AI risk assessment does not replace traditional cybersecurity. It adds a broader view of risks tied to an AI system’s design, development, use, and evaluation, while conventional security practices protect the system, its data, and the infrastructure it depends on. Organizations should use both, tailoring the work to the system and its setting.
What is the difference between AI risk assessment and cybersecurity?
Traditional cybersecurity focuses on protecting systems and information, especially their confidentiality, integrity, and availability. Those concerns still apply when software includes AI: an AI system can expose data, be altered, become unavailable, or inherit weaknesses from its software, hardware, and operational environment. NIST also identifies training data and AI outputs as part of the security picture (NIST AI Risk Management Framework).
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AI risk assessment adds attention to trustworthiness and other risks that may arise from or be amplified by AI. NIST’s AI Risk Management Framework (AI RMF) is intended to incorporate trustworthiness into AI system design, development, use, and evaluation. Its scope includes considerations beyond cybersecurity and privacy, so it is not simply a new name for a security review (NIST AI RMF overview).
Why should organizations use both approaches?
The two approaches overlap, but neither covers the whole job alone. Security risk assessment addresses the familiar protections an AI system needs: safeguarding information and accounts, maintaining the integrity of data and software, and keeping services resilient. AI risk management asks whether the system is trustworthy for its intended purpose and whether risks across its lifecycle are being identified and managed.
NIST presents its AI RMF as voluntary guidance, not a certification or a guarantee that a system is secure. It also points organizations toward established resources—including the Cybersecurity Framework, Privacy Framework, Risk Management Framework, and Secure Software Development Framework—as complementary ways to address AI security and privacy (NIST AI RMF resources).
Which NIST frameworks and profiles are relevant?
NIST AI RMF 1.0
NIST released AI RMF 1.0 on January 26, 2023, for voluntary use. NIST’s framework page says the 1.0 framework is being revised, so consult the live page for current status rather than assuming that 1.0 is the newest version (NIST AI RMF; AI RMF 1.0 publication).
Generative AI Profile
NIST AI 600-1, published July 26, 2024, is a cross-sectoral companion to AI RMF 1.0. It describes risks that are novel to or heightened by generative AI and suggests actions aligned with the AI RMF functions. It can help organizations think through generative-AI-specific issues without treating every AI system as if it had the same risk profile (NIST AI 600-1).
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NIST’s Cyber AI Profile, NIST IR 8596, has been presented as an initial preliminary draft organized around Cybersecurity Framework 2.0 outcomes. It addresses three related areas: securing AI components, using AI for cyber defense, and thwarting AI-enabled attacks. Because the cited document is a preliminary draft, do not treat it as final guidance; check NIST’s page for any later release (NIST Cyber AI Profile; NIST IR 8596 preliminary draft).
NIST Risk Management Framework
NIST’s Risk Management Framework (RMF) is a risk-based process that integrates security, privacy, and cybersecurity supply-chain considerations into the system development life cycle. NIST says it can be applied to new and legacy systems and to organizations of different sizes and sectors (NIST RMF overview).
How to combine AI risk assessment with security controls
The following sequence is a practical way to apply NIST’s complementary, risk-based guidance. It is an operational synthesis, not a prescribed NIST checklist.
- Inventory the system and dependencies. Record the AI system’s purpose, components, data flows, users, supporting software and hardware, external services, and operational dependencies. Include the environment in which it will be used.
- Assess conventional security risks. Examine access, data protection, software integrity, infrastructure, availability, recovery, and operational resilience. Include training data and outputs alongside the system’s other data and assets.
- Identify AI-related risks for the actual use case. Consider what could go wrong during design, development, deployment, use, evaluation, and subsequent changes. The relevant issues depend on the model, task, users, and consequences of failure; a generative AI profile may be pertinent when generative AI is involved.
- Select suitable framework practices. Use relevant cybersecurity, privacy, secure-development, and AI risk-management practices rather than trying to apply every framework element indiscriminately. NIST says a profile should reflect the user’s setting, goals, risk tolerance, and resources (NIST AI RMF resources and profiles).
- Assign accountability and evidence. Decide who owns the system, who may approve its use and residual risk, and what evidence will show that safeguards and system behavior remain acceptable. The specific roles and evidence depend on the organization; NIST does not prescribe one universal role assignment or measurement threshold.
- Revisit the assessment when conditions change. Review it as the system, its use, dependencies, or operating context changes. NIST’s risk-management approach takes applicable requirements and constraints into account, rather than assuming the same controls suit every organization (NIST RMF).
How to judge whether an assessment is complete enough
A useful assessment should make its scope and decisions visible. Check that it addresses:
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- Scope: Does it cover only cyber threats, or also broader AI trustworthiness and impacts relevant to the use?
- Lifecycle: Does it account for the stages that matter, from development and procurement through deployment, use, evaluation, and changes?
- Assets and failure modes: Are confidentiality, integrity, availability, training data, outputs, and supporting software and hardware considered?
- Governance: Are ownership, approval authority, and acceptance of residual risk clear within the organization?
- Measurement: Is there evidence to review whether controls and system behavior remain acceptable? NIST’s guidance does not establish one universal metric or threshold.
- Fit: Does the approach match the organization’s goals, risk tolerance, available resources, sector, and applicable legal or regulatory requirements?
What AI risk assessment cannot promise
An assessment framework does not make a system secure by itself, guarantee trustworthy outcomes, or remove the need for operational safeguards. NIST’s AI RMF is voluntary guidance, and its recommendations must be selected and applied in context. Nor is there a single control set or score established here that every AI system must meet.
Best Value
The available NIST material also does not establish a universal percentage improvement in security or incident reduction from using AI risk assessment. Organizations should avoid treating the framework as proof of effectiveness; they need evidence appropriate to their systems, risks, and obligations.
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