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AI safety and AI security address different questions. Safety asks whether an AI system behaves acceptably and avoids unacceptable harm in its intended context. Security asks whether the system, its data, software, hardware, and operation are protected from compromise, misuse, disclosure, manipulation, or disruption. They overlap: a security incident can change a model’s behavior, expose sensitive information, or interrupt a consequential service, turning a security event into a safety problem.
Organizations should manage both under a coordinated risk-governance structure. Use the NIST AI Risk Management Framework (AI RMF) to govern, map, measure, and manage trustworthiness risks, while connecting AI-specific security work to established cybersecurity, privacy, supply-chain, and incident-response processes.
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AI safety concerns the acceptability of a system’s behavior and effects for its intended use. The questions are behavioral and impact-focused: Can the system produce harmful or unreliable outputs? Could its decisions create unacceptable consequences for people? Are its limitations understood, documented, and monitored in the context where it operates?
AI security concerns protection of the system and the assets on which it depends. That includes the confidentiality, integrity, and availability of the AI system, its training and output data, application software, model files, interfaces, supporting infrastructure, and hardware. It also includes AI-specific attack surfaces that conventional security programs may not fully address, such as attempts to evade a model’s intended behavior or extract information about the model.
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Safety is therefore broader than cybersecurity, while cybersecurity is an essential part of trustworthy AI. The National Institute of Standards and Technology (NIST) states on its AI security and resilience guidance that “The trustworthiness of AI technologies depends in part on how secure they are.” A secure system is not automatically safe: it may be well protected from unauthorized access and still produce unacceptable outputs. Conversely, a system with sound behavioral testing can become unsafe if an attacker alters its inputs, model, permissions, or operating environment.
How the two risk areas overlap
AI security and safety should have distinct objectives and evidence, but they should not be run as isolated programs. Their intersection is clearest when a security failure changes the conditions under which a system was judged safe.
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If an unauthorized party modifies a model, its configuration, a retrieval source, an input pipeline, or a connected application, the system may no longer behave like the version that passed its safety evaluation. Integrity controls, change approval, version tracking, and re-evaluation are therefore safety safeguards as well as security controls.
Disclosure can create harm
A confidentiality failure can expose personal, proprietary, or otherwise sensitive training and output data. The safety question is not only whether data was stolen, but what harm follows for the people represented in it, the customers relying on the service, or the organization’s ability to operate responsibly.
Disruption can become a safety incident
When an AI service supports a consequential workflow, an outage, denial of service, or loss of a critical dependency can force people into unsafe workarounds or remove an important review step. Availability planning, fail-safe behavior, manual fallback, and escalation procedures should reflect those consequences.
Safety choices shape security controls
The appropriate access restrictions, logging, testing depth, human review, and monitoring depend on the use case, affected people, exposure, and consequence of failure. A low-impact internal assistant and an AI component used in a critical process should not receive identical controls merely because both use machine learning.
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AI security and AI safety compared
| Question | AI safety | AI security |
|---|---|---|
| Primary concern | Whether behavior and effects are acceptable for the intended context | Whether the system and its assets are protected against compromise, misuse, disclosure, manipulation, and disruption |
| Main risks | Harmful or unreliable outputs, unacceptable impacts, undocumented limitations, and inappropriate use | Loss of confidentiality, integrity, or availability; AI-specific attack surfaces such as evasion and model extraction |
| Evidence | Behavioral evaluations, impact assessments, documented limitations, human-oversight records, and incident findings | Threat models, access and configuration records, security testing, supply-chain reviews, monitoring, and incident-response evidence |
| Typical owners | Business and product owners, responsible-AI or risk teams, domain experts, legal and privacy stakeholders, and operations | Security, infrastructure, application, privacy, supply-chain, and operations teams, with AI and business owners |
| Lifecycle emphasis | Design, development, deployment, use, evaluation, and reassessment as context changes | Architecture, development, deployment, operation, updates, incident response, recovery, and retirement |
| Relationship | Requires security and other trustworthiness properties to remain dependable | One component of trustworthy AI, not a replacement for broader safety and risk management |
How should organizations manage AI safety and security?
A practical program connects AI-specific work to existing enterprise risk processes instead of creating a parallel checklist. NIST AI RMF 1.0 provides four functions—Govern, Map, Measure, and Manage—that can organize this work. The framework is voluntary, cross-sector, rights-preserving, and use-case agnostic; it is not a universal legal mandate.
1. Govern: assign accountability and decision rights
Start with a complete inventory of AI systems, including internally built models, purchased services, embedded features, experimental deployments, and systems operated by suppliers. Assign an accountable business owner for each system and identify the people responsible for AI risk, information security, privacy, legal review, infrastructure, model operations, and incident response.
Document who can approve deployment, change a model or data source, grant access, pause service, accept residual risk, and notify affected parties. The record should identify the intended purpose, prohibited uses, operating boundaries, review cadence, and escalation path. Governance should also require evidence: a system cannot be considered approved merely because a team says it was tested.
2. Map: describe the system and its context
For every system, map the full technical and operational context:
- Model versions, training and fine-tuning data, retrieval sources, prompts or policies, applications, APIs, plugins, and connected tools.
- Infrastructure, cloud or on-premises dependencies, hardware, identity systems, secrets, logging destinations, and third-party components.
- Users, affected people, operators, reviewers, downstream recipients, and anyone who may rely on or be harmed by an output.
- Data classifications, geographic or jurisdictional boundaries, retention requirements, and permitted uses.
- Normal operating conditions, foreseeable misuse, failure modes, fallback procedures, and the consequence of an outage or incorrect result.
Mapping is where safety and security questions meet. The same dependency map that reveals where sensitive data flows can show where a compromised component could alter a decision or bypass human review.
3. Measure: test behavior, exposure, and controls
Measure both intended behavior and attack exposure. Safety evaluation should examine whether outputs are reliable and appropriate for the stated use, where the system is limited, and which impacts could be unacceptable. Security evaluation should examine who can access the system and its data, how changes are authorized, whether components are exposed, and how the organization would detect and contain compromise.
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Include AI-specific testing relevant to the design. NIST’s security material highlights evasion and model extraction among AI-related risks. Test whether crafted inputs can defeat the system’s intended behavior, whether model or training information can be extracted, and whether connected interfaces expose capabilities beyond the approved purpose. Record test conditions, data and model versions, assumptions, failed cases, compensating controls, and unresolved uncertainty.
4. Manage: select proportional controls and act on evidence
Use the measurement results to choose mitigations that match exposure and consequence. Controls may include least-privilege access, separation of duties, encryption, protected secrets, approved model and data registries, integrity checks, change control, human review, rate limits, isolation of high-risk tools, resilient infrastructure, and tested rollback or shutdown procedures.
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Prioritize risks that could cause severe or irreversible harm, affect many people, expose sensitive information, or undermine a consequential service. Record residual risk rather than implying that a control eliminates it. Define who can accept that risk and when escalation is mandatory. Reassess after a model update, data change, new integration, permission change, user-population change, or shift in operating context.
What AI risks should a company address?
A useful assessment covers the following categories without treating them as separate silos.
| Risk category | Questions to answer | Examples of evidence |
|---|---|---|
| Behavior and impact | What could an unreliable, inappropriate, or harmful output do in this context? Who could be affected? | Use-case definition, impact assessment, evaluation results, limitations, human-review records |
| Confidentiality | Could unauthorized users, services, or model behavior reveal training, input, output, or operational data? | Data classification, access reviews, retention settings, logging, privacy and security tests |
| Integrity | Could an attacker or faulty dependency alter data, model files, configuration, prompts, outputs, or decisions? | Version records, approvals, integrity checks, change logs, adversarial testing, rollback evidence |
| Availability and resilience | What happens if the model, an API, a data source, or supporting infrastructure is unavailable? | Capacity and dependency analysis, fallback procedures, recovery tests, service monitoring |
| AI-specific attack surfaces | Can inputs evade intended controls? Can an actor extract information about the model or exploit an exposed interface? | Threat model, attack testing, interface restrictions, detection rules, incident playbooks |
| Supply chain | Which providers, models, datasets, software, hardware, and hosted services influence the system? | Supplier assessments, contractual responsibilities, component inventory, update and notification terms |
| Change and drift | What changes could invalidate prior safety or security conclusions? | Reassessment triggers, deployment gates, monitoring, versioned evaluations, approval records |
Controls across the AI lifecycle
Design and development
Define the intended use, unacceptable uses, affected groups, data boundaries, and consequence of failure before implementation. Build the threat model and impact assessment together. Decide which functions require human approval, what the system must refuse or escalate, and which data and tools it may access. Establish secure development, code review, dependency management, secrets protection, and reproducible version records.
Validation before deployment
Require evidence that the deployed configuration matches the evaluated configuration. Test representative use, foreseeable misuse, failure handling, access boundaries, output handling, and recovery procedures. Include security testing for evasion, extraction, exposed interfaces, and unauthorized changes where applicable. Document limitations in language operators and users can act on, not only in technical notes.
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Deployment and operation
Limit permissions to the minimum needed for the approved purpose. Separate development, testing, and production environments. Log model, data, configuration, identity, tool, and administrative events in a way that supports both security investigation and safety review. Monitor output quality, unusual access, data-handling violations, dependency health, and changes in user or operating context.
Incident response and recovery
Define joint criteria for a security incident, a safety incident, and an event that is both. The response plan should identify who can disable a feature, revoke access, isolate a dependency, roll back a model, switch to a manual process, preserve evidence, assess affected people, and communicate externally. Exercise the plan with scenarios in which a compromise changes behavior or availability, not only scenarios involving data theft.
Retirement and replacement
When a model or service is retired, revoke credentials, remove unnecessary interfaces, preserve required records, handle retained data according to policy, and verify that dependent applications no longer route work to it. A replacement system needs its own safety and security assessment; inherited approval should not be assumed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare internal programs, frameworks, or vendors
When comparing two approaches, score both against the same decision criteria. A security-only program may offer strong protection of infrastructure but leave behavioral and societal impacts unexamined. A safety-only process may evaluate outputs while overlooking access, integrity, availability, and supply-chain compromise.
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- Risk scope: Does it cover system compromise and resilience as well as behavioral, human, and societal impacts?
- Lifecycle coverage: Does it address design, development, deployment, use, evaluation, incident response, monitoring, change, and retirement?
- Evidence quality: Does it produce threat models, security tests, behavior and impact evaluations, documented limitations, approvals, and incident records?
- Ownership and integration: Are business, AI, security, privacy, legal, and operational responsibilities explicit and connected to existing enterprise processes?
- Context and proportionality: Can requirements scale with exposure, affected people, consequence of failure, and available mitigations?
- Change management: Does the approach specify when a model, data, integration, permission, user group, or operating context requires reassessment?
Current NIST frameworks and their status
| Resource | Status and scope | How to use it |
|---|---|---|
| NIST AI RMF 1.0 | Released January 26, 2023. Voluntary, cross-sector, rights-preserving, and use-case agnostic. NIST’s current overview says it is being revised as part of the White House AI Action Plan; no final replacement should be assumed. | Use Govern, Map, Measure, and Manage to structure trustworthiness risk work and accountability. |
| NIST Generative AI Profile (NIST AI 600-1) | Published July 26, 2024 as a cross-sector companion to AI RMF 1.0. | Use it to consider risks and actions specific to generative-AI systems alongside the core framework. |
| Trustworthy AI in Critical Infrastructure Profile | NIST announced a concept note on April 7, 2026. It is under development, not a finalized requirement. | Track its status if your organization operates critical infrastructure; do not represent the concept note as a completed standard. |
| NIST Cybersecurity Risk Management Framework | A general seven-step information-security and privacy risk process. | Use it as a complementary enterprise foundation, extending existing controls and processes to AI-specific components and risks. |
NIST also provides a Playbook, Roadmap, Crosswalks, and an AI Resource Center to support implementation. These aids do not turn the voluntary AI RMF into a legal obligation. Framework applicability alone does not answer compliance duties in a particular jurisdiction.
Evidence and metrics that make the program auditable
Do not measure success only by the number of policies written or tests completed. Maintain an evidence trail that lets an independent reviewer reconstruct the decision:
- Current inventory, owner, intended use, affected people, dependencies, data classes, and approved operating boundaries.
- Threat model and impact assessment, including assumptions and known limitations.
- Model, data, software, configuration, and permission versions tied to each evaluation.
- Results of behavioral, impact, security, privacy, and resilience testing, including failed cases and exceptions.
- Access reviews, change approvals, supplier assessments, monitoring alerts, incidents, corrective actions, and residual-risk acceptance.
- Dates and triggers for reassessment, with evidence that updates and context changes were reviewed.
Useful indicators are therefore evidence-based: whether every production system has an accountable owner; whether evaluated and deployed versions match; whether high-impact findings have an accepted disposition; whether critical dependencies have tested fallbacks; and whether incidents lead to documented control or evaluation changes. These are management signals, not universal benchmarks, and should be tailored to the organization’s risk profile.
Common mistakes and how to correct them
Treating security as the whole of AI safety
Strong access control and encryption do not show that outputs are acceptable or impacts are understood. Add behavioral evaluation, impact analysis, human-oversight design, and limitation records.
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A model can pass a behavior test and still be exposed through an interface, dependency, credential, or data pipeline. Include threat modeling, integrity, confidentiality, availability, and incident-response owners from the beginning.
Approving a model instead of a system
Safety and security depend on the complete deployment: model version, data, prompts or policies, tools, permissions, infrastructure, users, and operating context. Approve the configuration and use case, not an abstract model name.
Assuming a framework guarantees compliance
NIST AI RMF 1.0 is voluntary. Use it to organize risk management, then map obligations and controls to the laws, contracts, standards, and sector requirements that apply to your organization.
Failing to reassess after change
New data, integrations, permissions, users, model versions, or operating environments can alter both safety and security risk. Define reassessment triggers before deployment and block unreviewed changes from reaching production.
A practical operating model
The most durable arrangement is one coordinated program with separate accountabilities. A business or product owner remains responsible for the purpose and consequences of use. AI or model teams own technical behavior and limitations. Security owns protection against compromise and abuse. Privacy, legal, risk, domain, and operations teams provide specialist review. A cross-functional governance group resolves conflicts, approves residual risk, and decides when a system must be restricted, paused, or retired.
That structure prevents two opposite failures: security controls being applied without understanding the human consequences, and safety claims being made without protecting the system that produces the behavior. It also lets organizations reuse established cybersecurity and privacy processes while adding AI-specific evaluation, attack testing, monitoring, and change triggers.
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