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AI safety is not only a question of what a model should say. When an AI application can read files, retrieve private data, call APIs, or take actions, safety also depends on who and what can access those resources, under which conditions, and with what oversight. Zero trust offers a useful security lens for those decisions—but it is not a complete AI-safety framework.
What zero trust means for AI
Zero trust is an approach to access control, not a claim that every AI output is untrustworthy or that a model can be made safe by adding a security product. CISA’s Zero Trust Maturity Model Version 2 (April 2023) draws on NIST SP 800-207: minimize uncertainty by making accurate, least-privilege access decisions for each request, and assume the network may already be compromised.
That shifts security away from treating a device or service as trusted simply because it is inside a corporate network or passed an earlier check. Identity, context, and the sensitivity of the resource matter. Applied to AI, the same idea means evaluating access for the people, models, tools, services, and data stores involved—not granting an AI system broad, lasting authority because it has been approved once.
Why AI applications need more than a perimeter
AI applications inherit conventional cybersecurity risks: attackers may target software, hardware, accounts, and the confidentiality, integrity, or availability of systems. They also introduce risks tied to machine-learning behavior and the way people connect models to information and actions. NIST identifies AI/ML-specific concerns such as evasion, model extraction, and membership inference alongside conventional security concerns in its AI security and resilience overview.
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OWASP’s 2025 Top 10 for LLM and GenAI, as reproduced in a NIST-hosted presentation, includes these risk categories: prompt injection; sensitive information disclosure; supply-chain weaknesses; data and model poisoning; improper output handling; excessive agency; system-prompt leakage; vector and embedding weaknesses; misinformation; and unbounded consumption. These categories show why an authenticated user or a protected network is not enough: untrusted content can influence a model, a model can expose data, and an output can become dangerous when another system treats it as an instruction.
How to apply zero-trust principles to AI
The following controls are practical applications of zero-trust access principles and AI risk management. They are not a claim that CISA prescribes a particular AI architecture.
Scope access to tools and data
- Give each model, agent, service account, and human user access only to the tools and data needed for its assigned task.
- Use narrowly scoped permissions rather than a single broad credential that can reach many systems. Where feasible, make access short-lived and tied to the requested action.
- Separate read access from write, export, and administrative privileges. Treat especially sensitive data and consequential actions as requiring stronger controls.
Verify requests and put consequential actions behind approval
Check the identity and context of the user or workload requesting access, and evaluate each request against the resource and action involved. For actions with significant consequences—such as sending money, changing permissions, or deleting records—require an appropriate human approval step rather than letting a model execute solely on the basis of its own generated output.
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A model response should not automatically become a database query, shell command, access decision, or message to an external recipient. Validate and constrain outputs before passing them to downstream systems; use structured interfaces and explicit checks appropriate to the action. This reduces the chance that malicious instructions or an incorrect response will be treated as trusted operational input.
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Log activity and evaluate the system over time
Maintain useful records of access requests, tool calls, approvals, and outcomes so teams can investigate unexpected behavior. Monitor for changes in use and test the system across design, development, deployment, and operation. A single review at launch cannot establish that a changing model, data source, integration, or threat environment remains safe.
Zero trust is not the whole AI-safety program
CISA’s zero-trust model is enterprise cybersecurity guidance. NIST’s AI Risk Management Framework is a voluntary framework for managing AI risks and incorporating trustworthiness through design, development, use, and evaluation. They address related but different problems.
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The AI RMF covers trustworthiness characteristics beyond access security, including safety, security and resilience, accountability and transparency, explainability, privacy, and fairness. NIST released AI RMF 1.0 on January 26, 2023, and its framework overview says the framework is being revised. NIST released the Generative AI Profile on July 26, 2024 to address risks specific to generative AI.
Use zero trust to make access and action boundaries harder to misuse. Pair it with AI-specific risk assessment and evaluation; access control alone does not resolve unsafe behavior, unfair outcomes, or failures of transparency and accountability.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge a zero-trust approach for an AI system
A maturity label or security product alone does not show whether an AI deployment is well protected. Assess the design across the full system:
- Access recipients: Which users, models, agents, devices, and workloads can reach each resource?
- Resource sensitivity: What data or action is exposed, and what is the impact of misuse?
- Privilege scope and duration: Are permissions limited to the task and removed when no longer needed?
- Verification: What identity and contextual checks occur, and are they repeated when circumstances change?
- Visibility and response: Can the organization see access and tool activity, investigate anomalies, and revoke access or halt actions?
- Coverage: Do controls extend across identities, devices, applications and workloads, and data?
CISA describes zero-trust maturity as progress from traditional, often manual practices toward automated, dynamic, continuously monitored controls. That progression depends on coordinated coverage, not on purchasing one product. CISA and partner agencies’ June 18, 2024 guidance on modern approaches to network access security also points organizations toward approaches such as zero trust, Secure Service Edge, and Secure Access Service Edge to improve visibility and address risks from traditional remote access and misconfiguration.
Where phishing-resistant MFA fits
A FIDO2-compatible hardware security key can support phishing-resistant multifactor authentication for a person’s account. That is useful for protecting administrator and other sensitive accounts, but it addresses authentication—not prompt injection, poisoned data, unsafe output handling, or excessive model permissions. Treat it as one identity control within a broader system of safeguards, not as an AI-safety solution.
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