safe' should be aware that this entry does not provide one. The available evidence covers only general-language and unrelated commercial and entertainment uses of the word.

Inside Safe

Contextual and relative meaning
"Safe" is not a fixed, absolute property of an AI system. It is typically defined relative to a specified use context, threat model, and acceptable-risk threshold. A system judged safe for one purpose or population may not be safe for another.
Harm scope
Assessments of safety generally consider potential harms to individuals, groups, organizations, or society, which may include physical, financial, psychological, reputational, or rights-related harms depending on the system and its deployment.
Risk reduction, not elimination
As commonly framed in governance and risk management practice, safety measures reduce or manage the likelihood and severity of harm; they do not eliminate risk. Residual risk typically remains even after controls are applied.
Relationship to governance and model risk management
Safety objectives are often operationalized through AI governance structures (policies, accountability, oversight) and through model risk management activities (identification, measurement, monitoring, and control of model-related risk). These are distinct but overlapping mechanisms for pursuing safety.
Contested and evolving definition
The meaning of "safe" varies across sectors, jurisdictions, and frameworks and continues to evolve. It carries different connotations in, for example, banking model risk, general enterprise AI, and consumer-facing product contexts.

Common questions

Answers to the questions practitioners most commonly ask about Safe.

Does calling an AI system "safe" mean it has no remaining risk?
No. As commonly used in AI governance and model risk management, "safe" does not denote the absence of risk. Governance controls, testing, and validation are measures that reduce or manage risk; they do not eliminate it. A system described as "safe" typically means residual risk has been assessed and judged acceptable against defined criteria, not that inherent risk has been removed. Treating "safe" as "risk-free" is a frequent error that can lead to under-monitoring after deployment.
Is "safe" a defined regulatory term that applies the same way across all frameworks?
Not in a uniform sense. The word carries different emphases across instruments and jurisdictions, and its meaning is often shaped by the specific framework invoking it rather than by a single authoritative definition. Because the term is used across binding law, guidance, and voluntary standards with differing scope, professionals should identify which instrument and jurisdiction they are relying on rather than assuming a common, portable definition. Where a specific framework's treatment is uncertain, use qualified language instead of asserting a universal meaning.
How can an organization document that a system is "safe" in a way that withstands review?
Rather than asserting the label, document the underlying basis: the risk criteria applied, the assessment of inherent versus residual risk, the controls in place, and the evidence from validation and monitoring that supports the conclusion. Tie the determination to a defined acceptance threshold and the accountable owner who approved it. This makes the claim reviewable and avoids presenting "safe" as a self-evident status.
Who should be accountable for a "safe" determination within governance structures?
Accountability is typically distributed across lines of defense: those who build and operate the system, an independent function that challenges and assesses the determination, and independent assurance that reviews the overall process. The determination itself should rest with an identified accountable owner with appropriate authority. Keeping these roles distinct helps ensure that a "safe" conclusion is independently challenged rather than self-certified by the developing team alone.
How often should a "safe" determination be revisited?
Because a determination reflects conditions at a point in time, it is commonly treated as time-bound and subject to re-assessment. Ongoing monitoring can surface changes in inputs, usage, or model behavior that affect the earlier conclusion. Organizations typically define triggers for review, such as material changes to the system, its data, its operating context, or its performance, so the label does not persist unexamined after deployment.
What should a "safe" determination cover beyond model performance?
Performance is only one dimension. A determination may also need to consider factors such as the intended use and its limits, the operating context, potential harms to affected parties, and the controls that manage identified risks. Because a model can perform well on its metrics while still posing unaddressed risks in deployment, conflating strong performance with "safe" is a common pitfall. The scope of what "safe" must cover is often shaped by the applicable framework and the system's use case.

Common misconceptions

"Safe" is a binary, permanent status a system either has or lacks.
Safety is typically treated as contextual and conditional, tied to a defined use, threat model, and acceptable-risk threshold. A system considered safe under one set of conditions may not be under different conditions, and status can change as the system, data, or environment changes.
Implementing governance and controls makes a system risk-free.
Controls are measures that reduce or manage risk rather than eliminate it. Residual risk generally remains, and describing a system as "safe" does not mean harm is impossible.
There is one authoritative, universal definition of "safe" that applies across all frameworks and jurisdictions.
The term is contested and used differently across sectors and regulatory contexts. Practitioners should scope the definition to the applicable use case and framework rather than assume interchangeability.

Best practices

Define "safe" explicitly for each system by specifying the intended use context, affected populations, threat model, and the acceptable-risk threshold you are measuring against.
Frame safety claims in terms of risk reduction and residual risk rather than as absolute or permanent guarantees.
Identify the categories of potential harm relevant to the deployment (for example physical, financial, psychological, reputational, or rights-related) and assess safety against each.
Distinguish between governance mechanisms (policies, accountability, oversight) and model risk management activities (identification, measurement, monitoring, control) when documenting how safety is pursued, and note where they overlap.
Reassess safety on an ongoing basis, since the determination can change as the system, its data, or its operating environment changes.
State the scope and limitations of any safety conclusion, including the sector or framework context assumed, and avoid implying that a single definition applies universally.