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Secure an AI system across its full lifecycle—not just by checking the model. Protect the software, infrastructure, data, model-development process, connected services, and operational setting, using controls suited to the system’s purpose and potential impact. Guidance published through August 2025 points to a practical approach: map the system and its risks, build on established software-security practices, test AI-specific attack paths, deploy with safeguards, and keep monitoring and response active.
What does AI security need to protect?
AI systems inherit ordinary cybersecurity concerns: confidentiality, integrity, and availability apply to the system, its data, and the hardware and software it depends on. They also introduce attack surfaces tied to model behavior, training data, inputs and outputs, and connections to tools or services. NIST’s AI security and resilience overview describes the field as active research and cautions that existing frameworks do not comprehensively address several machine-learning attack classes or the full complexity of AI systems.
That means a secure model is not, by itself, a secure system. A model accessed through an application or external API can still be exposed through the application, its data flows, provider relationships, permissions, or operational environment.
How should an organization organize AI security?
Use a lifecycle, not a one-time review
CISA and the UK National Cyber Security Centre’s joint secure-AI-development guidelines, announced on November 26, 2023, apply to AI systems broadly, including systems that use externally hosted models or APIs—not only frontier models. Their lifecycle framing covers design, model development, system development, deployment, and operation. It is a useful way to assign security work before launch and keep it active afterward.
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Secure-by-design also requires organizational ownership. Assign someone accountable for security decisions, involve security expertise early in design, and make clear who can approve risk, remediate findings, and coordinate with providers. CISA and NCSC describe the approach as prioritizing security outcomes, transparency, accountability, and organizational structures that make secure design a priority.
Start with a system and data-flow inventory
Before choosing controls, document the system boundary and how information moves through it. This inventory is a practical implementation of lifecycle and system-boundary thinking, not a verbatim checklist mandated by the cited guidance.
- Record the use case, model and provider, and whether the model is internally developed, hosted by a provider, or accessed through an API.
- Map data inputs and outputs, including sensitive or business-critical information and where it is stored or processed.
- List connected tools, APIs, plugins, and other services, including what each is permitted to do.
- Note the deployment environment, intended users, and the consequences if the system is misused or unavailable.
- Identify the teams responsible for the model, application, infrastructure, data, and incident response.
Use this map to focus security effort. A system handling sensitive information, taking actions through connected tools, or supporting consequential decisions calls for controls and testing proportionate to those risks; there is no single checklist that fits every deployment.
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What secure-development baseline should AI teams use?
Apply conventional software-security practices to the whole system, then add AI-specific threat analysis and evaluation. NIST’s Secure Software Development Framework (SSDF) provides the baseline. Its July 2024 SP 800-218A final community profile augments SSDF version 1.1 with practices, tasks, recommendations, and considerations for generative AI and dual-use foundation-model development across the software lifecycle.
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- Manage software and dependencies: track components, assess changes, and handle vulnerabilities in the application and supporting services as well as in AI-related components.
- Protect confidentiality, integrity, and availability: consider how data or model changes could be exposed, corrupted, or made unavailable.
- Plan vulnerability handling: define how reports will be assessed, assigned, remediated, and communicated, including when a provider or another organization owns part of the system.
Using an external model or API does not remove the need to secure the application, data flows, credentials, integrations, and operating environment under your control. Clarify where provider responsibilities end and yours begin.
Which AI-specific attacks should security testing cover?
Threat-model the actual model, interfaces, data, and degree of autonomy. NIST’s finalized March 2025 report, AI 100-2e2025, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations, provides shared terms for adversarial machine-learning threats. It is a taxonomy, not a guarantee that every attack or mitigation is covered.
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| Threat area | What to examine |
|---|---|
| Adversarial evasion | Whether crafted inputs can cause the model to make an incorrect or unsafe prediction or response. |
| Model extraction | Whether repeated access to outputs could reveal or approximate a protected model. |
| Membership inference | Whether an attacker could infer that particular information was present in training data. |
| Data or model integrity | Whether unauthorized changes to data, model artifacts, or related components could undermine system behavior. |
| Availability attacks | Whether the model or its supporting services can be disrupted or overwhelmed, and what the operational impact would be. |
These are examples, not an exhaustive test plan. Select scenarios based on the system’s interfaces and likely consequences; ordinary application testing alone should not be assumed to cover model behavior.
What controls belong at deployment and during operation?
Translate the threat model into controls at the system boundary. The appropriate choices depend on the deployment and use case, but teams should decide in advance how the system may be accessed, what connected tools can do, what information may flow through it, and how failures or suspicious behavior will be handled.
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- Define which data may be submitted, returned, retained, or passed to connected services, and apply the protections appropriate to its sensitivity.
- Establish operational monitoring for relevant service, security, and model-behavior concerns; route findings to an accountable owner.
- Prepare response procedures for suspected compromise, unsafe or unexpected behavior, data exposure, and service disruption.
- Reassess the system when models, prompts, tools, datasets, dependencies, or threat conditions change.
Monitoring and reassessment matter because a deployed AI system is not static: a change to an integration, model, or data source can alter its attack surface even if the surrounding application appears unchanged.
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What should organizations ask AI providers?
For procurement or a hosted-model arrangement, use questions to establish system boundaries and responsibilities. These are practical questions derived from the lifecycle and security-ownership approach; the cited guidance does not prescribe a universal vendor questionnaire.
- What development and vulnerability-management practices cover the model, service, and supporting components?
- Which parts of the system does the provider operate, and which security responsibilities remain with the customer?
- How are submitted data and outputs handled, and what relevant data flows or connected services should the customer account for?
- What security evaluations have been performed, what attack classes and system boundaries did they cover, and what limitations should customers know?
- How can customers report vulnerabilities or incidents, and how will the provider coordinate response and communicate relevant information?
Record answers and unresolved responsibilities before deployment. A provider’s assurance about its own service does not establish that the customer’s integrations, permissions, or use of the service are secure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which guidance is established, and what was still developing in 2025?
| Publication or initiative | Status and relevance |
|---|---|
| CISA and UK NCSC secure-AI-development guidelines, announced November 26, 2023 | Published lifecycle guidance for secure AI development, applicable to AI systems broadly, including those using external models or APIs. |
| NIST SP 800-218A, July 2024 | Final community profile that augments SSDF version 1.1 for generative AI and dual-use foundation-model development. |
| CISA JCDC AI Cybersecurity Collaboration Playbook and fact sheet, January 14, 2025 | Materials intended to support operational collaboration among government, industry, and international partners. |
| NIST AI 100-2e2025, March 2025 | Final adversarial-machine-learning taxonomy and terminology; it does not claim to cover every attack or mitigation. |
| NIST AI security control-overlay project, announced August 14, 2025 | A concept paper and proposed action plan for SP 800-53 overlays. The project was work in progress at that point, not finalized control guidance. |
The 2023 CISA/NCSC guidelines, 2024 NIST profile, and 2025 NIST and CISA materials have different dates and purposes; they should not be treated as one newly issued standard. The guidance summarized here is centered on U.S. and UK sources and is not a jurisdiction-by-jurisdiction legal or regulatory survey.
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How do organizations coordinate security beyond their own systems?
AI services and dependencies can cross organizational boundaries, so vulnerability handling and incident response may require coordination with providers and other partners. CISA’s Joint Cyber Defense Collaborative (JCDC) AI Cybersecurity Collaboration Playbook and fact sheet, released January 14, 2025, are intended to support operational collaboration among government, industry, and international partners. Organizations can complement internal response plans by establishing contacts and information-sharing procedures appropriate to their role and applicable obligations.
How to turn the practices into a working plan
- Map: inventory the use case, system boundary, model and provider, data flows, integrations, users, and consequences of misuse.
- Assign: name owners for security decisions across development, deployment, providers, and operations.
- Build: apply the SSDF baseline and use SP 800-218A where its generative-AI and dual-use foundation-model focus fits.
- Test: select conventional and AI-specific attack scenarios based on the system’s actual exposure and impact.
- Operate: deploy with defined access, data-handling, monitoring, and response controls.
- Revisit: reassess when the model, data, dependencies, tools, or threat conditions change, and coordinate findings with affected providers or partners.
The core practice is to treat AI security as system security: establish ownership, apply a sound software baseline, test model-specific risks, and maintain controls and coordination throughout operation.
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