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What “trusted access” means when AI is involved
Trusted access is the set of decisions and controls that let the right person or service reach the right resource under appropriate conditions. Three functions are related but not interchangeable:
| Function | Question it answers | Federal guidance |
|---|---|---|
| Digital identity | Who is the person, and how can a service verify or recognize them? | NIST SP 800-63-4 covers identity proofing, authentication, and federation, with separate assurance levels for each. |
| Authorization | What is this requester permitted to do? | Agencies must make and audit access-permission decisions; successful authentication alone does not grant every permission. |
| Zero-trust architecture | How should access to an enterprise resource be evaluated across the environment? | NIST SP 800-207 provides the architecture; SP 1800-35 demonstrates implementation approaches. |
| AI governance | How should an agency use AI responsibly, including in identity-related processes? | OMB policy sets broad executive-agency priorities, while NIST’s identity guidelines specify conditions for AI/ML used in identity systems. |
The distinction matters. A biometric system may help establish or authenticate identity; a policy engine may decide whether that identity can access a particular record; and zero trust describes how an organization structures and continually evaluates access. None of those functions, on its own, answers all the others.
How should federal agencies verify identity when AI is involved?
The current final federal guideline suite identified here is NIST SP 800-63-4, published July 31, 2025. It applies to federal online services used by the public, business partners, employees, and contractors, but excludes national security systems. It addresses logical access, not the entire physical-access process.
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Set assurance for the service and user group
NIST assigns separate assurance levels to identity proofing (IAL), authentication (AAL), and federation (FAL). Proofing concerns establishing an identity; authentication concerns whether the claimant controls the relevant authenticator; federation concerns how an identity provider’s assertion is conveyed to a relying service. Agencies should choose levels based on the service’s risks and the affected user group, not assume one level fits every service.
For each online service, the risk assessment should weigh both the harm identity controls are intended to prevent and harm the identity system itself could cause. Relevant impacts include mission disruption, reputational damage, unauthorized information access, financial liability, and consequences for health or safety. A false acceptance can enable impersonation or account takeover; a false rejection or inaccessible process can keep a legitimate user from a necessary service.
NIST therefore calls for federal relying parties to apply its Digital Identity Risk Management process to all online services. The process considers privacy exposure, usability barriers, and fraud-control shortcomings alongside unauthorized access. Agencies may tailor controls and consider compensating measures rather than maximizing assurance regardless of context. For public services, NIST says agencies should offer federation as an access option when risk, law, and regulation permit; it is not a universal requirement for every situation.
Require visibility into AI used in identity processes
AI/ML may be used for biometric matching, automated evidence or attribute validation, fraud detection, or user assistance such as chatbots. NIST does not prohibit these uses. For AI/ML used in, or relied on for, identity processes, however, SP 800-63-4 requires organizations to document and communicate that use to relying organizations. They must also provide information about training methods and data, update frequency, and completed testing, and document privacy risk assessments for personal information and data processed. NIST recommends evaluating these systems using its AI Risk Management Framework.
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This gives an agency and its relying partners a basis to ask what the model does, what evidence supports its performance, and what personal data it handles. A vendor’s claim that a system is “AI-powered” is not a substitute for those disclosures, test evidence, or privacy documentation. These specific NIST requirements concern AI/ML in identity systems; they should not be misrepresented as a complete rulebook for every federal AI application.
What does zero trust mean for government access?
Zero trust is an architecture for controlling access to resources, not a slogan that says every user or device is inherently malicious. It avoids treating presence on a trusted network as sufficient proof that a request should be allowed. Access decisions instead take account of the identity, the requested resource, and relevant context, with controls applied across on-premises and cloud environments.
NIST SP 1800-35, published in June 2025, is a practical implementation guide aligned with SP 800-207. It addresses distributed on-premises and multiple-cloud resources for a hybrid workforce and partners. The National Cybersecurity Center of Excellence developed the guide with 24 collaborators under cooperative research agreements; it describes 19 example implementations. Those figures describe the guide’s collaboration and examples, not measured proof that one design is more secure or less costly for every agency.
For agencies, the practical implication is to connect identity decisions to resource-level access controls and the wider security architecture. A strong login does not by itself establish that authorization is appropriate, that a device or session remains safe, or that an application’s access rules are correct.
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How can agencies decide what level of identity assurance a service needs?
A defensible choice begins with the service’s consequences and users, then evaluates the controls as a system rather than selecting a credential in isolation.
- Define the service and users. Identify what information or action the service exposes, who needs it, and the impact if an attacker impersonates a user or a legitimate user cannot complete access.
- Choose IAL, AAL, and FAL separately. Determine the necessary proofing, authentication, and federation assurance for the use case; do not treat one level as a proxy for the others.
- Compare threat resistance and privacy. Examine account-takeover and compromised-federation risks alongside the personal information collected, retained, shared, or processed by identity and AI components.
- Test whether real users can succeed. Account for accessibility needs, device limitations, and process barriers that could exclude legitimate users, as well as threats that fraud controls need to address.
- Check interoperability and agency requirements. Review identity-provider and relying-party dependencies, federation protocols, and applicable PIV requirements. Federal PIV requirements extend the general guidelines for issuing and managing PIV cards and derived credentials.
- Establish accountability and resilience. Make clear who owns access decisions, how decisions are audited, how controls are evaluated over time, and how fraud or threat information informs operations.
- Demand AI-specific assurance where applicable. Obtain the required descriptions of model training, data, update cadence, and testing, plus documented privacy risk assessment; evaluate the AI component rather than accepting a general security claim.
The right balance depends on the service’s impact and operating context. A control that reduces impersonation risk but prevents a substantial share of legitimate users from enrolling may create a different security and mission risk that the agency must address.
What is phishing-resistant authentication, and where does a hardware key fit?
Phishing-resistant authentication is designed to make it harder for an attacker to steal and reuse a user’s authentication secret through a deceptive login page. NIST SP 800-63-4 updates the threat model and adds options for phishing-resistant authentication. A FIDO2-compatible hardware security key is one product category agencies may consider as part of an appropriate implementation.
A key addresses an authentication factor; it does not establish a person’s identity during proofing, decide what the person is authorized to do, or secure every device, application, and federation relationship. Compatibility with a particular service, agency approval, and procurement status must be confirmed for the actual deployment. NIST’s category-level guidance is not an endorsement of a brand or specific model.
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Where the guidance stops: agents, APIs, and excluded systems
Agencies should not assume that SP 800-63-4 resolves every trusted-access problem. The guidelines do not explicitly address machine-to-machine authentication, IoT devices, or API access on behalf of subjects. An AI agent or other service identity therefore needs controls and governance appropriate to that non-human use case; person-focused identity guidance alone is not a complete answer.
Scope also matters. SP 800-63-4 excludes national security systems, and OMB’s current AI memorandum, M-25-21, does not cover AI used as a component of a National Security System. Separate national security direction exists; it should not be conflated with civilian federal online-service requirements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What current federal AI and service-access policy says
OMB Memorandum M-25-21, dated April 3, 2025, rescinded and replaced M-24-10. It directs executive departments and agencies, including independent regulatory agencies, to accelerate AI use while advancing innovation, governance, and public trust and protecting privacy, civil rights, and civil liberties. This is government-wide AI policy context, distinct from NIST’s technical identity requirements.
The White House OMB memorandum index, accessed October 3, 2026, lists M-26-04, “Increasing Public Trust in Artificial Intelligence Through Unbiased AI Principles,” dated December 11, 2025, and M-26-05, “Adopting a Risk-based Approach to Software and Hardware Security,” dated January 23, 2026. It also lists M-26-18 on scaling Login.gov for universal sign-on, dated August 31, 2026. The index confirms those titles and dates; the operational details should be taken from the memoranda themselves.
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A newer public-service direction came in a White House order signed September 29, 2026. It calls for America.gov to become the single entry point for covered online federal services and for Login.gov to be integrated as the authentication service. The order directs protection of personal information through data minimization, secure authentication, auditable authorization, and lawful disclosure practices.
Coverage is defined by scope, not by a claim about total platform reach: the order uses a threshold of more than 100,000 users in a 12-month period for covered public-facing federal services that can be accessed or applied for online. IRS tax filing, Department of War services, and Intelligence Community services are excluded. Agencies are directed to identify and integrate covered services securely and with privacy protections; an OMB implementation memorandum is due within 90 days of the order. This is new direction with implementation still underway, not evidence that every federal service has already migrated.
What agencies should take away
AI makes trusted access a governance responsibility spanning identity, authorization, privacy, usability, and enterprise security. Agencies should apply NIST’s risk process to the service and its users, make AI identity components transparent and assessable, and connect identity decisions to zero-trust resource controls. That approach is more durable than assuming one authentication method, one platform, or one AI product can settle every access question.
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