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AI risk assessment does not replace traditional cybersecurity. It builds on it: conventional security practices protect systems, data, and operations, while AI risk management also examines trustworthiness and risks across an AI system’s design, development, use, and evaluation. Organizations should combine the two, tailoring the work to the system and its setting.
What is the difference?
Traditional cybersecurity focuses on protecting systems and information against threats to confidentiality, integrity, and availability. Those concerns still apply when software uses AI: the model, its data, the surrounding applications, and the hardware and software that support them all need security attention.
AI risk assessment considers those cybersecurity risks alongside risks related to how an AI system is designed, developed, used, and evaluated. Depending on the system and its purpose, that broader view can include trustworthiness concerns not captured by a cyber-threat assessment alone. The two approaches overlap, but neither makes the other unnecessary.
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Shared security concerns
An AI system can be exposed to familiar software and infrastructure risks. NIST specifically notes that cybersecurity concerns can involve confidentiality, integrity, and availability, as well as training data, outputs, and the software and hardware underneath the AI system. Protecting access, information, and operational resilience therefore remains part of managing AI risk.
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Additional AI risk considerations
The AI Risk Management Framework (AI RMF) is designed to incorporate trustworthiness into AI systems’ design, development, use, and evaluation. Its scope includes considerations beyond cybersecurity and privacy. Which additional issues matter depends on the system, its intended use, and the consequences of errors or misuse; a single checklist will not fit every AI deployment.
How NIST guidance fits together
NIST presents its AI RMF as voluntary guidance, not a certification or guarantee of security. It also identifies other frameworks as useful complementary resources when organizations address AI security and privacy.
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| Guidance | What it contributes | Status and date |
|---|---|---|
| NIST AI RMF 1.0 | A voluntary framework for incorporating trustworthiness into AI design, development, use, and evaluation. | Released January 26, 2023; NIST’s framework page says it is being revised. |
| Generative AI Profile, NIST AI 600-1 | A cross-sectoral companion to AI RMF 1.0 that identifies risks novel to or exacerbated by generative AI and suggests actions aligned with the RMF functions. | Published July 26, 2024. |
| NIST Cyber AI Profile, NIST IR 8596 | Applies Cybersecurity Framework 2.0 outcomes to securing AI components, using AI for cyber defense, and thwarting AI-enabled attacks. | The cited document is an initial preliminary draft published in December 2025, not final guidance. |
| NIST Risk Management Framework (RMF) | A risk-based process integrating security, privacy, and cyber supply-chain activities into the system development life cycle. It can apply to new and legacy systems and organizations of different sizes and sectors. | NIST describes it as applicable across system life cycles. |
NIST also points organizations toward the Cybersecurity Framework, Privacy Framework, Risk Management Framework, and Secure Software Development Framework as resources to consider for AI security and privacy. They are complementary options, not a claim that every organization must adopt every framework. Because NIST’s AI RMF status and the Cyber AI Profile can change, consult the current AI RMF page and NIST cybersecurity materials for the latest releases.
How to combine the assessments
A practical assessment can proceed in a sequence. This is a way to apply NIST’s complementary, risk-based guidance, not a prescribed NIST checklist.
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- Inventory the AI system and its dependencies. Record what the system does, where it is used, and the relevant data, models, applications, infrastructure, and suppliers. Include both AI components and the ordinary software and hardware they depend on.
- Assess conventional security risks. Examine data protection, access, software and infrastructure security, and operational resilience. Consider confidentiality, integrity, and availability, including risks to training data and system outputs.
- Identify AI-specific concerns. Look at the system’s intended use and lifecycle, including design, development, deployment, use, evaluation, and relevant changes. Use the Generative AI Profile when generative AI risks are relevant; do not assume that every concern applies to every system.
- Select guidance that fits. Choose relevant framework outcomes and AI RMF practices in light of the organization’s goals, risk tolerance, resources, sector, and applicable requirements. NIST’s RMF is risk-based and accounts for requirements and constraints.
- Assign accountability and evidence. Decide within the organization who owns the system, who can approve its use and residual risk, and what evidence will show that security controls and system behavior remain acceptable. NIST does not prescribe one universal role allocation, metric, or threshold for every organization.
- Revisit the assessment when conditions change. Review it as the system, its use, or its context changes so that the assessment remains relevant to the system in operation.
How to tailor the assessment
Frameworks are starting points, not substitutes for context. NIST advises that profiles reflect the user’s setting, goals, risk tolerance, and resources. In practice, the depth and focus of an assessment should also account for applicable legal or regulatory requirements and the system’s role. A low-impact internal tool and a system used in a consequential process may call for different scrutiny; the framework does not prescribe a universal control set for both.
Likewise, do not treat completion of a framework as proof that an AI system is secure or trustworthy. Use it to organize decisions, controls, ownership, and evidence, then assess whether those measures are suitable for the actual system and risks.
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Common mistakes to avoid
- Replacing cybersecurity with an AI assessment. AI-focused risk management does not remove the need to secure data, access, software, hardware, and operations.
- Applying one identical checklist to every AI system. Relevant risks and controls depend on the system, its use, and the organization’s constraints.
- Assuming a framework guarantees an outcome. NIST’s guidance is voluntary risk management support, not a security guarantee or certification.
- Calling a draft final guidance. The cited Cyber AI Profile is a preliminary draft; check NIST for a later release before relying on it as final.
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