The U.S. Department of Homeland Security (DHS) framework asks the organizations that build, host, deploy, and oversee AI to share responsibility for managing its risks in critical infrastructure. It is a voluntary set of recommendations—not a regulation—and it groups its guidance around five areas: secure environments, responsible design, data governance, safe deployment, and ongoing monitoring.
How does the DHS framework keep AI safe in U.S. critical infrastructure? It lays out responsibilities for cloud and compute providers, AI developers, infrastructure operators, civil society, and government, while urging those groups to exchange information across the AI lifecycle. The framework was issued on November 14, 2024; available DHS sources confirm its publication listing was updated in 2025, but do not establish its status or adoption after that.
What the DHS framework is—and whether it is mandatory
DHS published the Roles and Responsibilities Framework for Artificial Intelligence in Critical Infrastructure on November 14, 2024. DHS described it at release as voluntary guidance, not a binding compliance requirement. Its recommendations were intended to help organizations manage AI risks in essential services and systems.
That legal-status description is specific to the framework’s November 2024 launch. DHS’s critical infrastructure index lists the publication and is marked last updated September 30, 2025; that page date does not show that the framework itself was revised then. The available sources do not establish whether it has been revised, superseded, or widely adopted since.
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Why DHS focuses on the whole AI ecosystem
AI used in infrastructure can involve several organizations: one may supply computing resources, another build a model, and a third incorporate it into a service. Because infrastructure systems are interconnected, a weakness in one part can affect services beyond the model or organization where it began. DHS cited mail distribution, earthquake detection and aftershock prediction, and electric-service reliability as examples of critical infrastructure uses; these are examples in the DHS release, not independent findings about any specific system’s performance.
The framework groups the risks into three broad classes:
- Attacks using AI: AI may be used to support attacks against people, organizations, or infrastructure.
- Attacks targeting AI systems: Models, data, or the systems around them may themselves be attacked.
- Design and implementation failures: Weaknesses in how AI is developed, integrated, or operated may create vulnerabilities or unsafe outcomes.
Rather than treating the model developer as the only party responsible, DHS distributes recommendations across the organizations that shape and use AI. Its official framework document organizes those recommendations into five areas.
The five areas of recommended action
1. Secure the environments where AI is built and run
Cloud and compute providers are asked to protect the environments used to develop and deploy AI. Recommendations include vetting hardware and software suppliers, controlling access, securing data centers physically, watching for anomalous activity, and establishing channels for reporting suspicious or harmful activity.
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AI developers are encouraged to use secure-by-design practices, evaluate potentially dangerous capabilities, align systems with human-centric values, and test for bias, failure modes, and vulnerabilities. The framework also recommends strong privacy practices and independent assessment for models that could pose heightened risks to infrastructure.
3. Govern data responsibly
Data governance is shared across the lifecycle. Developers are encouraged to protect privacy, while infrastructure owners and operators should protect customer data when fine-tuning products. The framework’s broader emphasis is on making relevant information available to the parties that need it to assess safety and security—not treating data practices as solely a developer concern.
4. Deploy AI safely and securely
Critical infrastructure owners and operators are encouraged to account for AI risks in their cybersecurity work, be transparent about AI use in services or benefits, and consider deployment context when deciding how a system should be used. Those decisions matter because a model’s risks can change when it is connected to operational systems or used in a particular service.
5. Monitor performance and impact
Operators are encouraged to monitor how AI systems perform in use and share relevant results with developers and researchers. Ongoing monitoring can surface problems that did not appear in design or pre-deployment testing, while feedback can help the people building or evaluating a system understand its real-world context.
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The framework identifies five groups with distinct but overlapping roles. An organization may fit more than one category—for example, a company could provide cloud infrastructure and also operate an AI-enabled service.
| Actor | Recommendations in the framework |
|---|---|
| Cloud and compute infrastructure providers | Secure development and deployment environments; vet suppliers; manage access and physical data-center security; monitor anomalous activity; and create channels to report suspicious or harmful activity. |
| AI developers | Build securely, assess potentially dangerous capabilities, use privacy practices, test for bias, vulnerabilities, and failure modes, and support independent assessment for models with heightened infrastructure risk. |
| Critical infrastructure owners and operators | Include AI in cybersecurity risk management; protect customer data used for fine-tuning; be transparent about AI use in services or benefits; monitor performance; and share results with developers and researchers. |
| Civil society | Contribute research and evaluation relevant to infrastructure use cases, participate in standards development, and inform values and safeguards. |
| Public-sector entities | Support responsible AI use in public services, advance safety and security through appropriate statutory or regulatory action, cooperate internationally, and support foundational research. |
Information-sharing is part of the safety model
DHS’s approach depends on communication between organizations, not just a checklist of isolated controls. Infrastructure operators need information about risks considered during model design and testing. Developers and service providers need details about infrastructure components and suppliers. Operators, in turn, can give developers deployment context and report observed outcomes.
That exchange is important because no single actor necessarily sees the full chain—from computing environment and training data through integration and operation. In practice, an organization applying the framework would need to decide who owns each recommendation, what evidence demonstrates it is being followed, and how findings or incidents move to other relevant parties. The framework sets out broad responsibilities; it does not by itself supply a complete operational roadmap for every organization or use case.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What analysts said about its practical value
Launch-era commentary was broadly supportive of greater attention to AI security, but analysts differed on whether voluntary, high-level guidance would be enough to drive implementation. These were expert opinions reported at launch, not measurements of adoption or safety outcomes.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11- Naveen Chhabra, Forrester principal analyst, called the framework “a living document,” anticipating substantial advances in AI.
- Peter Rutten of IDC argued that guidance for securing AI development and deployment was critical, citing security and data-use concerns.
- Bill Wong, Info-Tech Research Group research fellow, raised the prospect that organizations might not adopt voluntary government recommendations when priorities differ or funding, expertise, and resources are insufficient. He also questioned whether the framework offered enough practical help to organizations still forming AI strategies.
- David Brauchler, technical director at NCC, described frameworks as “a starting point” that provide broad guidance rather than detailed roadmaps, and highlighted privacy and human oversight.
Those comments identify a practical distinction: agreeing with the framework’s aims is not the same as having the people, budget, technical detail, or internal processes to implement its recommendations.
What the framework does not establish
- It does not, by itself, create a binding legal obligation; DHS described it as voluntary at its November 2024 release.
- Its publication does not demonstrate that organizations have adopted the recommendations or that incidents have been reduced.
- The available sources do not establish its post-2025 status, any later revision, or whether another federal framework has superseded it.
- It identifies responsibility areas but does not provide a one-size-fits-all implementation plan for every infrastructure operator or AI application.
For organizations using AI in critical infrastructure, the framework is best read as a map of shared responsibilities and risk areas. Turning it into practice requires assigning owners, selecting controls suited to the deployment, documenting evidence, and setting up communication among providers, developers, operators, and relevant public-interest bodies.
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