AI can make biometric identity systems better at matching faces, checking evidence, and spotting fraud—but it can also help create the forged media those systems must detect. A biometric match alone does not prove that an image came from a live person or that the media reached the system without alteration. Security depends on the whole identity workflow, from capture and transmission to review, recovery, and privacy protections.
Where biometrics fit: identity proofing is not authentication
Biometrics use physical or behavioral characteristics—such as a face or fingerprint—to compare a person with a reference or to help unlock an authenticator. Those uses can look similar to a user, but they answer different security questions.
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| Process | What it establishes | Example |
|---|---|---|
| Identity proofing | Whether a person is who they claim to be, based on evidence and checks made during enrollment. | A remote service compares a person’s face with a photo on an identity document. |
| Authentication | Whether the person trying to access an account can demonstrate control of an enrolled authenticator. | A device uses a fingerprint to let its owner access a stored credential. |
A face comparison in a proofing process is not the same thing as using a fingerprint to unlock a credential. The first helps assess identity evidence; the second may be a convenient local way to use an authenticator. Neither label, by itself, tells you how well the complete system resists attacks.
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How AI and machine learning change biometric security
NIST’s Digital Identity Guidelines, SP 800-63-4, published in July 2025, describe AI and machine learning as tools identity systems may use to improve matching performance, validate evidence or attributes, detect fraud, and assist users. These capabilities can support faster or more consistent decisions, but they do not make the system trustworthy automatically. A model can make mistakes, behave differently as it is updated, or process personal information in ways that create privacy risks.
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The guidelines address identity proofing, authentication, and federation for people interacting with government information systems over networks. They are not a universal legal rule for every company or use of biometrics. Organizations outside that scope can still use the guidance to frame system design and evaluation, while determining which laws and requirements apply to them separately.
SP 800-63-4 calls for organizations using AI/ML systems—or relying on services that use them—to document privacy-risk assessments for personal information and data processed by those systems. It also addresses communicating AI/ML use and providing relevant information about training, datasets, updates, and testing to relying organizations. For a biometric service, that means the assessment should consider not only the comparison itself, but also the data the service collects, infers, retains, and shares.
How remote biometric checks can be attacked
Remote identity checks must assess both the person and the media presented to the system. AI-generated or modified imagery can target document validation, biometric operations, or a human reviewer. NIST SP 800-63A-4 cautions: “A biometric comparison performed with a captured sample does not prevent these attacks.” In other words, a system can correctly compare the face it receives and still be acting on a fake or substituted image.
Presentation attacks
A presentation attack occurs when someone presents a fake biometric sample at a sensor—for example, an image rather than the live face the system expects. Liveness detection is intended to help distinguish a live presentation from a spoof, but it is a control to test, not a guarantee that every attack will be caught.
Forged media and face morphing
Deepfakes and other manipulated images or videos may be used to fool automated checks or a person reviewing a remote session. A related mechanism is face morphing: combining facial images into one image that may represent multiple people. NIST researcher Mei Lee Ngan has described the possibility that more than one person could use a passport containing a morphed photo. This illustrates a risk; it does not establish how common the attack is.
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Digital injection
In a digital-injection attack, an attacker substitutes or alters media after capture but before it reaches the component that analyzes or reviews it. A strong matching algorithm cannot establish that the input traveled from a genuine capture device without alteration. The integrity of the capture device and the channel carrying its data therefore matter alongside matching accuracy.
Privacy, reuse, and impersonation
Biometric information can be sensitive because it is tied to a person and may be difficult to replace if compromised. AI analysis can also create privacy concerns through re-identification or additional inferences about individuals. The Federal Trade Commission’s policy statement on biometric information identifies deepfake-enabled impersonation among the concerns associated with biometric data. Collection, retention, sharing, and permitted reuse are therefore security decisions as well as privacy decisions.
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What defenses need to cover
Effective controls address the capture path, the analysis, and what happens when an automated result is uncertain. NIST SP 800-63A-4 calls for analyzing submitted media for signs of manipulation, testing detection against attack artifacts as well as genuine media, and documenting the artifact types tested and false-negative rates. Those details help an organization understand what its evaluations do—and do not—demonstrate.
- Measure biometric performance: Evaluate false acceptance and false rejection performance under relevant conditions. NIST’s covered biometric performance testing is to conform to ISO/IEC 19795-1:2021 and ISO/IEC 19795-10:2024, including demographic testing.
- Test for manipulated inputs: Record which attack artifacts were included in evaluation and the system’s false-negative rates. Testing only on genuine media cannot show how the system responds to forged media.
- Protect the capture path: Use authenticated channels and consider passive manipulation detection, authenticated sensors, or device attestation. These controls can help establish the origin or integrity of media, but their presence should not be treated as proof that all injection attacks are prevented.
- Use human review where appropriate: In attended remote collection, trained reviewers can help address automated detection errors. NIST also describes randomized cues, such as asking a person to move or to move an object between the sensor and their face. These steps are useful only when they fit the collection scenario and are implemented and evaluated appropriately.
- Manage data and model risk: Document privacy assessments, handling and retention choices, and the AI/ML information needed by relying organizations. Disclose the system’s AI/ML use as called for by the applicable guidance.
Biometrics, passwords, and phishing-resistant authenticators
Whether biometrics are “safer than a password” depends on what role they play. A biometric can make it convenient to access a device or credential, but the biometric comparison is not automatically the security property that makes the underlying authenticator resistant to phishing.
In its April 23, 2024 supplement announcement, NIST said correctly implemented syncable authenticators provide a phishing-resistant authenticator, with benefits that can include simplified recovery, cross-device support, and consumer-friendly platform features such as native biometrics. The key distinction is that phishing resistance belongs to the correctly implemented authenticator; a biometric may be the way a person uses it. Do not infer that every face or fingerprint login is phishing-resistant.
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Organizations should assess the authenticator and its enrollment, recovery, and fallback paths—not just the biometric prompt. A convenient biometric unlock does not remove the need to secure account recovery or provide a safe alternative for people who cannot or do not use that biometric.
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Before adopting facial recognition or another biometric identity service, ask for evidence and decisions in these areas:
- Performance: What are the measured false acceptance and false rejection results, under what conditions were they obtained, and do evaluations include demographic testing against the applicable standards?
- Attack coverage: Which presentation attacks, forged-media artifacts, and digital-injection scenarios were tested? What false-negative rates were recorded, and how representative were the tested genuine samples?
- Capture integrity: How does the service authenticate its sensor or device, protect media in transit, and detect or limit substitution between capture and analysis?
- Escalation: What happens when the automated result is uncertain or conflicts with other evidence? Is there a trained human-review path, and how can a user recover access or use a fallback?
- Privacy and reuse: What biometric and derived data are collected, where and how long are they retained, who receives them, and what uses are permitted? Is the privacy-risk assessment documented?
- AI/ML transparency: What information can the provider supply about model training data, datasets, updates, and testing, and how will changes be communicated to relying organizations?
These questions are evaluation dimensions supported by NIST guidance, not a vendor ranking. The cited standards do not establish results for any particular product or provider.
Why the whole workflow is the security boundary
Biometric matching is valuable only as one part of a trustworthy identity process. AI/ML may improve matching or help uncover fraud, while generative AI can make convincing material for attacks. Capture integrity, attack testing, human escalation, privacy practices, and recovery all affect whether a biometric decision is dependable. NIST SP 800-63-4 supersedes SP 800-63-3; its publication page also records an implementation-resource-hub planning note dated October 6, 2026.
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